Contact Map Transfer with Conditional Diffusion Model for Generalizable Dexterous Grasp Generation
Authors: Yiyao Ma
Kai Chen
Kexin ZHENG
Qi Dou
Venue: NeurIPS 2025 poster ()
Keywords: Dexterous grasp generation
Contact map transfer
Primary area: applications

Abstract: Dexterous grasp generation is a fundamental challenge in robotics, requiring both grasp stability and adaptability across diverse objects and tasks. Analytical methods ensure stable grasps but are inefficient and lack task adaptability, while generative approaches improve efficiency and task integration but generalize poorly to unseen objects and tasks due to data limitations. In this paper, we propose a transfer-based framework for dexterous grasp generation, leveraging a conditional diffusion model to transfer high-quality grasps from shape templates to novel objects within the same category. Specifically, we reformulate the grasp transfer problem as the generation of an object contact map, incorporating object shape similarity and task specifications into the diffusion process. To handle complex shape variations, we introduce a dual mapping mechanism, capturing intricate geometric relationship between shape templates and novel objects. Beyond the contact map, we derive two additional object-centric maps, the part map and direction map, to encode finer contact details for more stable grasps. We then develop a cascaded conditional diffusion model framework to jointly transfer these three maps, ensuring their intra-consistency. Finally, we introduce a robust grasp recovery mechanism, identifying reliable contact points and optimizing grasp configurations efficiently. Extensive experiments demonstrate the superiority of our proposed method. Our approach effectively balances grasp quality, generation efficiency, and generalization performance across various tasks. Project homepage: https://cmtdiffusion.github.io/

Contact Map Transfer with Conditional Diffusion Model for Generalizable Dexterous Grasp Generation
Yiyao Ma,
Kai Chen',
Kexin Zheng,
Dou
Department of Computer Science and Engineering, The Chinese University of Hong Kong
Abstract
Dexterous grasp generation is a fundamental challenge in robotics, requiring both grasp stability and adaptability across diverse objects and tasks. Analytical methods ensure stable grasps but are inefficient and lack task adaptability, while generative approaches improve efficiency and task integration but generalize poorly to unseen objects and tasks due to data limitations. In this paper, we propose a transfer-based framework for dexterous grasp generation, leveraging a conditional diffusion model to transfer high-quality grasps from shape templates to novel objects within the same category. Specifically, we reformulate the grasp transfer problem as the generation of an object contact map. incorporating object shape similarity and task specifications into the diffusion process. To handle complex shape variations, we introduce dual mapping mechanism, capturing intricate geometric relationship between shape templates and novel objects. Beyond the contact map, we derive two additional object-centric maps, the part map and direction map, encode finer contact details for more stable grasps. We then develop cascaded conditional diffusion model framework to jointly transfer these three maps, ensuring their intra-consistency. Finally, we introduce robust grasp recovery mechanism, identifying reliable contact points and optimizing grasp configurations efficiently. Extensive experiments demonstrate the superiority of our proposed method. Our approach effectively balances grasp quality, generation efficiency, and generalization performance across various tasks. Project homepage: https:/ // inndiiffuiin gi thub. io/
Introduction
Dexterous grasp generation is crucial task in robotics, enabling robotic hands to manipulate objects with human-like precision and adaptability [1, 3]. It plays fundamental role humanoid robotics, particularly in its application in unstructured environments{4. 5] and human-robot interactions [6. 7, 8, 9]. However, the high degrees of freedom of dexterous robotic hands increase the difficulty of achieving stable grasps [1], while complex contact dynamics between robot fingers and objects in diverse shapes hinder the generalization of existing methods [10, 11]. How to generate stable and generalizable grasps across various objects remains challenging problem that has gained extensive attention [12, 13, 14, 15] in recent years but has not yet been fully resolved.
Existing dexterous grasp generation methods can be broadly categorized into analytical [16, 17, 18, 19, 20, 21] and generative [22, 23, 24, 25, 26, 27] approaches. Analytical methods optimize grasps by defining objective functions based on hand-object constraints such as force closure [16, 17, 20]. penetration [21], and hand coverage [18, 19), ensuring high-quality and stable grasp synthesis.
Corresponding author
(yyma23, kaichen) cuhk. edu. hk
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Analytical Methods
Generative Methods
Proposed Transfer-based Method
Phoskal
Traning
dtisk wore" Word leak Nove dipect
"Oresp the Aasull pass
Contact Yrusstee
Oenorative Modill
Boor callagay sraaen skpedd
Unaan oeagentos
Forte cosure grosp with high stability and bask adapasblity
Eliiwency for lesketrantted gropping
effilency task adapiabiity
v/ High quality grasp
High generiiization ubjecta and lasks
Linilad quality O0C objects and
Figure I: Comparison of our proposed framework with existing analytical and generative methods. The proposed and generalization capability for dexterous grasp generation.
framework can effectively balance efficiency, quality, task adaptability,
However, these methods are computationally expensive due to the complexity of the optimization process [28]. Moreover, generating task-oriented grasps requires manually designing task-specific constraints (e.g-, object part constraints [29] or wrench constraints [30]), making them difficult to generalize across diverse real-world robotic tasks. In contrast, generative methods learn grasp distributions conditioned on object features, enabling efficient and diverse dexterous grasp synthesis. By integrating task embeddings [31, 32, 33]. they can further produce grasps aligned with specific task requirements. However, their performance heavily depends on the quality of training data, including the diversity of objects, accuracy of grasp annotations, and completeness of task descriptions, As a result, generative models still struggle to generalize to out-of-distribution (OOD) objects and novel tasks, where the learned grasp distribution may fail to transfer effectively. As illustrated in Fig. 1, while analytical methods ensure grasp stability and generative methods enhance efficiency and task adaptability, no existing approach effectively integrates their strengths to achieve both stability and generalization in dexterous grasp generation.
In this regard, we innovate a transfer-based framework for dexterous grasp generation. The core idea is use generative models to efficiently transfer high-quality grasps sampled by analytical methods on a shape template to novel objects within the same category. Unlike conventional analytical methods, our approach can improve efficiency and adaptability by using generative models for grasp transfer. Compared to existing generative approaches, our method models the conditional grasp distribution based on task embeddings and geometric similarities across object categories, rather than directly learning an implicit distribution tied to object features [31, 33]. This enables the generation of high-quality, task-oriented grasps while enhancing generalization to new object instances, unseen tasks, and even novel object categories. Nevertheless, implementing this framework presents several challenges. The substantial shape variations across objects, the complex contact interactions between robot hands and diverse object geometries, and the need to accommodate varying task specifications introduce fundamental difficulties that must be addressed.
In this paper, we present novel conditional diffusion model for dexterous grasp transfer. Specifically, we follow [34, 22, 35, 36] and leverage robust object-centric representations to reformulate the grasp transfer problem as the generation of an object contact map. We then propose framework that integrates object geometry similarity along with the task embedding into the diffusion model for a conditional generation of the contact map. To handle the complex shape variations across diverse objects, we introduce dual mapping mechanism within conditional diffusion. This mechanism explicitly models the dual mapping relationships between the template shape and the reconstructed template contact map, and between the noise and the contact map of the novel object. this way, the diffusion model effectively captures the intricate geometric similarities between the shape template and the novel object under different task specifications, enabling the generation of an accurate contact map aligned with the intended grasping task.
The contact map alone indicates whether the dexterous robotic hand is in contact with the object but fails to capture the finer details of the contact interaction. To achieve more stable and dexterous grasping, we follow [35] to further derive two additional object-centric maps, the part map and the direction map. which provide richer information about the contact regions and the grasping orientations. To jointly transfer three object-centric maps from the shape template to the novel object, we develop cascaded conditional diffusion framework. This cascaded design enables progressive generation process, ensuring intra-consistency among the three maps and preserving coherent contact, part, and direction information throughout the transfer process. Based on the transferred three object-
2

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centric maps, we design robust mechanism to automatically identify object points with reliable part and direction predictions. Finally, we recover the grasp configuration parameters through a fast and robust optimization scheme, ensuring the stability and feasibility of the generated dexterous grasps for novel objects. We summarize our main contributions as follows:
We formulate dexterous grasp generation as an object contact map transfer problem and propose a novel conditional diffusion model that jointly captures object geometric similarity and textual task embeddings, enabling more generalizable dexterous grasp generation.
We exploit object-centric part map and direction map to enrich hand-object contact represen- tation and develop a cascaded conditional diffusion framework to jointly transfer the object contact map, part map, and direction map with high consistency.
We propose a robust optimization method that adaptively identifies object points with reliable part and direction predictions and comprehensively leverages contact information from the contact, part, and direction maps to robustly recover dexterous grasp parameters.
Extensive experiments show that our method transfer dexterous grasps from shape templates to novel objects with high quality. It generalizes well across diverse objects, tasks, and unseen categories while generating grasps that well align with task specifications.
2 Related Work
Analytical Methods for Dexterous Grasp Generation. This kind of methods typically formulates
dexterous grasp generation as optimization problem. leveraging analytical models of contact mechanics [18, 19], force closure [37, 16, 17], and grasp stability [21, 38] to compute optimal hand configurations for grasping given object. While previous methods [39, 40, 41, 37] rely on computationally expensive approaches to compute force closure between the robotic hand and the object for grasp optimization, recent methods [16, 17, 20] have introduced differentiable force-closure estimators to speed up this computation. In addition, to apply these analytical methods to task-oriented grasping, recent approaches define task-specific object parts [29] for grasping or constrain the wrench relationship [30] between the robotic hand and the object based on the task type, thereby generating dexterous grasps that align with task requirements. However, these heuristic methods still struggle to generalize across diverse objects and tasks. In this paper, we will explore a more efficient and generalizable transfer-based framework for dexterous grasp generation.
Generative Methods for Dexterous Grasp Generation. Generative methods for dexterous grasp generation [42, 43] learn grasp distributions conditioned on object features, typically through direct parameter prediction [24, 44, 45] or optimization using object-centric contact representations [46, 22, 23, 35]. Compared to analytical methods, these methods can effectively incorporate high-level task embeddings [47, 48, 49, 32, 31, 50, 25], making them well-suited for practical applications. Among such methods, diffusion models [51, 52] are particularly adept modeling the complex data distributions [53, 54, 55, 56, 57], and recent works [58, 59, 60, 61, 62, 63, 33] have explored diffusion-based methods improve quality and efficiency for dexterous grasp generation. For example, Dex [58] generates dexterous grasps by denoising randomly sampled grasp pose parameters conditioned on object point clouds. FastGrasp [59] introduces a ote-stage diffusion model to enhance grasp generation efficiency and incorporates physical constraints into the latent grasp representation to improve grasp quality. UGG [62] further develops unified diffusion model for multi-task learning of hand-object interactions. However, the performance of generative methods is often limited by training data quality and scale, potentially yielding less stable grasps than analytical methods. This paper proposes a fiffussionbbseed grasp transfer framework that leverages object geometric similarity via text embeddings, instead of directly learning grasp distributions from training data. Our framework offers high scalability and effectively utilizes high-quality grasps from shape templates to enhance grasp quality for novel objects.
Transfer- based Methods for Dexterous Grasp Generation. Previous transfer-basee methods either necessitate training on individual object category [27, 64], or depend on pre-defined object parts and functionalities to guide the transfer process [26, 65], which limit their ability to generalize effectively to novel tasks and categories. Recent methods employ detailed object shape representations, such as Neural Radiance Fields (NeRF) or Signed Distance Functions (SDFs), to enable higher-quality grasp transfer [66, 67, 68, 69, 70]. For instance, Tink [69] learns an implicit SDF for each object instance and transfers grasps via shape interpolation. However, it requires learning separate models for each
3

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Target branch
"Grasp the handle to drink water"
x
Difusion Loss
Encoder
E-N(0.0)
Target Enc.
Target Dec.
Mo. I (,,,
Natsy Target [Rz.
Feature Adaption
Terget [x, ca]
Template branch
h. f A.
Add noise
Templato Enc.
Template Dec.
t- WUT)
Template
Recon. Template [x
Reconstruction Loss
Figure 2: Our conditional diffusion model learns transfer contact maps via template-targe target framework. The template branch encodes shape templates (Xe,Ce) C.) -> he, while the target branch denoises latent vector 2, conditioned target geometry X,, template features h and language f. A bidirectional adaptation module A bridges these branches through feature integration.
object or category, which is computationally expensive and fundamentally limits generalization to novel cafegories. In this paper, introduce conditional diffusion model for grasp transfer, which jointly learns task specifications and the shape similarity between a given shape template and novel objects. After training novel categories and tasks without the need for per-category retraining.
of base categories, approach exhibits strong generalization to
3 Methodology
In this section, we introduce our approach for dexterous grasp generation. Given target object X, E mR represented by points, our goal is to generate the corresponding dexterous grasp configuration qa E & based on the task specification l, where denotes the degree-of- frreedom of the robot hand (e.g., k 24 for the ShadowHand) To ensure robust grasp recovery, follow the pipeline [22, 35], which first generates an object contact map C E R=xl and then optimizes the grasp parameters based the contact information. We formulate this process
contact transfer problem. Specifically, let X, E gnx3 be a shape template associated with X., and let e, denote its contact map for task L. Our objective is to transfer C, from x, to x. obtain the target contact map c, that aligns with f, which then would be used to recover the grasp configuration.
To this end, we first introduce conditional diffusion model in Sec. 3.1, which jointly integrates the geometric similarity between x, and x, with the textual task description for contact map transfer. In Sec. 3.2, we further present an object-centric part and direction mapping mechanism to enhance contact representation and present a cascaded framework based on the conditional diffusion model to jointly transfer object contact, part, and direction maps while ensuring their mutual consistency. Finally, in Sec. 3.3, we develop a robust optimization method that adaptively identifies reliable object points based part and direction predictions, efficiently leveraging the enriched contact information to recover dexterous grasp parameters with high robustness.
3.1 Conditional Diffusion Model for Contact Map Transfer
Diffusion Preliminaries. A denoising diffusion probabilistic model (DDPM) is trained to learn the transformation relationship between C, and latent variable m sampled from tractable Gaussian distribution. Formally, this learning process consists of two Markov chains: forward chain q(z)) ca)) ~N(z;; vārca, (1 xz I) that gradually adds noise to c, over timesteps, where ā := I1. a2 is the cumulative product of noise scheduling coefficients a, E (0, 1), and z; represents the noisy version of c, timestep f. The reverse process pc Zee C) iteratively reconstructs c, from zu through a noise prediction network én (2r, C, t). where C is the condition on Xe, Xa. Ce, É. The model is trained by minimizing the objective:
(()) = E ||c C,t)),,,t) 2
(1)
where 14 and A are the uniform and Gaussian distributions, respectively. While this vanilla conditional diffusion implicitly captures the distribution of contact maps, it would fall short in handling the

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shape variation between x and Xa for contact map transfer. We therefore present a conditional diffusion model with dual mapping architecture to enhance the geometric feature learning of the shape template and novel target objects, and to improve the contact map transfer performance.
Diffusion with Dual Mapping Branch. As shown in Fig. 2, we propose diffusion architecture consisting of a template branch and a target branch to jointy learn template feature he and target feature hg. coupled with dual feature adaptation module A that mutually integrates the learned features to bridge the shape gap between them. For the template branch, we employ a reconstruction network to encode and reconstruct the template's 's contact map. First, the point encoder fene extracts geometric and semantic features h. = fene (Xe. (xe Ce) from the template's 's point cloud and its ground- truth contact map. These features are then adapted by the adaptation module Ae. which injects target-aware characteristics from the target branch's intermediate features h Finally, the network is trained minimize the reconstruction loss:
Lrecon ||ce faee Sac (h,
For the target branch, implement the denoising network eg comprising the encoder Jene and decoder Idec. where (2, C,t) _ 9dec (Aa (x.: 2,), he), (f), explicitly combines geometric encod- ing and conditional noise prediction. In specific, we first extract target features h Gene (X,, 2e). fuse them with template features he via adaptation module Aa, and condition on language features fi(() [71]. The target decoder Iåc ultimately predicts the noise to optimize the following objective:
Laif = B.-.u(..T)..--o....l.. || - Eg(ze C,
Adaptation Module for Conditional Diffusion. The adaptation module A is designed to mutually integrate features between the template and target branches, serving as a conditioning mechanism to guide the learning process for both networks. Formally, the adaptation process for A., is defined as:
h
Ao (h h.) - MLP softmax
he
h h
+
where d is the dimension of he Similarly, Ag operates in reverse direction, merging the target feature to template. By adaptively attending to the relevant regions of the template (target) feature based on the target (template) geometric and semantic context, the model can effectively learn the inherent relationship between shape templates and novel objects.
Training and Sampling. During training, the model jointly optimizes the parameters of the template and target branches. The overall training objective combines the reconstruction loss from template branch and the diffusion loss from the target branch Leentact = frecon + >Lain where weighting factor. During inference, we first sample pure noise ZT ~ (0, I) and iteratively denoise it through 7 steps to generate the target contact map C A Zu- At each timestep L T.... ..... 1, the latent state updates as -,- = (z( C, o&E with e ~ .V(O,)) here o, determines the stochastic noise injection magnitude during denoising, following the variance schedule derived in DDPM. The conditional mean μ4 is computed by the target denoising network Eg through:
1 is
1
1 at cn ca(zi, Eo(2,, C.1))
no (zo C,
24
(5)
Vot
VI az
with C A (geno 2t), (ene(X,, ce)), f()} combining all conditions for generation.
3.2 Cascaded Conditional Diffusion for Joint Contact, Part, and Direction Transfer
Part and Direction Map for Dexterous Grasp. Despite the contact map provides a useful represen- tation of contact regions, it alone is insufficient to fully capture the complex hand-object interaction, leaving ambiguities in both the specific grasping parts and the grasping style. To address this, follow a previous work [35] on human grasp synthesis and model dexterous grasps using additional part maps and direction maps. The part map p x indicates the closest hand part for each point on the object, where b = 16 represents the 16 predefined parts of the dexterous hand. The direction map d E p encodes the direction from each point on the object to the center of its corresponding hand part. Together, these object-centric contact, part, and direction maps provide a more precise and detailed representation of the dexterous grasp. enhancing the potential for robust grasp transfer.
5

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Cascaded Diffusion Framework. Since the
three maps collectively describe different as- pects of the same hand-object interaction, it is
Conce
essential ensure the inherent consistency dur-
ing transfer. In this regard, we develop cas-
Tencplate Croup
Contact
Part
Direction
caded diffusion framework as shown in Fig. to introduce the previously generated maps as additional conditions to guide the generation of
Contact ues
Dimenikan LIHE
radi
Novel
subsequent maps.
Specifically, the generation of the target's part map Pa is conditioned on (X., Ces Pe: X. Xa, C,), where P. is the template's part map and c, is the predicted target's contact map. Similar to contact diffusion, we jointly optimize both re- construction and diffusion networks. However, since the part map is represented by b discrete labels, the template branch predicts the probability distribution over the parts for each point, and the training is supervised using negative log- likkelehhoood (NLL) loss:
Optiniaaiion
Figure 3: An overview of the cascaded diffusion framework.
b
part recon
Σ
(15) Pe
log D. (1.))
where Pe (5-1) and P (1.5) are the ground-truth and predicted association probabilities between point i and part For the direction map, employ cosine similarity measure the alignment between the reconstructed direction map de and the ground truth de:
Cdir recon
71
where d! and a the ground-truth and predicted direction vectors for point i. Please refer to Sec. A.1 for more details on the cascaded diffusion models.
3.3 Robust Dexterous Grasp Optimization with Transferred Maps
Identify Object Points with Reliable Map Values. Given the transferred contact map c, part map p. and direction map d for a novel object, we develop method identify object points with reliable map values to improve the robustness and accuracy of the recovered grasp parameters. By definition, the part map p assigns each object point to hand part, while the direction map dl provides a unit vector indicating the intended contact direction for grasping. Notably, for all points belonging to the same part in p. their corresponding direction vectors in d should theoretically converge at a single point in space. This point represents the closest joint position the robotic hand associated with the grasping action for that object part.
Leveraging this property, we can estimate the corresponding joint position for each part in by computing the intersection of direction vectors within the same part. Specifically, let x {x'li 1...., w) 33x be a part with points, and dx {d i 1.... w) E 3x denote the associated normalized directions. The corresponding joint position J can be recovered by minimizing:
[(Jxx,dx) = Σ (x' -J - (1- (d - JJ),
(8)
which computes the sum of squared distance between J and the normalized direction vectors. Given that 0D Em -2x (I- [I-d (d+) (x* J), optimizing Eq. equals to solve linear function as A = b, where = EE (I (dd)) and b = Σ - (I di (d)) ) Then, the joint position can be solved by j Alb, where A denotes the Moore-Penose pscudoinverse of A. Based on the recovered joint positions, we apply two filtering rules to select reliable object points for grasp parameter optimization: (i) The average distance between j and x should smaller than a threshold r.- This criterion eliminates parts where the direction predictions are highly noisy. (ii) Each object point in x should lie within distance threshold TN from the joint position This rule removes outliers in the part map where individual points significantly deviate from the expected grasping region.

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Analytical
Generative
Transfer
Methods
DFC [16]
DexGraspNet [17]
ContactGen [35]
UGG [62]
Tink [69]
Ours- Curs-Contact
Ours
SR (%) 1
78.98
83.63
73.00
70.50
84.65
Pen. (mm) 4
3.15
4.52
8,08
1.87
1.47
Cov. (%)
32.28
31.87
34.78
35.06
25.13
36.28
38.16
Table Performance comparison with different task-agnostic grasp generation methods. The best results are in bold. Cors-Contact denotes the result with only the transferred contact map, while Ours denotes the result after using the jointly transferred contact, part and direction maps.
high
Template
Tink
Template
low
Figure 4: Qualitative comparison with Tink for contact map transfer on novel objects.
Grasp Synthesis. Based on the remained points with reliable map values, we exploit an efficient optimization method recover the grasp parameters. The hand mesh can be reconstructed from q using differentiable forward kinematic. Given the contact map с E [Rmx1, part map E mRmxb and direction map d E mR333, iteratively update q by minimizing the following loss function:
Lsyn Acont Edi + E,,
(9)
where Ezont = LMSE(C. T) is the mean squared error between the predicted contact map è and the contact map derived from the current robot configuration q. and Edir weighted cosine similarity loss. In addition, E₁, is regularization term that ensures grasp quality by penalizing hand-object penetration and encouraging natural hand postures. We optimize 200 steps to obtain the final grasp parameters. Please refer to Sec. A.2 for more details on grasp synthesis.
Loos (d, d) is the
Experiment
In this section, will answer the following key questions through extensive experiments. (1) Does our transfer-basee framework enable high-quality grasp transfer to novel objects, and how does it compare existing grasp generation methods? (2) How well does method generalize to novel objects, unseen object categories, and new task specifications, compared to state-of the-art task-oriented grasp generation approaches? (3) How does each proposed module contribute the overall performance, including the adaptation module for conditional diffusion, cascaded diffusion framework, and robust grasp optimization method?
4.1 Experimental Settings
Datasets. To comprehensively evaluate dexterous grasp generation methods across various objects and robotic manipulation tasks, we conducted experiments the customized CapGrasp dataset [31]. CapGrasp is one the largest publicly available task-oriented grasp datasets, comprising 1.8k object instances from 32 diverse categories and providing high-quality human grasps for 50k tasks, each with specific textual descriptions. first applied the grasp retargeting method from [32] convert human grasps into ShadowHand [72] grasps. Next, we excluded object categories with only few instances (e.g., those containing single instance) and used the remaining categories for model training and evaluation. Specifically, we selected 16 out of the remaining 24 object categories for model training. Within each training category, approximately 10% of the objects were held out for testing to evaluate the model's ability to generalize to novel objects. Meanwhile, the remaining 8 categories were reserved to assess the model's generalization novel object categories.
assess grasp generation performance in robotic manipulation tasks, we
Competing Methods and Evaluation Metrics. answer question (1), we compared our method with representative dexterous grasp generation methods, including conventional analytical meth-

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"To mug sipping"
"To hold panet footage"
"To grasp the teepot to safely pour hot water"
"To hold steady for clear viewing"
RealDex
DoxGYS
Qurs
GT
ReaiDex
DoxBYS
Ours
GT
Figure 5: Qualitative comparison with generative methods unseen objects across diverse tasks.
Scen Categories
Unseen Categories
Methods
SR
Pen. J
Cov.
Cont. Emr.
Consis,
SR 1
Pen.
Cov. 1
Cont. Err. |
Consis.
RealDex [50]
42.16
3.26
23.61
0.1002
80.62
29.90
3.35
17.15
0.0975
70.85
DexGYS [32]
41.56
22.25
30.50
0.0834
74.85
39.16
23.63
32.37
0.1332
68.08
Tink [69]
62.60
1.29
24.86
0.0327
68.75
Ours-Contaed
76.14
1.68
33.55
0.0322
75.26
70.34
1.59
35.66
0.0410
75.00
Ours
79.32
1.74
36.77
0.0287
83.60
74.14
1.36
30.05
0.0363
79.28
Table 2: Performance comparison on Seen and Unseen Categories across different methods. Cont. and Consis. are two metrics used evaluate the alignment of the generated grasp with the task specifications. The best results are in bold.
ods DFC [16] and DexGraspNet [17], generative methods ContactGen [35] and UGG (a recent diffusiio-bbsed method) [62], and transfer-based method Tink [69]. Following [32, 31, 34J, quantitatively evaluate the grasp quality using representative metrics, including the overall grasp Success Rate (SR) in IsaaeGym simulator [73], Penetration Depth (Pen.), and Contact Coverage (Cov.). To answer question (2), we further compared our method with two recent generative ap- proaches, RealDex [50] and DexGYS [32], assessing their performance on novel object instances and categories across various tasks. In this experiment, we additionally assess the alignment of the gener- ated grasps with the task specifications using representative metries [74, 31, 32], including Contact Error (Cont. EIT.), VLM-assisted [75] Consistency (Consis.), Chamfer Distance (CD), R-Precision (R-Prec@ TopK), and Fréchet Inception Distance (P-FID). Please refer Sec. A.3 and Sec. A.4 for more implementation details.
4.2 Main Results
Quality evaluation in task-agnostic grasping. Focusing on evaluating the grasp quality, similar to [22, 23, 35], we evaluated different methods on novel objects belonging to seen categories. For each object category, we select five task-agnostic grasps with the lowest retargeting loss from its corresponding shape template, and transfer them various novel objects within the same category using our proposed diffusion model. For analytical methods, we perform individual analytical optimization with force closure for each novel object and select the five grasps with the lowest analytical energy function values for evaluation. For generative methods, we follow the pipeline [44] to select the top five grasps from the generated grasp candidates for quantitative assessment.
Tab. presents the average performance of all competing methods. As can be observed, both our method and the analytical methods achieve high grasp success rate across various objects. indicates that our method can effectively transfer dexterous grasps from template to diverse novel objects, maintaining high grasp quality even without performing complex force-closure-bast for each novel object. Compared to existing generative approaches, our method consistently produces higher-quality dexterous grasps for various objects, achieving a higher success rate, greater stability with smaller penetration, and better hand coverage over the object. We also compared our method with Tink, a widely used grasp transfer approach. Tink requires learning an implicit shape function for each object instance and performs grasp transfer through shape interpolation. To ensure fair comparison, we retrieved the most similar shape from the training set for each novel object based on Chamfer Distance and use its corresponding implicit shape for grasp transfer in Tink. However, Tink
optimization

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"To pass the bottle to another person"
"To grab the headband of the headphone"
"To carry a cup Uquid"
"To grasp bowl for stocking with other bowls"
Figure 6: Real-world experimental results. For each task, the left, middle, and right images show the template contact map, the transferred contact map, and the generated dexterous grasp, respectively.
Methods
SR
Pen.
Cov.
Cont.Err Err.
w/o A.
37.57
1.95
21.11
0.0522
w/o As
38.03
1.99
21.91
0.0512
w/o cascaded-I
60.71
1.87
25.08
0.0489
w/o cascaded-II
58.51
1.82
20.73
0.0594
w/o task desc.
73.85
1.83
34.03
0.0322
w/o robust opt.
55.04
1.74
27.95
0.0448
wio Robust.
Ours-
Ours
Ours
79.32
1.74 36.77
0.0287
Figure 7: Qualitative grasp comparison with / with- out robust optimization.
Table 3: Quantitative ablation study results.
is highly sensitive to shape variations, leading to noticeable drop in grasp quality when transferring grasps novel objects (as shown in Fig. 4). These results demonstrate that our proposed method can effectively transfer grasps from shape templates to a wide range of novel objects, achieving grasp quality comparable conventional analytical methods. Meanwhile, our method achieves significantly higher grasp quality compared to existing generative approaches and greater robustness to shape variations compared Tink.
Generalization evaluation in task-oriented grasping. In this experiment, we compared our ap- proach with two recent task-oriented dexterous grasp generation methods, RealDex and DexGYS, as well as the transfer-based method Tink. For RealDex, we first generated a set of grasp candidates and then use vision-language model prompted with the task description aligns with the given task. For DexGYS, the task description is directly used as an input feature to generate dexterous grasp that aligns with the specified task. Tab. 2 presents a comparative analysis of different methods on novel objects and novel object categories. As can be observed, our method effectively transfers grasps from shape templates to diverse novel objects while maintaining high grasp quality and strong alignment with task specifications. As shown in Fig. 5, across various novel objects, our approach significantly outperforms all competing methods. For the more challenging scenario of novel object categories with novel tasks, all methods experience a performance drop to varying degrees. However, our proposed method demonstrates superior generalization ability. Without requiring retraining on new object categories, it can be directly applied to novel categories to generate stable dexterous grasps for wide range of manipulation tasks. These results indicate the superior generalization capability of the proposed method for dexterous grasp generation. Please refer Sec. A.5 for more results.
select the grasp that best
Real world experiments. We conducted real-world dexterous grasping experiments to evaluate the effectiveness of our method in practical scenarios. The experiments were performed on self- developed humanoid robotic platform equipped with an Inspire dexterous hand mounted at the end of the robot arm. For visual perception, we deployed one ZED camera on the robot's head and placed two RealSense cameras on the left and right sides of the robot to capture multi-view RGB images. Given textual task instruction, we used Grounding-DINN [76] segment the target object and leveraged the recent 3D foundation model VGGT [77] to reconstruct the object's 's point cloud from the captured views. Without any re-training or fine-tuning of our model, we directly used the reconstructed point cloud to predict the dexterous grasp configuration. For grasp execution, we followed the protocol in [32], where the robot arm first moves to the predicted 6-DOF pose of the hand's root, and then the Inspire hand actuates its joint angles based on the predicted contact poses. We tested four object categories across five different task specifications, and repeated the grasping process five times and recorded the average grasp success rate for each object. A successful grasp

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is defined as lifting the object at least 30 cm from the table and holding it stably for 5 seconds. As shown in Fig. 6, our method can effectively transfer the contact map from template objects to novel. noisy objects, and achieve an average success rate of 70% across the tested categories (60% for Bowl, 80% for Bottle, 80% for Cup, and 60% for Headphones), demonstrating the practical applicability of our approach and its robustness to
observation noise.
4.3 Ablation Study
In the ablation study, we removed different individual modules from the complete diffusion model and retrained the model with the same training dataset. We then evaluated the dexterous grasp generated by different model variants on novel objects and Tab. presents the experimental results.
(i) Removing the adaptation module would result in a significant performance drop. Both adaptation modules play important roles in the dual-branch diffusion architecture. They improve the learning of complex geometric features for the shape template and novel objects, and effectively model the shape similarity between the shape template and novel objects for robust contact map transfer.
(ii) Our proposed cascaded framework is effective in improving grasp generation performance. We validate this through two ablations: (I) using three independent diffusion models separately for contact, part and direction map, and (II) a unified diffusion model that concatenates all three maps into single representation. The independent variant suffers from inter- map inconsistencies, while the unified model fails to disentangle competing learning signals (please refer to Sec. A.5 for more results). This demonstrates that the cascaded framework ensures better coherence among different grasp representations, ultimately improving grasp generation accuracy.
(iii) Incorporating textual task descriptions further enhances grasp generation. This is because the textual features improve the accuracy of the extracted conditioning features. For the same template- object pair, different textual inputs produce different condition features, which guide the model to generate task-specific map predictions. This confirms that task descriptions play a crucial role in adapting the generated grasps to different manipulation tasks.
(iv) Without the proposed robust optimization strategy, we observe that the grasp generation perfor- mance is even worse than using only the contact map (55.04 vs. 74)..). This is because, while the part map and direction map offer more detailed hand-object contact relationships, their generated results are also more susceptible to noise. As shown in Fig. 7, our proposed robust optimization strategy effectively filters out unreliable predictions in the part map and direction map, ensuring that useful information from these additional maps enhances grasp precision. Moreover, it significantly improves the robustness of the optimized grasp parameters, leading to more reliable grasp generation.
Conclusion
In this paper, propose novel transfer-based framework for dexterous grasp generation, integrating the strengths of both analytical and generative methods. We introduce a conditional diffusion model that leverages task embeddings to learn geometric similarities for contact map transfer, and further develop a cascaded diffusion framework to jointly transfer contact, part, and direction maps while maintaining their consistency. To enhance robustness, propose adaptive optimization strategy for reliable grasp parameter optimization. Extensive experiments show our method delivers high grasp quality and task adaptability, with strong generalization to unseen objects and categories, highlighting its potential for dexterous robotic and humanoid applications.
While our method shows promising results, our study is currently limited to experiments conducted on one of the most representative tivefiinered robotic hands, the Shadow To further evaluate the generality of our approach, we have conducted additional experiments on human hand grasping. The results demonstrate that our framework can be directly applied to human hands and achieves superior performance in both task-agnostic and task-oriented scenarios (please refer Sec. A.5 for detailed results). Nevertheless, further exploration of the framework's 's generalization to cross-embodiment dexterous hands with varying numbers of fingers and morphological structures remains important and promising direction for future research. Moreover, investigating effective execution policies for dexterous grasping across diverse objects, well as exploring how the transferred contact map can serve as an enhanced visual representation for lisioo-language-cctinn models, constitutes another interesting and challenging problem for real-world dexterous manipulation.
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Acknowledgments This research work has been supported by grants from the National Natural Science Foundation of China (Project No. 62322318), the Research Grants Council of Hong Kong. China (Project No. C4042-23GF), and InnoHK of the Government of Hong Kong via the Hong Kong Centre for Logistics Robotics.
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A Appendix
In this supplementary material, provide additional contents that not included in the main paper due to the space limit:
A1. Details Cascaded Diffusion Models
A2. Details of Grasp Synthesis
A3. Implementation Details
A4. Details Experimental Settings
A5. Additional Results
A6. Discussion on Broader Impacts
A.1 Details Cascaded Diffusion Models
Diffusion Model for Part Map Transfer. As mentioned the main paper, the generation of target's part map Pa is conditioned on (Xe, Ce, Xe: C.). The diffusion loss for the part map transfer is defined as:
Caart Be- ||e - -eee (Aa (h'g, h²).
where part the decoder for the part map diffusion process. For notational consistency with the contact diffusion formulation in the main paper, map diffusion process in this section, and reuse he to denote the template feature (now encoding part information), and h2 for the target feature at timestep t. The target feature h is obtained by encoding the concatenation of target's s point cloud X., the predicted contact map ča, and the noisy part map Pa at timestep using the target encoder Jenci
Jdee denote the decoder for the part
h Jene ( C.s P..)
Similarly, the template feature he is obtained by encoding the template's point cloud x contact map Ce. and part map P. using the template encoder fene:
he Sene (X,, Ce Pe).
These features are then adapted through the adaptation module Aa, which integrates h, into the target branch to guide the generation of the target part map. The overall loss function for part map generation combines the reconstruction loss and the diffusion loss:
Lpan =
Cport + Ap
ppart
where Ap
is the weighting constant.
During inference, we randomly sample noise 27 N( I) and perform T denoising steps to gradually obtain the predicted part map p.. zo conditioned Xe, Xa. Ces Pe. Ca. and t.
Diffusion Model for Direction Map Transfer. Similarly, the diffusion loss for the direction map transfer is defined as:
E1-2200,)). |||- -
Pdte (Ao (h he). t)
BiT
where dir is the decoder for the direction map diffusion process, h is the target feature at timestep and he is the template feature.
The target feature h obtained by encoding the target's point cloud x,, the predicted contact map C., the predicted part map p.. and the noisy direction map d', at timestep using the target branch encoder Genc:
hr Jene (Xa, c. Pa: dy).
The template feature h, is obtained by encoding the template's point cloud x., contact map c., part map Pe, and direction map de using the template branch encoder fene:
he fene (Xe, Ce. Pe. de).
The overall loss function for direction map generation combines the reconstruction loss and the diffusion loss:
Lait dir + ddi
where Xa- is the weighting constant.
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A.2 Details of Grasp Synthesis
The regularization term E,, ensures grasp quality by incorporating penetration, naturalness, and hand-object distance into the optimization. It is defined as:
F Apn Eren + > Fig + Adis Fdis:
(10)
where Apen. Ag. and Adis are weighting coefficients that balance the contributions of each term. In detail, the penetration penalty Epen is computed as:
Epen
Σ Reb,(
(11)
hEW
where <(h, x) is the signed distance from point sampled from the hand mesh H. the object x. The naturalness term Fig penalizes deviations of joint angles A from their upper and lower limit Oupper and Owwwrr
Egg = ||ReLU(0 uppper) ReL.U 0)1
(12)
Finally, the distance term Edis = d( h4 x) encourages the predefined keypoints h& to be closer to the object surface. Here, -( computes the Euclidean distance between the points and object.
A.3 Implementation Details
We set the number of points n and m for the template and target point cloud to 2048, respectively. We employ PointNet++ [78] as the backbone for both the template reconstruction model and the target diffusion model, which contains 4 Set Abstraction layers and 4 Feature Propagation layers point encoder and point decoder following the standard U-Net [79] framework. The feature dimensions for he and ha are both 512. The diffusion timestep t embedded into feature with 512 1512) with Swish activation. We use a pre-trained language foundation model, Bert-base base [71]. to extract the token embedding fi(E) E 768 from the task description. The adaption module A contains 4 attention heads and 64 hidden dimensions. We employ the Adam optimizer with a batch size of 56, number of workers 4, and learning rate 2e-4, jointly training the branches from scratch for 1000 epochs takes about 24 hours. The diffusion timesteps 7 are set to 1000 for both training and sampling. For robust grasp optimization, the thresholds Ta and т are both set to 0.1. The weighting coefficients for grasp synthesis are configured as Acont = 1 x 10-1, Adir = 1 x 10-2 An = 1.0, Apen 3.0, Xg 1.0, and Akp 1.0.
dimensions via two-layer MLP (I2 12 - RR 512
NVIDIA 3090 GPUs, which
A.4 Details Experimental Settings
Our customized CapGrasp [31] dataset contains 24 categories, divided into seen and unseen sets with strict category- separation to evaluate generalization:
Seen Categories (16 categories):
Binoculars, Bottle, Cameras, Cylinder bottle, Eyeglasses, Frying pan, Hammer, Light bulb, Lotion pump, Mouse, Mug, Pen, Phone, Power drill, Screwdriver, and Teapot.
Unseen Categories categories):
Bowl, Cup, Flashlight, Game controller, Headphones, Knife, Trigger sprayer, and Wineglass.
The dataset consists about 50k hand-object grasp pairs, each annotated with on average 5 task descriptions. During training, we randomly select one task description for each grasp. To facilitate training efficiency and balance the data distribution, for object categories containing more than 2k grasp pairs, randomly retain most 2k pairs per epoch during training. This sampling strategy is applied consistently to both our proposed method and all baseline methods to ensure fair comparison.
As discussed in the main paper, we evaluate the quality of generated grasps using Success Rate, Pene- tration Depth, and Contact Coverage. Success Rate (SR) evaluated in the IsaacGym simulator [73]. A successful grasp involves lifting an object with gravity at -9.8m/s* s², raising it over 10 cm, and ensuring that the object displacement remains below 2 cm after 60 simulation steps. We randomize the object position and rotation on table and run 10 trials for each grasp, taking the average as the success rate. ensure secure grasp in the simulator, we follow [22] and apply force through single-step pose optimization for all compared methods. Initially, select finger points facing the
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Table 4: Comparison of the generated human grasps on unseen objects.
Method
Pen. Volume +
Contact Ratio 1 Simulation Disp. 4
ContactGen [35]
5.44
82.75%
3.96
Tink [69]
1.91
77.62%
3.31
Ours
1.11
87.45
2.76
object and within 5mm of its surface, then define target hand pose by moving these points 2mm toward the object. Next, we optimize the hand joint parameters using a single step of gradient descent to minimize the discrepancy between the current and target poses. Finally, the force is applied the object by updating the hand pose via positional controller. Penetration Depth (Pen.) measures the maximum penetration depth from the object point cloud to the hand meshes, quantifying surface penetration. Contact Coverage (Cov.) computes the percentage of hand points within +22m of the object surface, reflecting the ratio of points in contact with the object.
Following [3 32, 74], we adopt Contact Error, P-FID, Chamfer Distance, R-Precision and VLM- assisted approach evaluate the task consistency. Contact Error (Cont. Err.) calculates the L2 distance of object contact map between the generated grasp and ground truth. P-FID computes the Fréchet Inception Distance between the generated hand point cloud and the ground truth point clouds, leveraging pre-trained feature extractor from [80]. Chamfer Distance (CD) quantifies the average point-wise discrepancy between the generated hand point cloud and the ground-truth point cloud. R-Precision (R-Prec @ TopK) evaluates the semantic alignment between the language instructions and the generated grasps. We render the generated hand-object interactions into images and use pre-trained image and text encoders from [80] to measure how well the visual output matches the textual description. For each generated grasp image, we create a candidate pool composed of its ground-trute instruction and 31 randomly selected samples. We then compute the cosine distances between the image features and the language features for every sample in the pool and then rank them accordingly. We report the Top-k retrieval accuracy (for k,1, 5), which is the percentage of trials where the ground-truth truth instruction is successfully ranked among the top k candidates. -Ls-sssisted Consistency (Consis.) is performed by prompting the GPT-4o [75] to score the consistency of the synthesized grasp based on the grasp images and task instructions. The scores provided range from 0 to 100, with higher scores indicating better consistency.
A.5 Additional Results
Evaluation on Human Grasp Generation. In the main paper, we primarily use robotic hand [72] for evaluation. Apart from the robotic hand, we also evaluate our method on the human grasp generation. In specific, we change the three object-centric maps on template from robotic hand to human hand parameterized MANO model [81]. We compare our method with one generative method, ContactGen [35], and one transfer-based method, Tink [69]. Both methods leverage contact map, part map, and direction map as intermediate representations for dexterous grasp generation. Following [35], evaluate the generated grasps based on the Penetration Volume (Pen. Volume), Contact Ratio, and Simulation Displacement of the object center under gravity (Simulation Disp.). As shown in Table 4, our method achieves the best performance in all metrics, indicating that method can also be effectively applied to MANO model. Fig. 16 shows the visualization results of method unseen objects and unseen categories for various task descriptions.
More Visualization Results. Fig. 13 shows more qualitative results generated by our method. Fig. 10 presents a comparison with Tink [69] for contact map transfer on novel objects. Fig. 15 further demonstrates the visualization results of our proposed method performing contact transfer on novel object categories. The model effectively captures the geometric relationships between novel template and target objects, demonstrating strong generalizability across diverse and challenging scenarios.
Additional Results on Generalization Evaluation. Fig. 11 provides qualitative comparison with RealDex [50] and DexGYS [32] on novel objects across various tasks. Additionally, Fig. 14 illustrates the grasps generated by our proposed method on unseen categories. Tab. 5 shows the quantitative comparison of generative metries among our method, RealDex, and DexGYS. Our method significantly outperforms other methods, showing superior generalization ability across a wide range of tasks and unseen categories. To further enhance the comparative analysis with generative methods for grasp transfer, we also modified the bascline DexGYS to incorporate template
25

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Table Quantitative comparison of generative metrics with generative methods on novel categories.
Method
P-FID
CD}
R-Prec@ R-Prec@Toppf
R-Prec@ @.Prec@Top31
R-Prec@Tops:
RealDex
14.564
0.155
0.1081
0,1662
0.3784
DexGYS
10.344
0.120
0.1351
0,3243
0,4595
Ours-contact
6.381
0.037
0.2569
0,4538
0.5972
Ours
6.124
0.028
0.2322
0.4795
0.6199
DexGYS
DexGYs-Template
Ours
90%
20
0.10
90
10
25
0.05
30
0%
Rate
Penetration
Coverage
Contact Error
Consistency
Figure 8: Comparison with generative baseline with without template unseen categories
information. Specifically, introduced a new template branch in the first-stage module of DexGYS, which takes both the template object point cloud and the corresponding hand point cloud as inputs. We extracted template-relevan features using point cloud backbone and concatenated them with the original target object and textual task features to condition the grasp generation process. We re-trained this customized DexGYS+Templat model from scratch the same training dataset used for our method. As shown in the Fig. 8, incorporating the template grasp improves the performance of DexGYS to some extent, especially in terms of grasp quality and task relevance. However, the performance of this variant remains consistently inferior to our proposed model across all 8 unseen object categories. This result suggests that simply using the template grasp as additional condition, without explicitly modeling the geometric similarity between the template and the target object, is insufficient for generalizing to novel categories.
Ablation Study on Cascaded Framework. Fig. 17 compares the transferred part and direction maps between models trained independently (w/o cascaded-I) and cascaded framework. The model without the cascaded framework (columns (b.f) exhibits significant inconsistency across the three maps, highlighting the importance of the cascaded design.
Ablation Study on Task Embedding. Fig. 12 compares the transferred contact maps and generated grasps between the model without task embedding and our method. Our method (columns (b.d)) aligns better with the input task, indicating that incorporating task descriptions into the model enhance grasp adaptation to diverse tasks.
Table 6: Robustness to template selection strategy on novel categories.
Method
SR
Pen.
Cov.1
Cont. Err.
Consis.1
Ours
74.14
1.36
30.55
0.0363
79.28
Ours-Random
75.36
1.50
32.32
0.0391
77.19
Robustness to Template Variations. In main paper, for each object category, one shape template is randomly selected from the CapGrasp dataset and used consistently across all experiments. To further evaluate our model's sensitivity to the choice of shape template, we conducted additional experiments on unseen object categorics using varying template shapes. Specifically, for each target object during testing, we randomly selected different shape template (from the same category but the same instance) from CapGrasp, and directly applied our trained model for grasp transfer without any re-training. In addition, we also conducted experiments using alternative template hands generated by two different methods: DexGraspNe (an analytical method) and ContactGen (a generative method). We sampled a diverse set of grasps around the shape template using these methods, then executed them in simulation and selected the 10p-10 grasps based on success rate template hands for grasp transfer. We then evaluated the transferred grasps using three label-free quality metrics. shown in the Tab. 6 and Fig. 9, our model maintains strong performance across different template hand sources,
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demonstrating that the proposed grasp transfer framework is not restricted to fixed shape template and is robust to shape variation between the template and the target object.
A.6 Discussion Broader Impacts
In this paper, we propose transfer-based frame- work for dexterous grasp generation. By en- abling robust grasp transfer across objects within categories, our method can potentially enhance assistive robotics for daily living support, im- prove industrial automation efficiency manu- facturing and logistics, and facilitate more natu- ral physical interactions service robotics. The method's ability to handle complex shape vari- ations makes particularly valuable for applica- tions requiring adaptable grasping, such customized prosthetic control, warehouse automation for diverse products, and educational roboties platforms. These advancements may ultimately contribute to increased workplace safety, expanded accessibility for individuals with mobility impairments, and reduced physical strain in repetitive manual tasks.
Dedirasp Net
ContactGin
Durs-origina
1004
Success
Penatration
Coverage
Figure 9: Performance transferring grasps from different sources
high
Template
Tink
Ours
Template
Tink
Ours
low
Figure 10: Qualitative comparison results with Tink for contact map transfer novel objects.
firmly drinking"
pump"
hold
bottle
"To swirt mug sipping"
swirl
sipping"
ReslDex
DexGYS
Ours
ReaiDex
DexGYS
Ours
Figure 11: More qualitative comparison with generative methods on unseen objects across diverse task descriptions.
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"To the food using alt five fingers"
"To headband with fingers
"To press the camere's 's button"
"To grasp the teapot safety pour hot water"
"To use the knife to whittle wood"
"To hold the wineglass to make toast"
(a)
(b)
(d)
Figure Ablation results on the use of task embedding for contact map transfer and grasp generation. (a,c) Transferred contact maps/grasps from the model without using task embedding. (b,d) Transferred contact maps/grasps from our proposed method.
Figure 13: Visualization of grasps generated by our proposed method on diverse objects.
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"To grasp the wineglass to check the wine's clarity"
TTo hold the game controller to control en In-geme vehicte"
replace flashlight"
"To cut something with the knife"
"To firmly grip the closure trigger sprayer"
"To hold the headband to adjust the fit the headphones'
"To carry a cup filled with liquid"
Figure 14: Visualization of the grasps generated by our method on unseen categories for various task descriptions.
Flashlight
Trigger sprayer
Trigger sprayer
Cup
Game controller
Knife
Wineglass
Wineglass
Wineglass
Bowl
Headphanes
Headphones
Template
Ours
GT
Template
Ours
GT
Template
Ours
GT
Figure 15: Qualitative results of our method for contact map transfer unseen categories.
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"To hold the knife's 's handle to use it"
"To hold the stem of wineglass to drink wine"
"To tighten trigger sprayer's closure for secure storage"
"To grip the headphones to balance it head"
firmly body drinking"
grasp flashlight inspection"
"To use the mug to make a toast"
"To use the bowl to serve food using five fingers"
Figure 16: Visualization the human grasps generated by our method on unseen objects and unseen categories for various task descriptions.
(8)
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Figure 17: Qualitative ablation study of the cascaded framework. (a.e) Transferred contact maps. (b.f) Transferred part/direction maps from the model w/o cascaded-L (c.g) Transferred part/direction maps from our model. (d.h) Ground truth part/direction maps.
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Reviews:
[{"review_id": "KCVWg6oKtT", "reviewer": "Reviewer_2Xnf", "summary": "The paper tackles the long-standing generalization gap in dexterous grasp synthesis.\nInstead of learning a grasp distribution from scratch for every object, the authors propose to transfer analytically generated, high-quality grasps from a shape template to novel objects of the same category with a cascaded conditional diffusion pipeline.\nKey ingredients are:\n•\ta dual-branch diffusion model that simultaneously reconstructs the template contact map and denoises the target map, connected through bidirectional adaptation modules that encode template–target similarity ;\n•\textension from contact map to part and direction maps and a cascaded generation scheme that keeps these three modalities self-consistent ;\n•\ta robust grasp recovery routine that filters noisy map predictions and optimizes a grasp with differentiable kinematics and force-closure regularizers .\nOn the large CapGrasp benchmark (16 seen, 8 unseen categories), the method reaches analytical-level success rate (84.7 %) while outperforming recent diffusion and transfer baselines in penetration and coverage (Table 1) and retains clear advantages on unseen categories and language-conditioned tasks (Table 2) . Thorough ablations quantify the contribution of each module .", "questions": "1.\tInference latency. How long does a single grasp prediction (contact + part + direction + optimization) take on GPU/CPU? A table comparing to analytical and generative baselines would clarify practical impact.\n2.\tTemplate acquisition. In a real deployment, how would you choose or construct the shape template for a new object?\n3.\tRobustness to partial / noisy observations. Have you tested the transfer when the target point cloud is sparse or occluded? If not, please discuss expected behavior.\n4.\tFailure analysis. Please include qualitative examples where the transfer fails (large geometric discrepancy, unusual tasks) and discuss why.", "limitations": "The authors acknowledge evaluation on a single five-finger hand and purely simulated setup; extending to varied embodiments and hardware experiments remains future work . I agree and would add the reliance on template availability as another practical limitation.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths \nThe originality is good. Framing grasping as contact-map transfer with a dual-branch diffusion and a cascaded multi-map design is novel relative to prior diffusion graspers that work in pose space or single-map space. The empirical result is good. Solid architecture description, clear losses, and extensive ablations (e.g., removing adaptation drops SR from 79 → 38 %). \n## Weaknesses \n\n1.Simulation-only validation. All quantitative results are in IsaacGym; no physical robot experiments or real point-cloud noise study.\n\n2.Computation cost. Training takes 24 h on two 3090s with 1 000 diffusion steps and batch 56, and inference speed is not reported.\n\n3.Template dependence. The pipeline assumes access to a suitable shape template; the paper does not discuss template selection or failure cases when templates are sparse or mismatched.", "was_revised": true, "final_justification": "The rebuttal addressed several of my key concerns:\n\nTemplate acquisition: The authors provided a clear and practical strategy for maintaining and expanding a category-level shape template library, including the use of large-scale generative 3D models for unseen categories. This alleviates my earlier concern about template availability in realistic deployments.\n\nRobustness: Additional experiments with partial/noisy point clouds—both in simulation and with real-world reconstructions—show the method retains strong performance, with only a modest drop in success rate. This partially resolves my concern about sensitivity to observation quality.\n\nInference speed: Detailed timing results and comparisons against analytical, generative, and transfer baselines clarify that the method achieves a favorable balance between quality and runtime, and is competitive for practical use.\n\nFailure analysis: While visual examples are still missing (due to rebuttal-phase constraints), the authors gave a clear conceptual discussion of common failure modes, which they plan to include in the final version.\n\nMinor issues: Typos and formatting will be fixed.\n\nRemaining limitations:\n\nPhysical robot experiments are still absent; although real-world point cloud tests help, full hardware validation would strengthen claims.\n\nSome dependency on category-level templates remains, and while the library-based approach is practical, mismatches in topology/semantics still cause failures.\n\nWeighting: The method is technically sound, original in framing grasp transfer as cascaded multi-map diffusion, and empirically strong in simulation with additional robustness validation. While real robot validation is missing, the rebuttal resolves most of my practical concerns and clarifies feasibility.", "rebuttal": "Dear reviewer 2Xnf, thanks for your time to review our submission and provide constructive comments. In the following, we respond to your questions in detail one by one:\n\n---\n\n**Q1: Clarify how to acquire the shape template used for grasp transfer.**\n\n**A1**: We thank the reviewer for raising this important question. In our practical implementation, we maintain a shape template for each object category by collecting representative 3D mesh models from publicly available object datasets and asset libraries (e.g., [A, B]), thereby forming a library of shape templates across categories. During inference, we retrieve the corresponding shape template from the library based on the textual task description and the involved object category name. For object categories that are not included in the library beforehand, we follow the approach of [C] to leverage a large-scale pre-trained 3D generative model (e.g., [D]) to generate the required shape template based on the category-level text description. Since our proposed grasp transfer framework operates at the category level, it only requires a single canonical shape template to handle a variety of instances with different shapes and sizes within the same category. This makes the cost of maintaining such a template library affordable and scalable. Moreover, thanks to the expressiveness of the diffusion-based generative model and our design of language-conditioned geometric similarity modeling, our method does not rely on texture information and exhibits robustness to shape variations in the template. This enables us to easily expand the template library using 3D generative models and ensures applicability across a wide range of object categories. We will append these implementation details in our revised manuscript.\n\n[A] Chang A. X. et al. ShapeNet: an information-rich 3D model repository. Arxiv 2015.\n\n[B] Deitke, Matt, et al. Objaverse: A universe of annotated 3d objects. CVPR 2023.\n\n[C] Wang, Zhenwei, et al. Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion. ICLR 2025.\n\n[D] Meshy: Create stunning 3d models with ai. 2024.\n\n---\n\n**Q2: Robustness to object point cloud artifacts in simulation and real-world settings.**\n\n**A2**: We appreciate the reviewer’s insightful comment. Before reporting our robustness analysis, we would like to clarify that during training, we applied standard point cloud data augmentation techniques, including random point sampling and additive Gaussian noise in the 3D coordinates, to improve the grasp transfer model’s robustness to real-world point cloud artifacts. Following the reviewer’s suggestion, we conducted robustness evaluations in both simulation and real-world settings. In simulation, we followed [A] to randomly mask out 30% of the object points to simulate irregular and incomplete point cloud distributions. Additionally, we added Gaussian noise with a standard deviation of 0.03 to the remaining points to emulate potential sensor-induced coordinate errors. As shown in the table below, our grasp transfer model remains robust under such conditions, with no significant drop in quantitative grasp quality metrics. \n\nTable A: Quantitative Results of Robustness to Simulated Point Cloud Noise\n\n| Test Data | SR (%) $\\uparrow$ | Pen $\\downarrow$ | Cov $\\uparrow$ | Cont. Err. $\\downarrow$ | Consis. $\\uparrow$ |\n| --------- | :-----------: | :--------------: | :------------: | :---------------------: | :----------------: |\n| Clean     |     74.14     |       1.36       |     30.05      |         0.0363          |       79.28        |\n| Noisy     |     69.34     |       2.67       |     24.19      |         0.0370          |       74.13        |\n\nWhile the grasp success rate slightly decreased from 74.14% to 69.34%, we found this was primarily due to missing points in critical object regions (e.g., the handle of a mug or the rim of a bowl) that can impair accurate contact computation during the final optimization step. In our real-world experiments, we reconstructed object point clouds from multi-view RGB images captured by one ZED camera and two RealSense cameras. Due to partial occlusion and limited sensor coverage, the reconstructed point clouds exhibited realistic noise and missing regions. We directly fed these point clouds into our grasp transfer pipeline and executed the transferred configuration on a dexterous robotic hand. The results show that our method achieves an average grasp success rate of 70% under real-world point cloud artifacts, demonstrating strong robustness. Due to the page limit, please refer to our response to Q2 of reviewer UCkK for further details on real-world experiments.\n\n[A] Wang, Tao, et al. Sparse convolutional networks for surface reconstruction from noisy point clouds. WACV 2024.\n\n---\n\n**Q3: More detailed analysis on the inference speed.**\n\n**A3**: We thank the reviewer for the valuable suggestion. To provide a detailed breakdown of the inference speed, we conducted repeated grasp transfer experiments on 10 different objects across 50 task specifications. All experiments were performed on a workstation equipped with an Intel(R) Xeon(R) Gold 6326 CPU and an NVIDIA RTX 3090 GPU (24 GB). On average, our method requires 14.5 seconds for contact map transfer, 15.8 seconds for part map transfer, and 16.3 seconds for direction map transfer. These steps are followed by a final grasp optimization stage, which takes approximately 15.6 seconds. We also compared the overall inference time of our method with two analytical approaches, two generative baselines, and one transfer-based baseline. \n\nTable B: Quantitative Comparison of Inference Time for Grasp Generation\n\n| Method                |  DFC  | DexGraspNet | ContactGen | UGG  | Tink | Ours-Contact | Ours |\n| --------------------- | :---: | :---------: | :--------: | :--: | :--: | :----------: | :--: |\n| Time (s) $\\downarrow$ | >1000 |     > 400     |    17.1    | 76.0 | 87.6 |     ~30.1     | 62.2 |\n\nAs shown in the table, our method generates high-quality grasps more than 10× faster than analytical methods, while achieving comparable or even superior grasping quality and success rates (i.e., 84.65 for ours vs. 78.98 for DFC and 83.64 for DexGraspNet). Compared to generative methods, our framework operates at a similar inference speed but significantly outperforms them in grasp quality and success rate (i.e., 84.65 for ours vs. 73.00 for ContactGen and 70.50 for UGG). Overall, these results indicate that our method strikes a favorable balance between inference speed and grasping performance. We believe this makes our framework a strong foundation for further optimization and deployment in practical, time-sensitive robotic applications. We will report the detailed inference speed of our method in the revised manuscript.\n\n---\n\n**Q4: Possible failure analysis and discussion.**\n\n**A4**: We thank the reviewer for this important question. Due to policy constraints this year, we are unfortunately unable to provide qualitative visualizations of failure cases during the rebuttal phase. However, we will elaborate on these cases with qualitative examples and detailed discussion in the revised manuscript. Within our framework, the textual task specification is used not to directly predict the grasp pose, but rather as a task-relevant condition to guide the modeling of geometric similarity between the shape template and the target object. This design enhances the model’s robustness to variations in task instructions, especially compared to conventional generative methods that map text directly to grasp configurations (as demonstrated in Table 3 of the main paper). That said, failure cases do exist in our grasp transfer framework, particularly when object instances within the same category exhibit not only shape variations but also significant differences in topology or structural composition. In such cases, the success of grasp transfer depends not only on geometric similarity but also on whether the task-relevant affordance region lies within or outside the topologically mismatched area. For example, within the 'camera' category, transferring a grasp from a DSLR camera (with a protruding lens) to a compact camera (without a lens) works well for the task “to press the camera's button,” since the relevant affordance region is preserved. However, for the task “to hold the camera by its lens,” the transfer fails due to the absence of a corresponding semantic region on the target object. These limitations highlight a potential weakness in our current approach when handling large topological or semantic mismatches. In future work, we plan to incorporate object-centric semantic primitives to enhance the model’s ability to accommodate such structural variations. Thanks again for this constructive comment, and we will expand the discussion on failure cases in our revised manuscript.\n\n---\n\n**Q5: Spelling typo in section header.**\n\n**A5**: Thanks for your careful checking. We will proofread the manuscript thoroughly and correct all spelling and grammatical issues in the revised version."}, {"review_id": "azSDJDZGXp", "reviewer": "Reviewer_UCkK", "summary": "The manuscript presents a transfer-based framework that leverages a conditional diffusion model to migrate high-quality grasps from shape templates to novel objects, conditioned jointly on geometric similarity and free-form task language. Grasp generation is reformulated as the coupled synthesis of an object-centric contact map, part map, and direction map via a dual-branch diffusion network and a cascaded pipeline, which enforces cross-map consistency and yields richer contact semantics. A subsequent optimization stage filters unreliable predictions and reconstructs full hand configurations, producing stable, physically plausible grasps. Experiments on the CapGrasp benchmark indicate that the proposed method achieves superior success rate and contact coverage while reducing penetration relative to analytical, generative, and prior transfer baselines, thereby demonstrating strong generalization to unseen objects, tasks, and categories.", "questions": "1. Could the authors incorporate quantitative comparisons with additional grasp-transfer baselines such as DexFuncGrasp and SSCF to contextualize performance gains?\n2. Have the authors considered, or can they report, preliminary real-world experiments to substantiate sim-to-real transfer? Even a small-scale study would strengthen the empirical claims.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n1. **Well-motivated problem and principled solution.** The paper tackles the salient challenge of transferring dexterous grasps to novel objects and proposes a coherent, diffusion-based framework that is both conceptually sound and technically appealing.\n2. **Comprehensive evaluation.** Ablation studies isolate the contributions of each module, and benchmark comparisons substantiate the effectiveness of the overall system.\n3. **Clarity of presentation.** The methodology, optimization pipeline, and experimental protocol are described with sufficient detail, making the work straightforward to follow.\n\n## Weaknesses\n\n1. **Limited comparison with closely related work.** Several pertinent approaches—e.g., [66] for pose transfer and [i] for implicit contact-region representation—are not included in the quantitative or qualitative analysis.\n2. **Lack of hardware validation.** All results are reported in simulation; the absence of real-robot experiments leaves the practical robustness of the method unverified.\n3. **Static outcome.** The framework outputs single grasp configurations only and does not address the synthesis of full action sequences that might be required for dynamic manipulation.\n\n[i] Huang, Zeyu, et al. \"Spatial and surface correspondence field for interaction transfer.\" ACM Trans. Graph, 2024", "was_revised": true, "final_justification": "After reviewing the other reviewers’ comments and the authors’ rebuttal, I have decided to raise my score to Accept. I sincerely thank the authors for their patient, detailed, and rigorous responses.\n\nMy two main concerns—(1) comparisons with additional baselines and (2) real-world experiments—have been fully addressed in the rebuttal. I look forward to seeing the comparison details and hardware experiment results incorporated into the revised manuscript, as well as the authors’ future work on the humanoid robotic platform.", "rebuttal": "Dear reviewer UCkK, we are grateful for your time and supportive comments highlighting our method as a \"*principled solution*\" that is both \"*conceptually sound*\" and \"*technically appealing*\" to a \"*well-motivated problem*\". We are also glad you recognized our \"*comprehensive evaluation*\" and the \"*clarity of presentation*\". We have provided detailed responses to each of your questions below:\n\n---\n\n**Q1: Comparison with the additional pose transfer approach DexFuncGrasp, and contact-region representation approach SSCF.**\n\n**A1**: We sincerely thank the reviewer for pointing out these two relevant works. Following your suggestion, we have expanded our experiments to include comparisons with both DexFuncGrasp and SSCF. For DexFuncGrasp, which transfers grasps via shape interpolation between the template and target object, we note that its transfer strategy is conceptually similar to the transfer-based method Tink that we have evaluated in our main paper. DexFuncGrasp requires category-specific training to ensure interpolation quality, and thus cannot generalize to unseen object categories. To ensure fairness, we compared our method with DexFuncGrasp on various novel objects from the 16 seen categories and evaluated both human and robotic hand grasp transfers. As reported in Table A and B, our method consistently outperforms DexFuncGrasp in both settings. Furthermore, unlike DexFuncGrasp, our method does not require retraining and can directly transfer grasps to unseen object categories.\n\nTable A: Human Grasp Transfer Comparison on Seen Categories\n\n| Method       | Pen. Volume $\\downarrow$ | Contact Ratio $\\uparrow$ | Simulation Disp. $\\downarrow$ |\n| ------------ | :----------------------: | :----------------------: | :---------------------------: |\n| Dexfuncgrasp |           1.91           |          77.62%          |             3.30              |\n| Ours         |           1.11           |          87.45%          |             2.76              |\n\nTable B: Robotic Grasp Transfer Comparison on Seen Categories\n\n| Method       | Pen. $\\downarrow$ | Contact Ratio $\\uparrow$ | SR (%) $\\uparrow$ |\n| ------------ | :---------------: | :----------------------: | :---------------: |\n| Dexfuncgrasp |       1.14        |          25.13%          |       69.82       |\n| Ours         |       1.47        |          38.16%          |       84.65       |\n\nFor SSCF, which models contact regions via a learned spatial and surface correspondence field, we found that it requires a separately trained model for each object category. Moreover, its official GitHub repository does not provide training code, and only a pre-trained model for the ‘mug’ category is available. Therefore, we conducted a focused comparison on the ‘mug’ category. As shown in Table C, our method can achieve superior performance when compared with SSCF, even though their model was specifically trained for that category. These results highlight the superior generalization capability and effectiveness of our proposed approach compared to both DexFuncGrasp and SSCF. We thank the reviewer again for the valuable suggestions and will include these additional experimental results and analyses in the revised manuscript.\n\nTable C: Human Grasp Transfer Comparison on the 'Mug' Category\n\n| Method | Pen. Volume $\\downarrow$ | Contact Ratio $\\uparrow$ | Simulation Disp. $\\downarrow$ |\n| ------ | :----------------------: | :----------------------: | :---------------------------: |\n| SSCF   |           0.53           |          54.24%          |             4.41              |\n| Ours   |           0.46           |          56.70%          |             3.63              |\n\n---\n\n**Q2: Real-world robot experiments.**\n\n**A2**: We thank the reviewer for the constructive suggestion. Following your advice, we conducted real-world dexterous grasping experiments to evaluate the effectiveness of our method in practical scenarios. The experiments were performed on a self-developed humanoid robotic platform equipped with an Inspire dexterous hand mounted at the end of the robot arm. For visual perception, we deployed one ZED camera on the robot’s head and placed two RealSense cameras on the left and right sides of the robot to capture multi-view RGB images. Given a textual task instruction, we used Grounding-DINO to segment the target object and leveraged the recent 3D foundation model VGGT to reconstruct the object’s point cloud from the captured views. Without any re-training or fine-tuning of our model, we directly used the reconstructed point cloud to predict the dexterous grasp configuration. For grasp execution, we followed the protocol in [32], where the robot arm first moves to the predicted 6-DOF pose of the hand’s root, and then the Inspire hand actuates its joint angles based on the predicted contact poses. Due to limited time, we tested four novel object categories (i.e., Bowl, Wineglass, Cup, and Headphones) across five different task specifications. For each object, we repeated the grasping process five times and recorded the average grasp success rate. A successful grasp is defined as lifting the object at least 30 cm from the table and holding it stably for 5 seconds. Overall, our method achieved an average success rate of 70% across the tested categories (60% for Bowl, 80% for Wineglass, 80% for Cup, and 60% for Headphones), demonstrating the practical applicability of our approach and its robustness to real-world observation noise. We also observed two typical failure patterns in our real-world experiments: (1) collisions during the arm's approach to the object, often due to motion planning limitations, and (2) unintended contact with the table or object during finger closing, especially for grasping the rim of the cup or bowl. These issues highlight the gap between dexterous grasp generation and its robust execution in practice, which we identify as a key area for future exploration. Also, we will include a detailed description of the robot hardware setup and real-world experimental results in the revised manuscript. Thanks again for your valuable feedback.\n\n---\n\n**Q3: Only output a single grasp configuration while the dynamic manipulation may require full action sequences.**\n\n**A3**: We greatly appreciate the reviewer’s insightful comment, which well aligns with our observations during the real-world grasping experiments. Predicting a high-quality object-centric dexterous grasp pose is only the first step toward generalizable and stable dexterous manipulation. In real deployments, due to the high degrees of freedom and precision requirements of dexterous robotic hands, executing even a high-quality grasp can be challenging and often prone to failure. Generating full action sequences for dexterous grasp execution remains an open and highly challenging problem, receiving increasing attention in both reinforcement learning (RL) and vision-language-action (VLA) modeling communities. In future work, we are interested in exploring how the generalizable, task-conditioned object-centric representation produced by our transfer framework can be used to guide the training of RL policies or VLA models for sequential dexterous manipulation. Moreover, while our current model outputs a single grasp pose, apart from its use for object grasping, it provides a strong prior about the intended robot-object interaction and the preferred spatial hand-object relationship. This information can be valuable for many other robot-relevant scenarios, such as guiding pre-grasp planning, reachability analysis, safety-aware interaction design, and downstream policy learning. Thanks again for this insightful comment. We will further elaborate on this discussion in our revised manuscript."}, {"review_id": "3bfEt7CKLO", "reviewer": "Reviewer_EFeh", "summary": "This paper proposes a conditional diffusion model for dexterous grasp transfer. In detail, the proposed method generates an object contact map and transfers high-quality grasps from shape templates to new objects within the same category using a diffusion-based model, which can enhance the generalization to novel objects. The framework includes an innovative dual mapping mechanism and employs cascaded diffusion models to generate consistent contact, part, and direction maps. A robust optimization scheme recovers grasp configurations, ensuring stability and feasibility. The method achieves good performance in grasp success rates, penetration depths, and object coverage compared with other methods.", "questions": "1. Could the authors clarify the sensitivity of the model to variations in template quality or inaccuracies in template selection? (If I have not missed) How were the templates selected in the experiments?\n\n2. How would the method perform under real-world sensor noise and different types of robotic hands?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n1. Transferring contact maps through conditional diffusion models is an interesting design and shows good performance.\n\n2. The proposed conditional diffusion framework that jointly generates contact, part, and direction maps can significantly enhance grasp stability and consistency.\n\n3. Experiments show superior performance across multiple metrics compared to existing analytical, generative, and transfer-based approaches.\n\n4. The paper is well-written and easy to follow.\n\nWeaknesses:\n\n1. The performance of the proposed method may depend on the similarity between the template shape and the target object. A further study could be conducted to analyze how varying the templates influences performance. Using templates can provide additional information, simplifying the task compared to methods that do not utilize templates. Experiments could also be carried out either without templates or by introducing templates into generative models for comparative analysis.\n\n2. The proposed method involves cascaded diffusion models and optimization processes, which might pose challenges for downstream tasks, especially robotic grasping tasks with limited computational resources and real-time requirements.\n\n3. The significance of utilizing contact maps in hand-object interaction has been highlighted in prior research. It may be beneficial to include a more detailed discussion and additional references to closely related works. For example, [Ref 1] predicts an object contact map alongside the hand point cloud to model grasping motions and further refines the results through an optimization stage.\n\n[Ref 1] SAGA: Stochastic Whole-Body Grasping With Contact. ECCV 2022.", "was_revised": true, "final_justification": "This paper proposes a new conditional diffusion model for dexterous grasp transfer. The contribution is solid with novel technical components and competitive performance. With the planned revisions and additions, I believe it will be a valuable addition to the community.", "rebuttal": "Dear reviewer EFeh, we appreciate the time and effort you dedicated to reviewing our work. Our detailed, point-by-point responses are provided below:\n\n---\n\n**Q1: How is the shape template selected in the experiments, and how does varying the templates influence the performance?**\n\n**A1**: We thank the reviewer for the thoughtful question and apologize for any confusion. In our experiments, for each object category, one shape template is randomly selected from the CapGrasp dataset and used consistently across all experiments. The same template is also applied to other transfer-based baselines such as Tink to ensure a fair comparison. During training, the template-target object pairs are randomly sampled to encourage diversity, and we further employ standard point cloud augmentation techniques such as random point masking out and additive Gaussian noise to improve the model’s robustness to shape and distribution variations. To further evaluate our model’s sensitivity to the choice of shape template, we conducted additional experiments on 8 unseen object categories using varying template shapes. Specifically, for each target object during testing, we randomly selected a different shape template (from the same category but not the same instance) from CapGrasp, and directly applied our trained model for grasp transfer without any re-training. As shown in the Table, our model maintains strong performance across different template-target combinations, demonstrating that the proposed grasp transfer framework is not restricted to a fixed shape template and is robust to shape variation between the template and the target object.\n\nTable A: Robustness to Template Selection Strategy on Unseen Categories\n\n| Method      | SR (%) $\\uparrow$ | Pen. $\\downarrow$ | Cov. $\\uparrow$ | Cont. Err. $\\downarrow$ | Consis. $\\uparrow$ |\n| ----------- | :-----------: | :---------------: | :-------------: | :---------------------: | :----------------: |\n| Ours        |     74.14     |       1.36        |      30.05      |         0.0363          |       79.28        |\n| Ours-Random |     75.36     |       1.50        |      32.32      |         0.0391          |       77.19        |\n\n---\n\n**Q2: Additional comparison with generative models + templates.**\n\n**A2**: We sincerely thank the reviewer for the insightful suggestion to strengthen our experimental comparisons. Following your suggestion, we modified the recent generative model DexGYS [32] to incorporate template information. Specifically, we introduced a new template branch in the first-stage module of DexGYS, which takes both the template object point cloud and the corresponding hand point cloud as inputs. We extracted template-relevant features using a point cloud backbone and concatenated them with the original target object and textual task features to condition the grasp generation process. We re-trained this customized DexGYS+Template model from scratch on the same training dataset used for our method. \n\nTable B: Comparison With Baseline With or Without Template on Unseen Categories\n\n| Method          | SR (%) $\\uparrow$ | Pen. $\\downarrow$ | Cov. $\\uparrow$ | Cont. Err. $\\downarrow$ | Consis. $\\uparrow$ |\n| --------------- | :-----------: | :---------------: | :-------------: | :---------------------: | :----------------: |\n| DexGYS          |     39.16     |       23.63       |    **32.37**    |         0.1332          |       68.08        |\n| DexGYS+Template |     56.88     |       14.20       |      29.90      |         0.0712          |       70.98        |\n| Ours            |   **74.14**   |     **1.36**      |      30.05      |       **0.0363**        |     **79.28**      |\n\nAs shown in the table, incorporating the template grasp improves the performance of DexGYS to some extent, especially in terms of grasp quality and task relevance. However, the performance of this variant remains consistently inferior to our proposed model across all 8 unseen object categories. This result suggests that simply using the template grasp as an additional condition, without explicitly modeling the geometric similarity between the template and the target object, is insufficient for generalizing to novel categories. Furthermore, we qualitatively observe that the improvements from template conditioning in DexGYS are not always consistent, particularly when the template and target objects differ significantly in shape. These results demonstrate the effectiveness of our proposed transfer framework, which explicitly learns task-conditioned template-object geometric similarity and enables robust grasp transfer across diverse objects. Thanks again for this insightful comment to further enhance our experiments. We will include this new experimental comparison and the corresponding quantitative and qualitative results in the revised manuscript. \n\n---\n\n**Q3: More detailed analysis on real-world noise and different hands.**\n\n**A3**: We thank the reviewer for this important question. In our main paper, we have already demonstrated that our model can be applied to different five-fingered dexterous hands (e.g., the ShadowHand and a human hand model Mano), which showcases the generalization capability of our method across various hand morphologies. To further evaluate the effectiveness of our method under real-world sensor noise and more types of robotic hands, we conducted real-world dexterous grasping experiments on a self-developed humanoid robotic platform equipped with an Inspire dexterous hand mounted at the end of the robot arm. Our visual perception setup used a head-mounted ZED camera and two RealSense cameras on the left and right sides of the robot to capture multi-view RGB images. Then, we leveraged Grounding-DINO to segment the target object from the captured views according to language instruction, and employed the recent 3D foundation model VGGT to reconstruct the target’s point cloud from these segmented views. Without any re-training or fine-tuning of our diffusion model, we directly used the reconstructed point cloud to predict the dexterous grasp configuration. To execute the grasp, we adopted the protocol from [32]. First, the robot arm positioned the hand's root at the predicted 6-DOF pose. The Inspire hand then actuated its joints according to the predicted grasp poses. Due to limited time, our evaluation covered four novel object categories (Bowl, Wineglass, Cup, and Headphones) across five different task specifications. We attempted each grasp five times per object to record the average success rate. A grasp was considered successful if the object was lifted over 30 cm from the table and held securely for five seconds. Our method achieved an overall success rate of 70% across these categories (Bowl: 60%, Wineglass: 80%, Cup: 80%, Headphones: 60%), which highlights its robustness to real-world sensor noise and generalizability to different types of robotic hands. As mentioned in our future work, exploring the framework’s generalization to dexterous hands with varying numbers of fingers and morphological structures is an interesting and open research direction we plan to pursue. Additionally, we will incorporate a thorough description of our robotic platform and the results from the real-world experiments into the revised manuscript. We thank you again for your constructive suggestions.\n\n---\n\n**Q4: Possible limitation for practical application in robot platforms with limited computational resources.**\n\n**A4**: We thank the reviewer for this very important and practical comment. We agree that deploying computationally intensive models on resource-constrained robots is a critical challenge for real-world application, and we appreciate the constructive, application-focused feedback. Our framework, like many state-of-the-art generative models, is not yet optimized for real-time performance on edge devices, and we see this as an important direction for future engineering work. To bridge this gap for a practical deployment, we would investigate established pathways such as model quantization to reduce the memory footprint and a hybrid cloud/edge computing architecture, where the heavy grasp generation is offloaded to a remote server, leaving the local robot to handle low-latency execution. We will add a discussion of these limitations and potential solutions to the final version of our manuscript. Thank you again for your insightful suggestions.\n\n---\n\n**Q5: More discussion on works with hand-object interaction.**\n\n**A5**: We thank the reviewer for this good suggestion. We were indeed inspired by prior research such as SAGA and agree that object-centric representations are a highly effective way to model hand-object interaction. Building upon these works, our key contribution is a conditional diffusion model designed to transfer a known, high-quality contact map from a template to a novel object, a task specifically aimed at enhancing generalization. We will add a more detailed discussion contextualizing our work within this line of research to the revised manuscript."}, {"review_id": "JitPNY3X0G", "reviewer": "Reviewer_jrqf", "summary": "This work proposes a transferable dexterous grasp generation framework based on a conditional diffusion model, aiming to address the inefficiency of analytical methods and the poor generalization of generative approaches. The core idea is to reformulate grasp transfer as object contact generation. It employs a dual-mapping mechanism to handle shape variations, introduces part and direction maps to enhance contact details, and uses a cascaded conditional diffusion model to ensure consistency among them. Finally, a robust grasp recovery mechanism is applied to optimize grasp configurations. Experiments demonstrate that the method excels in grasp quality, efficiency, and generalization, achieving stable transfer to novel objects and tasks and outperforming existing analytical, generative, and transfer-based methods in terms of success rate, penetration depth, and other metrics.", "questions": "See Weaknesses", "limitations": "See Weaknesses", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The method is well-motivated, using contact transfer to enable generalization across objects with similar structures. The paper is well-written, with technical contributions clearly and concisely presented. \n\nWeakness\n1. The work lacks necessary real-world robot experiments to validate its applicability in practical environments.\n\n2. This paper appears to relay on relatively high-quality point clouds. In real-world settings, point clouds are often incomplete or noisy, which could compromise the effectiveness of the point cloud-based transfer process and introduce uncertainty in the subsequent optimization steps.\n\n3. For dexterous grasp generation, it is encouraged to adopt generative evaluation metrics that better reflect the model’s ability to produce aligned grasps. You may refer to the following papers [1,2].\n\n4. During testing, how the template hand is selected for each sample. And whether this choice can affect model performance.\n\n[1] Li K, Wang J, Yang L, et al. Semgrasp: Semantic grasp generation via language aligned discretization[C]//European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024: 109-127.\n\n[2] Wei Y L, Lin M, Lin Y, et al. Afforddexgrasp: Open-set language-guided dexterous grasp with generalizable-instructive affordance[J]. arXiv preprint arXiv:2503.07360, 2025.", "was_revised": true, "final_justification": "Overall, the strengths of the paper outweigh its weaknesses. It is recommended that the authors update the discussion results from the rebuttal into the final version of the paper.", "rebuttal": "Dear reviewer jrqf, thank you for your thoughtful review and constructive suggestions. In the following, we respond to your questions in detail one by one:\n\n---\n\n**Q1&Q2: Robustness to object point cloud noise and real-world robot experiments.**\n\n**A1&A2**: We appreciate the reviewer’s important comments. Following your suggestion, we performed additional robustness evaluations in both simulation and real-world settings. It is worth noting that our model was already designed for robustness, having been trained with standard point cloud data augmentation techniques (e.g., random sampling and Gaussian noise). For the new simulation analysis, we followed [A] to randomly mask out 30% of the object points to simulate irregular and incomplete point cloud distributions. As evidenced by the table below, our grasp transfer model's performance remains stable, with no significant degradation in quantitative grasp quality metrics under these conditions.\n\nTable A: Quantitative Results of Robustness to Simulated Point Cloud Noise\n\n| Test Data | SR (%) $\\uparrow$ | Pen. $\\downarrow$ | Cov. $\\uparrow$ | Cont. Err. $\\downarrow$ | Consis. $\\uparrow$ |\n| --------- | :-----------: | :--------------: | :------------: | :---------------------: | :----------------: |\n| Clean     |     74.14     |       1.36       |     30.05      |         0.0363          |       79.28        |\n| Noisy     |     69.34     |       2.67       |     24.19      |         0.0370          |       74.13        |\n\nAlthough the grasp success rate saw a minor decrease from 74.14% to 69.34%, we attribute this drop primarily to missing points in critical object regions (such as a mug's handle or a bowl's rim), which hinders accurate contact computation during the final optimization stage. For our real-world experiments, object point clouds were reconstructed from a multi-view camera setup (one ZED, two RealSense). These point clouds naturally contained realistic noise and missing areas owing to partial occlusions and limited sensor coverage. Feeding these imperfect point clouds directly into our grasp transfer pipeline, our method achieved an average success rate of 70% on a dexterous hand, underscoring its strong robustness to real-world artifacts. Due to page constraints, please see our response to Q2 of reviewer UCkK for a more detailed account of these experiments.\n\n[A] Wang, Tao, et al. Sparse convolutional networks for surface reconstruction from noisy point clouds. WACV 2024.\n\n---\n\n**Q3: More results with generative evaluation metrics.**\n\n**A3**: We thank the reviewer for this valuable suggestion. Following your recommendation, we have adopted three generative evaluation metrics from SemGrasp and Afforddexgrasp to more comprehensively assess the alignment between the generated grasps and their corresponding tasks. ***P-FID*** measures the distributional similarity between generated and ground-truth grasps with the same intention. We leverage the pre-trained feature extractor from [75] for this evaluation. ***Chamfer Distance (CD)*** quantifies the average point-wise discrepancy between the generated hand point cloud and the ground-truth point cloud. ***R-Precision*** evaluates the semantic alignment between the language instructions and the generated grasps. Specifically, we render the generated hand-object interactions into images and use pre-trained image and text encoders from [75] to measure how well the visual output matches the textual description. For each generated grasp image, we create a candidate pool composed of its ground-truth instruction and 31 randomly selected samples. We then compute the cosine distances between the image features and the language features for every sample in the pool and then rank them accordingly. We report the Top-k retrieval accuracy (for k=1, 3, 5), which is the percentage of trials where the ground-truth instruction is successfully ranked among the top k candidates.\n\nWe conducted these evaluations on the unseen categories benchmark to assess generalization. As shown in the below table, our method significantly outperforms all baselines across these generative metrics, confirming its superior ability to generalize and maintain semantic consistency.\n\nTable B: Generative Evaluation Metrics on Unseen Categories\n\n| Method       | P-FID $\\downarrow$ | CD $\\downarrow$ | R-Precision@Top1 $\\uparrow$ | R-Precision@Top3 $\\uparrow$ | R-Precision@Top5 $\\uparrow$ |\n| ------------ | :----------------: | :-------------: | :-------------------------: | :-------------------------: | :-------------------------: |\n| RealDex      |       14.564       |      0.155      |           0.1081            |           0.1662            |           0.3784            |\n| DexGYS       |       10.344       |      0.120      |           0.1351            |           0.3243            |           0.4595            |\n| Ours-contact |       6.381        |      0.037      |         **0.2569**          |           0.4538            |           0.5972            |\n| Ours         |     **6.124**      |    **0.028**    |           0.2322            |         **0.4795**          |         **0.6199**          |\n\n---\n\n**Q4: How is the template hand selected, and how robust is our model to this choice?**\n\n**A4**: We thank the reviewer for raising this important point. We apologize for the confusion and would like to clarify that in all our main experiments, we consistently use the template hand provided by the CapGrasp dataset. Importantly, for fair comparison, the same template hand is also used by other transfer-based baselines such as Tink. We choose the CapGrasp-defined template hand because it allows us to leverage the provided point-wise ground-truth contact maps on various novel objects, enabling rigorous quantitative evaluation of the transferred contact maps. Additionally, CapGrasp offers ground-truth grasps for different tasks, which further supports task-level evaluation of the transferred grasps using metrics such as Contact Error (Cont. Err.) and Chamfer Distance (CD). However, we emphasize that our method is not limited to this specific template hand. To verify the robustness of our framework to the choice of template hand, we conducted additional experiments using alternative template hands generated by two different methods: DexGraspNet (an analytical method) and ContactGen (a generative method). We sampled a diverse set of grasps around the shape template using these methods, then executed them in simulation and selected the top-10 grasps based on success rate as template hands for grasp transfer. We then evaluated the transferred grasps using three label-free quality metrics. \n\nTable C: Performance of Transferring Grasps from Different Sources\n\n| Source of Template Hand | SR (%) $\\uparrow$ | Pen. $\\downarrow$ | Cov. $\\uparrow$ |\n| ----------------------- | ------------- | ----------------- | --------------- |\n| DexGraspNet             | 82.99         | 4.21              | 32.83           |\n| ContactGen              | **86.88**     | 3.12              | 35.73           |\n| Our original            | 84.65         | **1.47**          | **38.16**       |\n\nThe results demonstrate that our model maintains strong performance across different template hand sources, indicating that our framework is robust to the selection and generation process of the template hand. This highlights the generalizability and applicability of our dexterous grasp transfer framework."}]

Author rebuttal:

Meta-review: The paper presents a conditional diffusion model for transferring high-quality dexterous grasps from shape templates to novel objects within the same category, aiming to address generalization challenges in grasp synthesis. The reviewers acknowledged several strengths of the proposed method: 1) a novel contact-map transfer strategy for grasp generation using dual-branch diffusion and cascaded multi-map design; 2) a well-motivated and principled solution with a coherent framework; 3) strong results across multiple metrics compared to existing approaches, with significant empirical improvements and comprehensive ablation studies; and 4) clear presentation. However, they also raised significant concerns initially regarding: 1) limited validation of real-world noise conditions and physical robot experiments, 2) unclear computational complexity in inference, 3) heavy reliance on shape templates without conducting robustness evaluation, and 4) insufficient comparison with related work and lacking generative metrics.

During the author-reviewer discussion, the authors provided a detailed response that included more results on real-world robot experiments, inference speed comparisons, analysis of the model's robustness to template selection, and additional comparisons with other methods using generative metrics. The rebuttal addressed most of the reviewers' concerns, and all four reviewers responded positively to the discussion. In particular, Reviewers UCkK and EFeh felt their major concerns were satisfactorily addressed and recommended acceptance, while the other two reviewers had remaining concerns regarding points 1) and 3) but maintained a positive assessment.

The AC agrees with the reviewers that the authors' rebuttal effectively addressed the main weaknesses of the initial submission and that the strengths of the paper outweigh its weaknesses. Overall, the AC believes the work is a valuable contribution to the field and therefore recommends acceptance. The authors should revise the manuscript to incorporate the reviewers' feedback and address the points discussed in the rebuttal.
Decision: Accept (poster)