PA3FF:Learning Part-Aware Dense 3D Feature Field For Generalizable Articulated Object Manipulation
Authors: Yue Chen
Muqing Jiang
Kaifeng Zheng
Jiaqi Liang
Chenrui Tie
Haoran Lu
Ruihai Wu
Hao Dong
Venue: ICLR 2026 Poster (2026)
Keywords: Robotic Manipulation
Imitation Learning
3D Representation
Generalizable Policy
Primary area: applications to robotics, autonomy, planning

Abstract: Articulated object manipulation is essential for various real-world robotic tasks, yet generalizing across diverse objects remains a major challenge. A key to generalization lies in understanding functional parts (e.g., door handles and knobs), which indicate where and how to manipulate across diverse object categories and shapes. Previous works attempted to achieve generalization by introducing foundation features, while these features are mostly 2D-based and do not specifically consider functional parts. When lifting these 2D features to geometry-profound 3D space, challenges arise, such as long runtimes, multi-view inconsistencies, and low spatial resolution with insufficient geometric information. To address these issues, we propose \textbf{Part-Aware 3D Feature Field (PA3FF)}, a novel dense 3D feature with part awareness for generalizable articulated object manipulation. PA3FF is trained by 3D part proposals from a large-scale labeled datasets, via a contrastive learning formulation. Given point clouds as input, PA3FF predicts a continuous 3D feature field in a feedforward manner, where the distance between point feature reflects the proximity of functional parts: points with similar features are more likely to belong to the same part. Building on this feature, we introduce the \textbf{Part-Aware Diffusion Policy (PADP)}, an imitation learning framework aimed at enhancing sample efficiency and generalization for robotic manipulation. We evaluate PADP on several simulated and real-world tasks, demonstrating that PA3FF consistently outperforms a range of 2D and 3D representations in manipulation scenarios, including CLIP, DINOv2, and Grounded-SAM, achieving state-of-the-art performance. Beyond imitation learning, PA3FF enables diverse downstream methods, including correspondence learning and segmentation task, making it a versatile foundation for robotic manipulation.  Project page: https://pa3ff.github.io/.

Published as a conference paper at ICLR 2026
РA3FF: PART-AWARD DENSE 3D FEATURE FIELD FOR GENERALIZABLE ARTICULATED OBJECT MANIPULA-
TION
Yue Chen'*
Muqing Jiang
Kaifeng Zheng2*
Jiaqi Liang'
Chenrui Tie
Haoran Lu'
Ruihai Wu
Hao Dong"
Peking University BBijing Institute of Technology
National University of Singapore
ABSTRACT
Articulated object manipulation is essential for various real-world robotic tasks, yet generalizing across diverse objects remains major challenge. A key generaliza- tion lies in understanding functional parts (e.g.. door handles and knobs), which indicate where and how manipulate across diverse object categories and shapes. Previous works attempted to achieve generalization by introducing foundation features, while these features are mostly 2D-based and do not specifically consider functional parts. When lifting these 2D features to geometry scometry-profound challenges arise, such as long runtimes, multi-view inconsistencies, and low spatial resolution with insufficient geometric information. To address these issues, we propose Part-Aware 3D Feature Field (PA3FF), a novel dense 3D feature with part awareness for generalizable articulated object manipulation. PA3FF is trained by 3D part proposals from a large-scale labeled dataset, via a contrastive learning formulation. Given point clouds input, PA3FF predicts continuous 3D feature field in a feedforward manner, where the distance between point features reflects the proximity of functional parts: points with similar features more likely belong to the same part. Building on this feature, we introduce the Part-Aware Diffusion Policy (PADP), an imitation learning framework aimed at enhancing sample efficiency and generalization for robotic manipulation. We evaluate PADP on several simulated and wolldwrldd tasks, demonstrating that PA3FF consistently outperforms a range of 2D and 3D representations in manipulation scenarios, including CLIP, DINOv2, and Grounded-SAM, achieving state-of-the-art perfor- mance. Beyond imitation learning, PA3FF enables diverse downstream methods, including correspondence learning and segmentation tasks, making it a versatile foundation for robotic manipulation. Project page: http://pafff.github..
profound 3D space,
INTRODUCTION
The next generation of assistive robots must possess the generalization ability to manipulate objects across broad range of scenarios with ease and adaptability. To achieve this goal recent stud-
ies (Black et al. 2024; Intelligence et al.) 2025 Team et al. 2024; Kim et al. 2024b) leverage the power of 2D vision- foundation models (e.g., CLIP (Radford et al.) 2021), DINOv2 (Oquab) et al. 2023). SigLIP (Zhai et al.] 2023)) to improve performance and generalization in robotic manipulation policies. However, these representations inherently lack 3D geometry and spatial continuity, which are crucial for reasoning about object shapes. part configurations, and affordance in manipulation tasks (Zhu et al. 2024 Zhang et al., 2023 Ke al. (024a))
Some recent works attempt to lift 2D features into 3D feature fields via multi view fusion or neural rendering (Kerr al. 2023a; Shen et al. 2023 Lin et 2023 Rashid et al. 2023 Ze et al. 2023). While these methods improve the understanding of 3D objects and scenes, they are not native 3D representations, and thus usually suffer from problems such as long inference times (even minutes),
"Equal contribution
1 Corresponding author

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Published as a conference paper at ICLR 2026
Part-Aware Dense 3D Feature Field
Part-Aware Diffusion Policy
Generalizable Manipulation
Dowastream Applications
Decrease
DINOv2
РA3FF (Ours)
"Open microwave"
40
Correspondenee
30
20
PADE
10
DP3
DINO12
PA3FF (Ours)
"Pulling lid pat"
Objects
Unseen Objects
Segmentation
Figure (1) We propose PA3FF, feedforward model that predicts part-aware 3D feature fields for 3d shapes. (2) We propose paut-aware diffusion policy, which leverages PA3FF, that can efficiently generalize to unseen objects. (3) PA3FF exhibits consistency across shapes, enabling various downstream applications such correspondence and segmentation.
feature inconsistency across views (Wag et al. 2024b). and limited spatial resolution (Fu et al. 2024), making them impractical for dense, fine- -eeegga and real-time robotic manipulation.
In this paper, we propose Part-Aware 3D Feature Field (PA3FF), novel 3D-native representation designed encode dense, semantic, and part-aware features directly from point clouds. PA3FF predicts a continuous 3D feature field a feedforward manner, where the distance between features reflects the part-level awareness-ppintts with similar features are more likely to belong to the same part. We leverage Sonata, a model pretrained on 140k point clouds with self distillation, to provide rich 3D geometric priors for our proposed representation. we incorporate contrastive learning framework that establishes consistent relationships between 3D part-level features and their corresponding semantic counterparts by a collection of public dataset -Pabiitt-Mooility (Xiang al. 2020; Mo et al., 2018). 3DCoMPaT (Slim et al. 2025).
enhance the part-awareness,
PartObjaverse-Tiny (Yang et al.) 2024b)).
To demonstrate the power of PA3FF in robotic manipulation scenarios, we introduce Part-Aware Diffusion Policy (PADP), a 3D point cloud-based visuomotor policy that integrates PA3FF with a diffusion policy architecture, capability to novel shapes empower the highly sample-efficient and generalizable manipulation behaviors across objects. As shown in Figure (Right), beyond imitation learning, PA3FF enables diverse downstream methods, including correspondence learning, along with key-point proposals for planning constraints, making versatile foundation for robotic manipulation.
shown in Figure T (Left). The part awareness and generalization
We evaluate PA3FF and PADP on a broad spectrum of robotic manipulation tasks from the PartIn- struct (Yn et al. (025)).. well as the real world. PADP sets new state-of-the-art on PartInstruct with a 9.4% absolute gain, outperforming existing 2D and 3D representations with Diffusion Pol-
icy (Chi al. 2023), such CLIP (Radford al. 202T) DINOv2 (Oquab al.) 2023) and
GrM (Ren et al. 2024). We further show PADP significantly surpasses strong baseline (GenDP (Wang et al. (2024c) from real- weal-world tasks, offering 18. 75% increment. We also provide
detailed analysis of how well and why our method can generalize to novel instances.
In summary, our contributions include:
We introduce PA3FF, a 3D-native representation that encodes dense, semantic, and func- tional part-aware features directly from point clouds.
We develop PADP, diffusion policy that leverages PA3FF for generalizable manipulation with strong sample efficiency.
PA3FF can further enable diverse downstream methods, including correspondence learning and segmentation, making a versatile foundation for robotic manipulation.
We validate approach 16 PartInstruct and 8 real-world tasks, where it significantly outperforms prior 2D and 3D representations (CLIP, DINOv2, and Grounded-SAA offering 15% and 16.5% increase.
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2 RELATED WORK
3D Semantic Representation in Robotic Manipulation. One common approach to 3D semantic representation involves extracting functional or affordance information from observations Paulius
ct al.
(0166;; Kokic et al. (2017); Wu & Zhao (2022); Zhao et al. (2023b); Wang et al. (2022); Wu
al. (2023) Wen et al. (002)); Di Palo Johns (2024); Chen et al. (2024;; Wu et al. (2025a);
Liang et al. 2026)) This line of research focuses on tasks that can be accomplished using motion
primitives such as grasping, picking, and placing. However, our diffusiin-basse policy offers greater
flexibility in terms of action representation and task execution. Another approach lifts 2D foundational features (c.g.. CLIP, DINOv2) to 3D representations via multi-view fusion (Wang et al. 2024b| Kerr
ct al. 2023a Wang et al. 2024c Ke et al. 2024at] Yang et al. 22024b] Kerr et al. 2023b Qiu et al. 2024, Zou et al. (025). However, this method has several notable limitations. First, viewi-view fusion in previous approaches is computationally expensive and suffers from inconsistent features across views. Second, these methods often sacrifice spatial resolution for semantic quality in 2D features. For instance, ViT-family models process image tokens as patches, resulting in significantly lower-resolution feature maps (e.g., 14x smaller in DINOv2), which leads to a substantial loss of spatial information. In contrast, our approach leverages visual representations that are pre- pre-trained on large-scale point clouds, which enables our robot to generalize to unseen configurations efficiently.
Imitation Learning. Imitation learning (IL) has proven effective in enabling end-to-end robotic
training from expert demonstrations via supervised learning (Peng et al. 2020 Radosavovic et al.
202T; Zhao et al.) [223a]] Tie et al. 2025| Chi al., (023). However, many recent IL methods
rely on large-scale datasets to learn robust manipulation policies (Radosavovic et al. 2021 Peng
(et al. 2020). To improve data efficiency, several works (Tie et al., 2025] Yang et al. 2024a Wang
et al. 2024a) incorporate equivariance into policy architectures, thereby enhancing spatial general-
ization. Other approaches have explored multi-modal fusion of 3D vision, language instructions,
and proprioception (Gervet et al. 2023 Shridhar et al. 2023 Zhang et al. 2023] 2024, Ke et al.
2024a; Wang et al., 2025b). Despite their success, these methods typically predict discrete keyframes rather than continuous control trajectories (e.g., PerAct (Shridhar et al. (023). Act3D (Gervet et al.,
2023), Chained Diffuser (Xian et al. 2023), and 3D Diffuser Actor (Ke et al. 2024a)), limiting their fine-grained manipulation tasks. method most closely related to ours is GenDP (Wang et al. 2024c), which computes dense semantic fields by measuring cosine similarity between 2D image features and scene observations. This enables category- generaliza- tion, but suffers from two key limitations: (I) its reliance on 2D features from DINOv2 introduces inconsistencies across views; and (2) its semantic representations lack the granularity needed to identify functionally relevant object parts, which are critical for manipulation. In contrast, our method introduces a 3D-native fine-grained feature field that is function-aware, allowing for more accurate localization of interactive parts. Based on this representation, we develop manipulation policy that not only requires fewer demonstrations but also generalizes across unseen object categories, addressing both data efficiency and generalization in robotic manipulation.
effectiveness in long-horizon
METHOD
In this section, we cover the different components of our approach, as shown in 2 Initially, we present an overview of Part-Aware 3D Feature Field (PA3FF), and data strategy, training process, along with model architecture in section 3.1 Subsequently, we explore how to leverage this 3D feature field to achieve generalizable articulated object manipulation learning in section 3.2
3.1 PART-AWARE 3D FEATURE FIELD
Problem Formulation. Our objective is to design a 3D and part-awareness feature for generalizable articulated object manipulation. Given an input point cloud P = {p, E ] this model predicts continuous 3D feature field 3 + Rn that encodes the part structure and their hierarchy in feedforward manner. This feature field assigns each point p E P an n-dimensiinal latent feature vector fp), resulting in per- per-point embedding of the input point clouds. The notion of parts is captured by the proximity of features in this latent space: points Pa and Pa that belong to the same part should have similar features, i.e., fp.) f(p.).
Backbone for 3D Feature Extraction. In this stage, we aim to get a 3D feature extraction backbone that leverages the geometric cues of 3D objects and learns 3D priors from a large-scale 3D dataset.
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STAGE
STAGE
Leverage 3D Geometric Prior
Part-Aware Perception Part-Aware DifTusion Policy
Global thww Prrpe Aov
Demonstration
CALCULATE LOSS
INPUT
Environment
INPUT
Perception
Decision
Large-Scale 3D Dataset
Self- Distilliation
STAGE II Learning Part-Aware Dense 3D Feature Field
Train
Evaluntion Process
Refine Layers
Process
-00
PERCEPTION
Embedding Space
SiaLip
Noised Action
Lenguage
Ixput
Swwtthh Spout, Base of Faucet"
Sägl
PAJIF
060
Poler Cloud
Output
Input Cloud
Layers
Contrastive Learning
Flaal Actionn of
Figure 2: Overview of our Learning Framework. (1) Pretraining the PTv3 backbone extract part-aware 3D features. (2) Feature refinement via contrastive learning across objects to enhance part-level consistency and distinctiveness. (3) Downstrean usage by integrating the refined features into a diffusion policy for action generation.
Unlike prior work He et al. (224);
; Yang et al. (2024b); Kim al. (2024a); Liu al. 2023);
Zhou et al. (2023) that relies on per- sha-shape optimization to lift or distill 2D predictions or priors, instead leverage Sonata (Wu et al.) 2025b), self-supervised pre-trained Point Transformer V3 Wu et al. 2024). We then employ Sonata with its pre-trained weights as our feature extractor f()) to extract multi-scale features from point clouds. The key advantage of approach lies in addressing limitations present in prior methods that rely on 2D feature distillation. First, multi-view fusion in previous approaches is computationally slow and suffers from the problem of inconsistent features across views. Second, these methods typically sacrifice spatial resolution for semantic quality in 2D features-for example, ViT-family models process image tokens patches, resulting in much lower-resolution feature maps (e.g., 14x smaller in DINOv2), leading to significant loss of spatial information. contrast, Sonata allows for 3D dense feature extraction that maintains both geometric accuracy and semantic information. This approach offers several advantages: (a) efficient, feedforward inference; (b) consistent and complete 3D feature fields that generalize well across objects; and (c) per-point dense features that capture accurate geometric cues. However, Sonata was originally trained on scene-level data and is not directly tailored for object- or part-level representation learning. Specifically, its backbone. PoineTransformer v3 (PTv3), is designed for large-scale scenes, using aggressive downsampling to expand receptive fields and reduce computational cost. In contrast, object-level inputs smaller in both point count and spatial extent, making such downsampling suboptimal. To adapt Sonata for our task, we remove most downsampling layers in PTv3 and instead deepen the network by stacking additional transformer blocks, which enhances detail preservation and improves feature abstraction. Importantly, our overall framework is model-agnostic and can accommodate more advanced 3D feature extraction. More details can be found in Appendix A
Learning Part-Aware Dense 3D Feature Field. Building on the promising 3D priors we have obtained, we aim enhance these representations by incorporating part-aware semantic features. To achieve this, we introduce contrastive learning framework that effectively establishes consistent relationships between 3D part-level features and their corresponding semantic counterparts.
We design two complementary loss functions to achieve this goal. The first one is geometric loss, which focuses on the spatial relationships between points within the same part and between different parts. It encourages the model to bring points from the same part closer together in the feature space while pushing points from different parts apart. Given a set of feature/label pairs {fk, h=...... and assumes there Na, samples sharing label ag. The geometric loss is derived from the Supervised Contrastive Loss (Khosla et al. )0 a widely-used method in contrastive learning. The geometric loss is defined as:
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exp (fs (fs-tf//)
Goo
Σ j=1
1igj 1a.=a;
log
(I)
A= 1ish exp (f: fk/T)
where 7 1 denotes the balancing coefficient in SupCon.
In addition to geometric alignment, we introduce a semantic loss, which aligns point-level features with semantic representations derived from part names. Specifically, we leverage SigLip (Zhai et al. 2023) to encode the part names from the dataset into semantic vectors. These semantic vectors are then used as targets for aligning point features through InfoNCE Loss (van den Oord al., 2019), which encourages the model to map point-level features to their corresponding semantic representations.
Let represent the number of distinct part names in the current object category, denoted as 52, j. These part names are encoded using the SigLIP text encoder to obtain semantic representations 22 LigLip(ss), k = 1...nn. Given a set of N feature/label pairs {fs, h= I...N, where O₁ E L....., m is the index of the ground-truth part name for the k-th point feature, just like above. The semantic Loss is defined as;
exp (f: TT./T)
Sem
log
E exp (f: xx/T)
where T E R+ denotes the balancing coefficient in InfoNCE.
The total loss used for training combines both the geometric loss and the semantic loss:
Liotal LGeo + LSem
This combined loss function ensures that the model learns features that are both geometrically consistent within parts and semantically aligned with their corresponding part names. To further refine the feature representations, we propose lightweight feature refinement network, consisting of shallow per-point MLP. This network processes the output from the Sonata model and the total loss function to guide the learning process, as shown in Fig 2
3.2 PART-AWARE DIFFUSION POLICY
Problem Formulation. Part-Aware Diffusion Policy is a diffusion-basse model for action gener- ation (Chi et al. 2023 Tie et al. 2025) that takes 3D point cloud observations and robot proprio- ceptive state as input predict future action sequences (action chunks). We formulate visuomotor control as modeling the conditional distribution p(A, oz). where the observation
time is o, = .......P P 4:) with P representing the point cloud from the ith camera view and q: the pro- prioceptive state. The predicted action chunk is A, [a,, a1+1...., -1+H-1] We train Denoising Diffusion Probabilistic Model (DDPM) (Ho et al. (2000) and use Denoising Diffusion Implicit Model (DDIM) (Song al. 2022) for accelerated inference sampling. The denoising process is defined as: k-1
1 1 gh a.
BB
3
ay ,'v,
Ik=1 are scalar coefficients from predefined noise schedule, ^ ; 1 3% and _ = - B The noise term is (o, I) when A: > 1; otherwise, 3k-1 and v = 0. The model is trained by minimizing the mean squared error (MSE) between the ground-truth action a, and the model's prediction:
where ( K K ===1 and {
L(Φ) = MSE (a., Do (or, ā., A:),
(5)
where
B + BKe, I0,,1, k Uniform((L...., KK).
(6)
Policy Design. We employ our Part-Aware 3D Feature Field (PA3FF) frozen backbone to extract point cloud embeddings. These embeddings are then fed into a trainable Transformer encoder to aggregate per-point features into global representation. Since the features provided by our backbone are semantically meaningful, we use the semantic embedding of the task-critical part name as the CLS token to guide this aggregation. Next, concatenate the resulting global scene feature with the agent's 's pos and pass it through a two-layer MLP to reduce its length. producing the encoder's final output. Conditioned the compact representation, diffusion action head outputs the robot action.
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"Pulling lid of pot"
"Open drawer"
"Close box"
"Close lid of laptop"
"Open microwave"
"Open bottle"
"Put the the kettle"
"Press dispenser"
Figure 3: Task illustrations. We evaluate our model on eight downstream tasks.
EXPERIMENT
We systematically evaluate PA3FF and PADP through both simulation and real-world experiments, aiming address the following questions: (1) How does the performance of method compare to previous imitation approaches? (2) How well does our method generalize under object and environmental perturbations? (3) What factors contribute the generalization of our method novel instances? (4) Beyond imitation learning, what additional applications can PA3FF facilitate?
4.1 EXPERIMENTAL SETUP
We benchmark PADP both simulated and real-world environments. The simulated environments serve controlled platform to ensure reproducible and fair comparisons. The real-world experi- ments demonstrate the method's applicability to real- cal-world settings.
Setup. In simulation, we conduct multi-task training on the PartInstruct benchmark (in et al. 2025 which focuses on part-level fine-gra -ee-graiddd manipulation tasks. For real-world experiments, we use Franka Emika Panda robotic arm equipped with UMI fingers (Chi et al. 2024) replacing the standard parallel gripper. Perception handled by three Intel RealSense D415 depth cameras positioned around the workspace. Figure Saaturrrilllutttte
real-world setup and experimental objects.
Tasks and Metrics. We design real-world tasks covering diverse manipulation scenarios (Figure 3 The Train split uses the same object instances/environmentt as demonstrations (with randomized initial (OOD)
poses); the Test split employs unseen objects/envirrnmenss to evaluate generalization protocol generalization (Figure 5). Simulation adheres to PartInstruct's five-level
(object states [OS], object instances [OI], task-type part combinations [TP], task categories [TC], object categories [OC]); Appendix C.2 for details.
Data Collections. For the real-world experiments, we collect demonstrations by human teleoperation. The Franka arm and the gripper are teleoperated by the keyboard. Since our tasks contain more than one stage and include two robots and various objects, making the process of demonstration collection very time-consuming,
only provide 30 demonstrations for each task. For all six tasks, object poses are randomly initialized on the table. The action space contains the end-effector pose and gripper state, while observations include RGB images and corresponding depth images captured by three Intel RealSense D415 depth cameras. For simulation tasks, we leverage the demonstrations provided from Part-Instruct Yin et 2025). More detailed settings can be found in Appendix
Baselines. In order to comprehensively evaluate PADP, we carefully select six baselines. These include image-to-action imitation learning baselines, Diffusion Policy (DP) (Chi et al., (023). We also compare with models that have been specifically designed for 3D object manipulation including
Act3D (Gervet et al. 2023), RV72 (Goyal et al. 2024). 3D Diffuser Actor (3D-DA) (Ke et al. 2024b).
GenDP (Wang et al. 2024c). DP3 (Ze al. 2024). These baselines show different 2D and 3D representations for policy. For DP. DP3, and GenDP. we add language-condee coneeeiooonnnnnngggege module in the same manner PADP fuse language instructions. Details be found in Appendix
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4.2
COMPARISON WITH BASELINES
Table Simulated results across five test sets. The best-performing results are highlighted in bold
Method
Test (OS)
Test (OT)
Test (TP)
Test 4 (TC)
Test (0C)
Average
Gervet
2023
6.25.118
5.68
4.55.11
2.08111
3,88118
RVT2 Goyal
4.55+20
4.55=20
6.36+23
0.91
3.33+1a
4.04+21
3D-DA
8.08
5,05=22
4,04:119
0.0
3.70.16
4.26
DP (Chi al.
7.27+118
8.64=19
8.18+18
3.75
6,67=32
5,96#22
Ze 2024
23,18128
23.18128
18.18126
7.73118
6.67112
15,40126
GenDP Wang al.
2024c
24.34=22
23.36+22
24.53=19
10.00=20
14.61
19.36*27
Ours
36.76=23
34.33:3.6
32,45=16
13.75-20
26.67132
28.79
Comparision PA3FF with other foundation features. For intuitive understanding of PA3FF, Filusteall the feature fields various objects. DINOv2 and SigLip. two 2D methods, use rendered images from 16 views of the mesh as input. After each image is encoded, the resulting features are remapped onto the mesh surface to generate a feature map. Sonata and our PA3FR, however, directly utilize point clouds sampled from the mesh input generate feature maps. The feature maps shown here for the 2D methods represent the result of mapping mesh features onto the point clouds input to Sonata and PA3FF. Compared to 2D foundation features like DINOv2 and SigLIP, our approach leverages the continuity of Sonata features, avoiding common multi-view consistency issues when aggregating 2D features from different views. As result, the generated feature fields are smoother and less noisy (as shown in the faucet example). Our method also highlights key functional parts, such as microwave and refrigerator handles. Another limitation methods is their difficulty in capturing small or thin parts, which may occupy less than patch or pixel the 2D images and fail to be adequately represented (c.g., the refrigerator handle on the right). In contrast to Sonata, our approach explicitly promotes intra-part feature consistency and inter- part distinctiveness within specific object categories, leading to more semantically meaningful and discriminative part-level representations. More feature visualization can be found in Appendix A.3
Object
DINOv2
SigLip
Sonata
РA3FF
Object
DINOv2
SigLip
Sonata
РA3FF
Refrigerator
Bottle
Dispenser
Oven
Microwave Faucet
Pot
Chair
Figure 4: The feature field visualizations of PA3FF and other foundation features.
Performance and Comparison. Table 2 presents the main results on real-world tasks. PADP significantly outperforms several strong baselines across all tasks, by achieving mean success rate of 58.758.. 75% under unseen objects, compared to the highest success rate of 35% achieved by baselines. Figure 9 presents snapshots of the real-world experiments. Table TalleeTshone shows the results simulation tasks. Consistent with the real-world results, the simulation results also demonstrate that PADP enhances policy generalization.
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Table 2: weal-world task success rates across different methods (train/test). Each task is evaluated with 10 trials under randomized initial conditions. The best-performing results highlighted in bold
Pulling lid of pot
Open drawer
Close bux
Close lid of laptop
Method/Tan Task
Train
Test
Train
Test
Train
Test
Train
Test
DP (Chi et al.
2023
4/10
2/10
1/10
1/10
2/10
1/10
4/10
3/10
DP3 ie al.
2024
6/10
4/10
4/10
3/10
3/10
3/10
5/10
5/10
GenDP Wang èt al
2024c
7/10
6/10
5/10
5/10
3/10
3/10
6/10
4/10
PADP (Ours)
8/10
6/10
6/10
6/10
5/10
5/10
7/10
7/10
Open microwave
Open bottle
Put lid on kettle
Press dispenser
Method/Task
Train
Test
Truin
Test
Train
Test
Train
Test
DP (Chi et al. 2023
1/10
0/10
5/10
1/10
0/10
0/10
0/10
DP3 el al. 2024
3/10
1/10
4/10
3/10
3/10
1/10
1/10
1/10
GenDP (aag et al.
2024c
4/10
3/10
5/10
4/10
4/10
2/10
2/10
1/10
PADP (Ours)
6/10
5/10
8/10
6/10
6/10
5/10
4/10
3/10
Table 3: Generalization evaluation of the Open Bottle task (10 trials).
Completion Rate (%)
Method
Disturbance
Original
Spatial
Object Environmet
DP
50
40 V10
0 4.0
DP3
40
20 120
20
10 430
GenDP
50
20 130
30 120
30 420
PADP (ours)
80
70 110
60 120
60 420
Following (Wang et al. 2025a). we evaluate generalization across three dimensions: Spatial, Object and Environment. (1) Spatial Generalization: When object poses change, baseline methods struggle locate handles the Open Microwave task. PADP consistently identifies correct grasping positions (Figure 15 in Appendix D) because its part-aware feature field effectively locates functional parts and understands object geometry. (2) Object Generalization: As demonstrated in Table 2 PADP maintains robust performance unseen objects, while DP and DP3 fail new instances. GenDP leverages semantic fields for improved generalization, but PADP surpasses all methods through PA3FF's precise feature representation. For example, in the Open Microwave task (see Figure 15 in Appendix D). Despite varying appearances across microwave models, PA3FF identifies shared functional structures (handles, bodies), enabling consistent manipulation regardless of shape pose variations. (3) Environment Generalization: We evaluate robustness across four scenarios (Figure 5): original environment (Situation 0), added distractors (Situation 1), changed background (Situation 2), and combined changes (Situation 3). For the Open Bottle task, PADP maintains high performance across all conditions, while baselines degrade significantly in complex scenarios (Table 3). DP fails with background changes due to image dependency, DP3 resists color changes but struggles with distractors, and GenDP, despite outperforming other baselines, cannot handle the combined challenges of Situation 3.
W Vorkspace
Workspace
Distractors
Background Change
Distractors
Workspace
Background C hange
Spatial G Generalization
Object Generalization
Environment Generalization
Figure 5: Generalization test set of the Open Bottle task.
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4.3 VARIOUS DOWNSTREAM APPLICATIONS.
We evaluated the properties of the learned feature field in various applications, including part decomposition, 3D shape correspondences, and feature field consistency.
DINOv2
PA3FF
Source
РA3FF
DINOv2
Faucet
Chair
Lamp
Shape Correspondences
Segmentation
Figure 6: PA3FF exhibits consistency across shapes, enabling applications such correspondence learning and part segmentation.
3D Shape Correspondences. The cross-shape consistency embedded in PA3FF provides robust prior for fine-graing aeniinr point-to-point correspondence learning. As promising application, we use Functional Maps Ovsjanikov et al. (2012) to establish correspondences between source and target shape. To begin, we initialize the correspondences by finding nearest neighbors in the PA3FF feature space. These initial correspondences are then refined using Smooth Discrete Optimization Magnet iet al. (2022), which iteratively solves functional maps in coarse-to-fine fashion to recover smooth and accurate point-to-point mapping. As shown in Figure 6 we compare the performance of this method against DINOv2 Oquab et al. (2023) features. In particular, PA3FF excels in providing precise correspondences, even in challenging cases where the shapes differ significantly in topology or pose. Beyond capturing shape similarity, PA3FF is capable of encoding functional semantics of allosng match parts based on their function rather than just appearance- wwhle still maintaining smoothness in the resulting correspondences.
3D Part Segmentation. PA3FF learns a part hierarchy implicitly through contrastive learning on diverse 3D data. This hierarchical structure can also be explicitly derived using agglomerative clustering. Figure shows results obtained from the clustering of the DINOv2 and PA3FF feature maps using identical parameters. Table provide quantitative results of segmentation performance the BartNet-Ensemmlee (PartNetE) (Liu et al., (203)) dataset. PA3FF successfully identifies significant relationships between parts, feature that can be leveraged in range of practical applications.
CONCLUSION
We propose Part-Aware 3D Feature Field (PA3FF), a novel 3D feature representation that enhances generalization for articulated object manipulation by focusing on functional parts. Combined with the Part-Aware Diffusion Policy (PADP), imitation learning framework, PA3FF improves sample efficiency and generalization. Experimental results show that PADP outperforms existing 2D and 3D representations, including CLIP, DINOv2, and Grounded-SAN. in both simulated and real-word worldworld tasks. Beyond imitation learning, PA3FF enables a variety of downstream tasks, demonstrating its versatility and effectiveness in robotic manipulation.

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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENT
We gratefully acknowledge the financial support provided by the National Natural Science Foundation of China Igrant number 62376006). This funding has been instrumental in enabling research and the successful completion of this work.
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A DETAILS OF PА3FF
Table 4: Comparisons with Other Foundation Features
Method
Part-Aware
3D Representation
Dense
Semantic
DINOv2 (Quubb et al 2023
X
SigLip
Ziai c1a 6025
CLI
Callord
2021
ULIP
Xu0 al. 2025
X
V
D3Ficld
Waang et m.. 2024:0
X
LERF
(Ker a.. 20258
Sonata
(Ours(
A.1 COMPARISONS WITH OTHER FOUNDATION FEATURES
A key to generalization in articulated object manipulation lies in understanding functional parts (e.g.. door handles, drawer knobs), which indicate where and how to manipulate across diverse object categories and shapes. As shown in Table existing methods fall short in some critical dimensions:
Semantic-only methods (CLIP, SigL.p) provide global semantic understanding but lack 3D geomet- ric grounding and dense spatial resolution. They cannot answer "which specific point on this handle should the robot grasp?"
3D aware methods (ULIP, LERF, D3Field) lift 2D features into 3D space but remain coarse-grained and non-put nan-par--aware. They struggle with within-part geometric coherence: a handle on a round door versus
rectangular cabinet should share feature similarity despite shape differences.
Dense geometric methods (NDF, Sonata) achieve dense 3D representations but lack semantic separability. They cannot distinguish handle from knob based on functional semantics- geometric similarity.
Our Solution: Part-Aware 3D Feature Fields. We propose PA3FF, the first feature representation that simultaneously satisfies all four critical properties (Table part-aware, 3D-native, dense, and semantically grounded. This is essential for manipulation: robot must recognize that point merely "part of the object" but specifically "the handle," consistently across unseen, topologically distinct objects. PA3FF uniquely addresses this gap, enabling true part-level generalization for the manipulation of articulated objects.
A.2 LIMITATIONS OF FEATURE LIFTING UP
Multi images
Multi Inconsistent, Noisy Feats
Missed thin functional parts
DENO v
DINO
DINOv2
PA3FF (Ours)
Figure 7: Flaws of lifting up method.
Although 3D priors are known to enhance generalization, lifting features from 2D to 3D introduces significant challenges. Models that naively average multi-view features from frozen 2D networks suffer from inconsistent visibility across views. Rendered 2D images can also miss thin or small parts like handles buttons.
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A.3 MORE FEATURES' VISUALIZATIONS
Open the Bottle
Grasp the Bottle
Open the Drawer
Support Hands
Sit Chair
Figure 8: Heatmaps of point cloud features' similarity with several queries' text encoding.
Since the features generated by PA3FF contain semantic information, calculating feature similarity using different task statements for the same object allows us to focus on different parts of the object. The figure shows the heatmap visualization cosine similarity between different encoded instructions and features.
A.4 MORE QUANTITATIVE RESULTS PA3FF
Table 5: Segmentation Results on the PartNetE Dataset. Category mAP50s (%) are shown for different object categories. Higher values indicate better performance.
Method
Bottle
Chair
Display
Lamp
Storage Furniture
Table
Average
PointGroup Jiang al. 2020
8.0
77.2
16.7
9.8
0.0
0.0
18.6
SoftGroup Vu 2022
22.4
87.7
27.5
19.4
11.6
14.2
30.5
PartSlip Liu cf 2025
79.4
84.4
82.9
68.3
32.8
32.3
63.4
PartSlip++ Zhou 2023
78.5
86.0
74.1
66.9
36.7
33.5
62.6
Ours
94.6
90.0
86.5
69.5
49.6
33.4
70.6
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B ABLATION STUDY
To validate the effectiveness of our proposed PADP framework, conduct comprehensive ablation studies on the individual components of our method. Each ablation targets specific design choice to understand its contribution to the overall performance.
B. ABLATION COMPONENTS
We evaluate the following key components of our approach: Stacking Additional Transformers: We modify the PTv3 architecture by removing most downsampling layers and instead deepen the network through stacking additional transformer blocks. This architectural change enhances detail preservation while improving feature abstraction capabilities.
Feature Refinement via Contrastive Learning: We apply feature refinement through contrastive learning across different Sejjects(eemetrii & Semantic) to enhance part-level consistency and distinctiveness in the learned representations.
Table 6: Ablation study results showing the impact of different components on task performance across diverse tasks.
Method
Put in Drawer (%)
Turn Tap (%)
Open Box (%)
PADP (Full Method)
62
69
66
w/o Stacking Additional Transformers
58
63
59
w/o Geometric Loss
54
57
55
w/o Semantie Loss
46
53
52
Sonata + DP3
39
50
44
DP3 (Baseline)
37
47
39
B.2 ANALYSIS AND DISCUSSION
Tareeno the quantitative results of our ablation study on the "Put in Drawer" task, demonstrat- ing the contribution of each component to the overall performance. Our ablation study reveals several critical insights into the effectiveness of PADP:
Limited Gains from Direct Combination: The combination of Sonata with DP3 achieves only 39% success rate, representing a modest improvement of 2% over the DP3 baseline (37%). This demonstrates that simply integrating existing methods without architectural modifications yields limited performance gains.
Impact of Architectural Modifications: Removing the feature refinement component while main- taining our transformer modifications results in 46% success rate. This 7% improvement over the baseline combination indicates that our architectural changes the transformer stack provide meaningful benefits for manipulation tasks.
The most substantial performance degradation occurs when removing the feature refinement component, dropping from 62% to 46%. This 16% decrease high- lights the importance of contrastive learning for achieving pert-level consistency and distinctiveness in the learned representations.
Critical Role of Feature Refinement:
Synergistic Effect of Components: The full PADP method achieves 62% success rate, demonstrating that the combination of architectural modifications and feature refinement creates synergistic effect that significantly outperforms individual components.
These findings underscore that PADP's performance gains stem primarily from our novel part-aware feature field learning approach rather than merely utilizing Sonata representations. The substantial improvement from 46% to 62% when including feature refinement confirms that our algorithmic contributions are essential for addressing peve- manipulation challenges, and that Sonata alone is insufficient for solving these complex robotic tasks.
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C EXPERIMENT SETUP
In this section, provide detailed description of the experimental setup, including both real-world and simulation configurations, as well as discussion the baselines.
C.I REAL- ENVIRONMENT SETUP
C.I.1 HARDWARE SETUP
For the real-world experiments, our experimental platform built around Franka Emika Panda robotic arm, with its parallel gripper's fingers replaced by UMI fingers (Chi et al,) (024).. For perception, we employ three Intel RealSense D415 depth cameras. Figure shows our real-world setup and object used.
Figure 9: Real-world experiment environment and assets utilized.
C.1.2
TASKS DETAILS.
For the real-worke experiments, selected 8 representative tasks, covering variety of manipula- tion scenarios acenaris--including including pulling lid of pot, open drawer, close box, close lid of laptop. open microwave, open bottle, put the lid the kettle, and press dispenser to evaluate our system's performance across diverse challenges, as shown in Figure 3
Pulling lid of pot : A pot with detachable lid is placed the workspace. The robot must accurately grasp the lid's handle, lift vertically, and separate from the pot.
Open drawer:A A small drawer unit with front handle is present. The robot must grasp the handle and smoothly pull the drawer forward until is fully open.
Close box: A small hinged box sits on the table. The robot must grasp the lid's edge and rotate it along its hinge axis until the box fully closed.
Close lid of laptop:An open laptop lies on the workspace. The robot must grasp the top edge of the screen and fold it down smoothly until the laptop is fully closed.
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Open microwave:A microwave oven with front mouttmo handle is present. The robot must grasp the handle and pull the door outward until fully open.
Open bottle:A bottle with screw-on cap stands the table. The robot must grasp the cap, align its threads with the bottle neck, rotate it in the instructed direction by the required angle until the cap fully unscrewed, and then lift the cap off vertically.
Put the lid on the kettle:A kettle with a removable lid is placed on the workspace, The robot must grasp the lid, align it with the kettle opening, place smoothly on top, and press down vertically until it is securely seated.
Press dispenser:A dispenser bottle with squeeze nozzle is placed on the workspace. The robot must grasp the nozzle section with its parallel gripper and apply squeezing force to actuate the nozzle.
C.2
SIMULATION ENVIRONMENT SETUP
Table 7: Summary of the five test sets and the type of generalization each addresses.
Test Set
Type of Generalization
Test I (OS)
Novel object positions and rotations Novel object instances within the same category
Test (OI)
Test (TP)
Novel part combinations within the same task categories Novel part-level manipulation task categories Novel object categories
Test 4 (TC)
Test 5 (OC)
Benchmarks. PartInstruct contains 513 object instances across 14 categories (each annotated with part-level information) and 1302 fine-grained manipulation tasks grouped into 16 task classes. These 16 task classes include 10 seen categories for training and 6 unseen categories for testing, with each category defined by tasks that require the robot to perform a specific combination or sequence of part-level interactions.
Evaluation. To systematically evaluate the performance the learned policy, PartInstruct designed five-level evaluation protocol (see Table Each test evaluates policy in one type of generaliza- tion condition. Specifically, they focus on generalizability over initial object states(OS) novel object instances (OI), novel part combinations in the same task type (TP), novel task categories (TC), and novel object categories (OC). Detailed visualization can be viewed in Figure
14
Move the bottle the table from while holding top, then free
Figure 10: Left: Training set. Right: Test 1(OS). Novel object positions and rotations
Place gripper tip the screw the scissers.
Figure Left: Training set. Right: Test 2(OI). Novel object instances within the same category
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Grab the top of the mug and move it forwards.
Grab the handle of the and move it backwards.
Figure 12: Above: Training Below: Test 3(TP). Novel part combinations within the same task categories
Lift the bucket by its left, then rotate the left part to face front, then move to the left.
Move the bucket towards to the left in the air, then rotate handle to point towards back.
Figure 13: Above: Training set. Below: Test 4(TC). Novel part-level manipulation task categories
bts top.
by body.
Figure 14: Left: Training set. Right: Test 5(OC). Novel object categories
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C.3 DETAILS OF BASELINES
Diffusion Policy (DP) We train a CNN-based DP from scratch; the action prediction horizon is set 16 steps, with an observation horizon of 2 steps and action steps of 8. The input RGB images are cropped to size of 76 x 76. For language instructions, we use a pre-trained T5-small language encoder obtain language embedding of 512 dimensions. This language embedding then concatenated with other features to form the final feature representation.
GenDP Following the raw work (Wang et al. 2024c), we use DINOv2 and Groundinng-DIN. to extract information from multi-view 2D RGB images, and then project arbitrary 3D coordinates back each camera, interpolate to compute representations from each view, and fuse these data derive the descriptors associated with these 3D positions.
3D Diffusion Policy (DP3) The DP3 model trained under similar setup as DP, with an action prediction horizon of 16 steps, observation horizon of 2 steps, and action steps of 8. For the point cloud observations, we use an input size of 1024 points, which are downsampled from the original point cloud using the Iterative Farthest Point Sampling algorithm (Qi et al. 2017). The language instructions are processed in DP3 following the same approach as in DP.
Act3D Act3D takes image input size of 256 256. The action prediction horizon is set to 6 steps, and the observation horizon is step. Following the raw work Gervet et al. (2023), we use ResNet50He et al. (2016) the vision encoder, and use CLIP|Radies et al. (2021) embeddings for vision-language alignment. For 3D action map generation, the number of "ghost" points is set to be 10,000, with number of sampling level of 3.
3D Diffuser Actor (3D-DA) For 3D-DA, we use the front-view RGB and scene point cloud as vision inputs. The RGB image has a resolution of 256 256. Following Ke et al. (2024a), we extract visual features with pre-trained CLIP ResNet-50 encoder and use RadPoRd et al. (202T) embeddings for vision-lant language alignment. We use an interpolation length of 5 steps and an observation history of 3 steps.
RVT2 We first convert the depth map from the static camera view into a point cloud in the camera coordinates, then apply camera extrinsic to transfer the point cloud into the world coordinates, where the action heat maps will be generated, and apply supervision. The action prediction horizon chosen to be 6 steps, and the observation horizon is set to be step.
D MORE REAL- EXPERIMENT RESULTS
Ours Success
Baseline Fallures
b)
8)
Figure 15: Our policy reliably detects variations in object parts across different positions and successfully executes the corresponding tasks, whereas the baseline method exhibits weaker spatial generalization and struggles to accurately perceive the object parts after positional changes.
1. Spatial Generalization of PADP: In the "Open microwave" task, our policy accurately perceives and localizes the door handle even when the microwave is spatially displaced,
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allowing it to reliably grasp the handle and pull the door open (shown in Figure [3] left). In contrast, the baseline method exhibits weaker spatial generalization: fails to correctly identify the microwave and its handle after translation or rotation, often grasping incorrect regions and failing to open the door (shown in Figure 15 right).
2. Environment generalization of PADP: Other than the "Open microwave" task, we also conducted environment generalization experiments the "Open bottle" task. As shown in Table 3 and Figure 5 our policy maintains robust performance across varied spatial configurations, whereas baseline methods suffer significant drop in success rate.
E MORE SIMULATION RESULT
in drawer"
Tap"
"Open
"Open
Wine"
"Cluse jar"
Figure 16: Visualizations of simulation environments.
Benchmarks. RLBench comprises 100 completely unique, hand-designed tasks performed using a 7-DoF Franka Emika Panda with parallel gripper. Observations include rich proprioceptive data (joint angles, velocities, forces, gripper state) and visual data (RGB, depth, segmentation masks) from an whr-thh-shouuler stereo camera and an eye-in-hand monocular camera. Each task provides an infinite supply of notion-planned demonstrations, supporting research in imitation and few-shot learning.
Tasks Details. For our simulation experiments, we select 6 tasks from the RLBench, as shown in Figure I6
middle, and sower-with small cube resting on top. The objective is for the robot to grasp the bottom drawer's handle, pull open to an accessible position, and then precisely insert the cube into the opened drawer.
Put item in drawer: In this scenario, cabinet features three
Turn tap:In this scenario, two taps-one on the left and one on the right-each equipped with rotary handle, are present. The objective is for the robot interpret grasp that left tap's handle, and rotate it in the instructed direction until the tap is fully turned on.
Open box: In this scenario, box with hinged lid is placed before the robot. The objective is to accurately grasp the edge of the lid and lift it along its hinge axis until the box fully open.
Open oven: this scenario, oven with a front-facing handle is present. The objective is for the robot to firmly grasp the oven door handle and pull outward along its hinge until the oven is fully open.
Stack wine:In this scenario, standard wine bottle and wwrooootall-aaee wine rack designed to cradle bottles on their sides-are are placed on the work surface. The objective for the robot securely grasp the bottle, slide it into the designated rack slot on its side, and ensure it is evenly supported and perfectly centered without tilting.
Close jar:In this scenario, two jars and single lid are presented. The robot must grasp the lid, align its threads with the jar the left, and rotate it in the instructed direction until the lid is fully tightened and sealed.
Training Details. We collected expert demonstrations using the built-in scripted policies in RLBench, gathering 25 and 50 demonstrations per task for training. For DP and DP3, after training for least 2000 epochs, we select the single bert-performing epoch for evaluation: we run each policy over 10 episodes, choose the top three success rates, and report their average the final performance metric.
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For our policy, instead select the best best-performing epoch within the range of epochs 300 400 and apply the same evaluation protocol.
Results. We evaluate method RLBench tasks, shown in Figure and Table Consistent with the real-world results, the simulation results also demonstrate that PADP enhances learning efficiency and generalization.
Table Simulated results with different numbers of demonstrations. Here we use the two seen objects in the training phase to test the success rate, and conduct five trials for each object with random initialization in each task.
Method/Task
Put in Drawer
Turn Tap
Open Box
Open Oven
Place Wine
Close Jar
Number Demos
25
50
50
25
25
50
25
DP
16%
22%
19%
26%
13%
18%
23%
31%
11%
16%
4%
7%
DP3
31%
37%
41%
47%
34%
39%
51%
59%
41%
49%
9%
13%
GenDP
38%
44%
48%
55%
41%
48%
63%
68%
48%
56%
14%
19%
PADP (Ours)
52%
62% %
61 %
69
56%
66% %
77%
86 %
63%
74%
22%
30% %
F LIMITATION
Our work focuses primarily on rigid and articulated objects. While it can be extended to handle flexible objects with relatively simple deformations such as cables or ropes- it remains limited when applied to deformable objects exhibiting complex structural changes after deformation. The foundation model we rely on, Sonata, struggles to cope with such challenging cases. Addressing objects with complex deformations remains an open problem for future research. We hope our work can inspire further exploration this direction.
G THE USE OF LARGE LANGUAGE MODELS
We used a Large Language Model (LLM) only as a writing assistant to polish the language of the manuscript (e.g., grammar refinement, style adjustment, and clarity improvement). The research ideas, methodology design, experiments, and analysis were entirely conceived, implemented, and validated by the authors without reliance the LLM. The LLM did not contribute to research ideation, experimental design, or result interpretation.
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Reviews:
[{"review_id": "G8UKM8OzIr", "reviewer": "Reviewer_RqDZ", "summary": "In this work, the authors consider the task of learning representations for articulated objects which are useful in downstream manipulation tasks. Specifically, the authors propose a procedure to pre-train a neural network to map 3D point clouds to part-aware features, with two different contrastive supervision techniques (spatial and semantics). They build on top of Sonata (PTv3 pre-trained), but make some architectural modifications to enable higher-resolution representations. These representations are then used in several downstream manipulation tasks. The authors show compelling results on simulated & real tasks.", "questions": "* Lots of details of training / architecture are omitted - despite being pointed to Appendix A I didn’t see much there. Particularly interested in specific architectural modifications, fine-tuning/pre-training technique when using the pre-trained Sonata weights.\n* Why is it called a field instead of a representation? Seems to be per-point features, like querying other points in space don’t seem to be possible without altering the representation?", "limitations": "", "rating": 8, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* Their proposed architecture, pre-training, and modification are all sensible & principled approaches to handling object-level features at high-resolution\n* The results do improve over SOTA considerably\n* There are extensive ablations showing how different parts of the system contribute to performance, as well as qualitative visualizations of feature representations.\n* The paper is well-written", "weaknesses": "* It’s unclear whether the comparison with DP3 is completely valid (e.g. Sonata + DP3); the authors should clarify the difference between DP3 and the various ablations (e.g. where/when SigLip are included, architectures, etc.). It would help the authors cleanly show that 1) the point cloud architecture change and 2) the pre-training compared to DP3 make a major difference on the task (right now it’s just difficult to tell from the details of the paper).\n* It’s unclear how much the spatial vs. semantic components actually make a difference. In ablations, the contrastive pretraining (feature refinement) is not broken down by whether the spatial or semantic components make the difference\n* Details on architecture / training are a bit sparse", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "ciSINeHE7X", "reviewer": "Reviewer_7uDR", "summary": "This paper proposes a novel Part-Aware 3D Feature Field (PA3FF), which is trained via contrastive learning to integrate 3D geometric priors and functional part awareness, addressing the challenge of limited generalization in articulated object manipulation. Building upon this feature, the authors introduce the Part-Aware Diffusion Policy (PADP), an imitation learning framework , and demonstrate superior performance over existing 2D and 3D representation baselines in both simulated and real-world tasks.", "questions": "1. Generalization Source and Action Semantics Decoupling: The PartInstruct benchmark tests generalization across various factors, including Object State (OS), Object Instance (OI), Part Combination (TP), Task Category (TC), and Object Category (OC). Please confirm if the model is trained only on the Training Set data. If so, how does PADP or its language encoding module achieve generalization over action direction semantics (e.g., generalizing from *forward* to *backward* action prediction as seen in Figure 12)? This requires explaining the policy's mechanism for understanding and decoupling non-object-related semantics in the language instruction.\n2. Precise Role of Language Embedding in Feature Aggregation: The paper states that the \"semantic embedding of the task-critical part name\" is used as the CLS token in the Transformer encoder to guide aggregation. Concurrently, Language Instruction is shown as an input in Figure 2, Stage III. Please clarify which specific text input (task-critical part name vs. full language instruction) is input to the PERCEPTION part for embedding, and how it relates to the language information input to SigLip during PA3FF training.\n3. Definition of Real-World Evaluation Metrics: Table 2 reports Train/Test success rates for real-world tasks. Given the experiment statement \"Each task is evaluated with 10 trials under randomized initial conditions\", please explicitly define: Does the Train success rate represent testing under randomized initial conditions using the exact objects and scenes used for training? And does the Test success rate represent testing under randomized initial conditions using unseen object instances or environmental changes? Clarifying these definitions is crucial for interpreting the real-world generalization performance.\n4. Feature Robustness and Cross-Topology Consistency: For the same object category with significantly different spatial morphologies, can PA3FF effectively cluster and align features? For example, regarding the faucets with topologically distinct structures shown in Figure 6, please provide a quantitative assessment of PA3FF features' cross-topology consistency/transferability between them. This is needed to more fully demonstrate the robustness limits of the part-aware features.\n5. Completeness of the PADP Framework Flow: The overall flow of the PADP framework remains insufficiently clear. Please provide a detailed description of the training process for a new task, explicitly detailing like which point cloud data requires manual labeling,how the data flows in the framework, and what information needs to be unified.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. Clarity and Novelty of Motivation: The paper proposes PA3FF as a novel 3D-native feature representation, directly addressing challenges faced by lifting existing 2D foundation features to 3D space, such as long runtime, multi-view inconsistencies, and low spatial resolution. PA3FF explicitly incorporates functional part awareness, which is crucial for generalizable articulated object manipulation.\n2. Demonstrated Generalization Capability: The proposed PADP framework is claimed by the authors to achieve superior performance over baselines in both simulated and real-world environments. It demonstrates notable robustness, particularly in handling unseen objects, spatial generalization, and environment generalization tasks.\n3. Methodological Completeness: The proposed approach features a complete multi-stage learning framework: 1) leveraging the pre-trained Sonata model to extract 3D geometric priors; 2) employing contrastive learning to fuse a geometric loss and a semantic loss, thereby enhancing feature part-awareness and semantic alignment ; and 3) integrating PA3FF into a diffusion policy for action generation.", "weaknesses": "1. The architectural modification of the PTv3 backbone (removing down sampling layers and stacking additional Transformer blocks) is a core engineering contribution. However, the paper lacks sufficient quantitative details (e.g., parameter count, FLOPs, precise layer configuration) and a dedicated gain analysis for these changes. This absence of detailed exposition and architectural diagrams prevents readers from adequately assessing its contribution to the final performance and hinders the reproducibility of the research.\n2. The real-world experiments are evaluated with only 10 trials per task. This low number of evaluations in robotics may not provide sufficiently high statistical reliability to convincingly support the \"state-of-the-art\" claims. Furthermore, restricting the ablation study to a single task (\"Put in Drawer\")  severely weakens the proof of generality for component contributions across a broad range of tasks and different generalization types (e.g., OI, OC).\n3. PADP exhibits a significant drawback in inference speed compared to baselines like DP3 (4.23 FPS vs. 12.7 FPS).  Although PADP achieves higher success rates, this over 60% reduction in inference speed has not been adequately justified as a necessary trade-off (i.e., whether a  10~20% success rate gain, which does not guarantee deterministic success, is worth the real-time cost). In real-time robotic control requiring high-frequency feedback or integration into large policy frameworks, this performance-efficiency trade-off may reduce its practical applicability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iKdNhwibvp", "reviewer": "Reviewer_Kvwg", "summary": "Summary:\nThe paper proposes PA3FF, a 3D-native, part-aware dense feature field learned directly from point clouds, and PADP, a diffusion policy that uses PA3FF as a frozen perception backbone with language and robot-state conditioning. Together they target articulated-object manipulation with better sample efficiency and generalization to unseen objects, showing superior results to prior 2D/3D features plus diffusion-policy baselines in both simulation and real-world tasks, and also enabling downstream uses like correspondence and part segmentation.\n\nMain contributions:\n1.PA3FF: a part-aware 3D feature field that enforces within-part feature coherence and between-part separability, trained with contrastive objectives and text alignment to functional part names.\n2.PADP: a diffusion policy built on the frozen PA3FF backbone, conditioned on language cues and robot state, improving sample efficiency and cross-instance and cross-category generalization.\n3.Strong empirical gains over representative baselines (e.g., CLIP/DINOv2 features and DP/GenDP families) across multiple generalization splits in simulation and eight real-world articulated-object tasks.\n4.Versatility of the learned representation, supporting additional perception tasks such as shape correspondence and part segmentation.", "questions": "1. Please state the paper’s **single indispensable conceptual increment** (not implementation or loss details) and explain what **new inductive bias/representational property** it introduces.\n2. Please provide a **conceptual comparison table** contrasting NDF / DP3 / GenDP / ULIP / *this work* , and mark which elements are **first introduced** by this paper.\n3. Please report ablations that **remove the key new component(s)** (e.g., part-consistency loss, specific field structure, alignment mechanism): do all primary metrics **drop significantly**? Include statistical significance.\n4. Under **identical sensing inputs, action space, number of demonstrations, and training budget**, run **head-to-head** comparisons against the strongest nearby baselines (DP3/GenDP/NDF variants). If the method still does not win, explain how the claimed “novelty” stands.\n5. Please provide a **correlation analysis from field quality to performance**: e.g., within-part coherence, cross-view consistency versus success rate, with correlation coefficients and visualizations.\n6. Please add **degradation and robustness** studies for the language component:\n   * Synonym substitution (handle/knob/grip), hierarchical terms (door handle vs. handle);\n7. How is the vocabulary constructed and disambiguated? How do you handle **same-name different parts** or **cross-category semantic drift**? Please provide **error cases and proportions**.\n8. Please provide a **full-factorial ablation**:\n   {Backbone: PTv3 / Point Transformer / others} ×\n   {With/without Sonata or equivalent large-scale pretraining} ×\n   Report results in **both simulation and real** settings.\n\n**If the authors can satisfactorily address these questions, I would raise my score.**", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "### Quality\n\n* Pipeline is reasonably complete: pretrained 3D backbone, contrastive representation learning, language conditioning, and diffusion policy, with corresponding ablations.\n* Experiments cover simulation and a modest set of real tasks, include cross-instance/category splits, and compare against common 2D/3D feature and diffusion baselines with generally consistent gains.\n\n### Clarity\n\n* Problem framing is clear: focus on functional-part consistency to address articulated-object manipulation bottlenecks.\n* Exposition is structured (representation → policy), with losses and inputs described in layers; implementation details are sufficient for high-level replication.\n\n### Significance\n\n* Potential to reduce instance-specific engineering and data needs, especially under shifts from unseen objects or deformations.", "weaknesses": "1. Insufficient novelty (core issue)\n   The paradigm—“part-aware dense 3D representation + language prompts + diffusion policy”—reads as a combination/tuning of existing components (NDF-style dense correspondence, DP3/GenDP-style 3D-aware diffusion, ULIP-style language–3D alignment). The manuscript does not present an indispensable conceptual increment (new inductive bias/new representational property/new problem formulation); current differences are mainly in implementation and loss engineering.\n   — Suggestion: Use a “conceptual comparison + ablation proof” to pinpoint your **single unique idea**: show that removing that idea (e.g., the part-consistency term or specific field structure) causes a **significant** drop, and provide head-to-head results against the strongest nearby baselines (DP3/GenDP/NDF variants).\n\n2. Lacking verifiable “necessity evidence” for the representation claim\n   You claim “part-aware” beats a “generic 3D semantic field,” but there is no counterfactual under matched supervision and budget to show the advantage comes from the representation itself rather than backbone scale or the pretraining data distribution.\n   — Suggestion: Under the **same backbone and training budget**, swap only “part-aware field ↔ generic semantic field,” and report cross-task/cross-object gains with significance tests.\n\n3. Unclear scope of the language module’s contribution\n   Conditioning/alignment on part names is not novel, and there is no degradation/robustness quantification (synonyms, hierarchical terms, noisy/wrong labels, no-language variant) to show language is a **key** driver rather than a cosmetic add-on.\n   — Suggestion: Provide curves of **language-noise strength → performance**, and report **per-task/per-part** marginal contributions.\n\n4. Heavy reliance on strong pretrained backbones; insufficient factorized ablations\n   A large portion of the gains may come from PTv3/Sonata-style pretraining; current ablations do not sufficiently disentangle backbone capacity from your objectives/structure.\n   — Suggestion: Run **full-factorial ablations** (backbone type × with/without large-scale pretraining × with/without language alignment, contrastive losses, part supervision, structural tweaks).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "XylEGdp6L7", "reviewer": "Reviewer_SDVB", "summary": "This paper argues that robots handle new objects better when they reason about the parts that matter for action—like handles, buttons, and lids—rather than whole objects. The authors introduce a 3D “part-aware” feature field that turns a point cloud into dense features where points on the same functional part look alike, and those features are tied to plain-language part names. They then use this representation in a diffusion policy that conditions control on the named part, letting the robot plan motions directly from the 3D scene. Because the features are native to 3D, they’re more consistent across viewpoints and make part boundaries clearer. In experiments spanning simulation and eight real-world tasks, the method outperforms strong 2D-feature and 3D-policy baselines, particularly when generalizing to unseen objects and states. The same features also enable point-to-point correspondence and unsupervised part segmentation, making the approach a broadly useful backbone for part-centric perception and manipulation.", "questions": "Here are more feature splatting paper to cite:\nLERF: Language Embedded Radiance Fields (ICCV 2023)\nFeature Splatting: Language-Driven Physics-Based Scene Synthesis and Editing (ECCV 2024)\nM3: 3D-Spatial Multimodal Memory (ICLR 2025)", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "The paper introduces a 3D-native, part-aware representation that’s aligned with language and plugs cleanly into a diffusion control policy, leading to strong generalization across unseen objects, states, and tasks. The evaluation is thorough—covering simulation and eight real-world tasks with clear five-way generalization splits—and shows sizable gains over both 2D-lifted and 3D baselines. Beyond control, the same features enable point correspondence and unsupervised part segmentation, and ablations clarify why the 3D-native design outperforms view-lifted alternatives.", "weaknesses": "1. The work has extensive evaluation on robot experiment, but lack of quantitative evidence on the feature field quality.\n2. Runtime/latency: PADP runs at ~4.23 FPS vs. DP/DP3 at ~12 FPS, limiting high-frequency control. \n3. Dependence on part supervision & external text embeddings: training leans on labeled parts and SigLIP part-name embeddings; baselines may not use comparable supervision.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)