Title: Contact Map Transfer with Conditional Diffusion Model for Generalizable Dexterous Grasp Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Dexterous grasp generation

Contact map transfer
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/


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nThe 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.\n\nQUESTIONS:\nSee Weaknesses\n\nLIMITATIONS:\nSee Weaknesses", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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 .\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n1.\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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}