Kinematics-Aware Diffusion Policy with Consistent 3D Observation and Action Space for Whole-Arm Robotic Manipulation

• URL: http://arxiv.org/abs/2512.17568v1
• Date: Fri, 19 Dec 2025 13:34:29 GMT
• Title: Kinematics-Aware Diffusion Policy with Consistent 3D Observation and Action Space for Whole-Arm Robotic Manipulation
• Abstract summary: Whole-body control of robotic manipulators with awareness of full-arm kinematics is crucial for many manipulation scenarios.<n>The typical approach for whole-arm manipulation is to learn actions in the robot's joint space.<n>This letter proposes a kinematics-aware imitation learning framework with consistent task, observation, and action spaces.
• Score: 9.547155394196158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Whole-body control of robotic manipulators with awareness of full-arm kinematics is crucial for many manipulation scenarios involving body collision avoidance or body-object interactions, which makes it insufficient to consider only the end-effector poses in policy learning. The typical approach for whole-arm manipulation is to learn actions in the robot's joint space. However, the unalignment between the joint space and actual task space (i.e., 3D space) increases the complexity of policy learning, as generalization in task space requires the policy to intrinsically understand the non-linear arm kinematics, which is difficult to learn from limited demonstrations. To address this issue, this letter proposes a kinematics-aware imitation learning framework with consistent task, observation, and action spaces, all represented in the same 3D space. Specifically, we represent both robot states and actions using a set of 3D points on the arm body, naturally aligned with the 3D point cloud observations. This spatially consistent representation improves the policy's sample efficiency and spatial generalizability while enabling full-body control. Built upon the diffusion policy, we further incorporate kinematics priors into the diffusion processes to guarantee the kinematic feasibility of output actions. The joint angle commands are finally calculated through an optimization-based whole-body inverse kinematics solver for execution. Simulation and real-world experimental results demonstrate higher success rates and stronger spatial generalizability of our approach compared to existing methods in body-aware manipulation policy learning.

Related papers

• Embodying Multi-Hand Manipulation Policies by Searching the Assignment and Null Spaces [19.655642356469748]
  Learned manipulation policies increasingly predict motions for abstract "hands"<n>Executing these trajectories on multi-arm robots is not trivial.<n>In the absence of algorithms that directly address this problem, practitioners typically extend single-arm inverse-kinematics pipelines in an ad hoc way.
  arXiv  Detail & Related papers  (2026-07-24T06:37:52Z)
• STARRY: Spatial-Temporal Action-Centric World Modeling for Robotic Manipulation [30.24537271257149]
  STARRY is a world-model-enhanced action-generation policy that aligns spatial-temporal prediction and action generation.<n>On RoboTwin 2.0, STARRY achieves 93.82% / 93.30% average success under Clean and Randomized settings.<n>These results demonstrate the effectiveness of action-centric spatial-temporal world modeling for spatially and temporally demanding robotic manipulation.
  arXiv  Detail & Related papers  (2026-04-29T16:13:39Z)
• Controllable Egocentric Video Generation via Occlusion-Aware Sparse 3D Hand Joints [87.13154261503168]
  Motion-controllable video generation is crucial for egocentric applications in virtual reality and embodied AI.<n>Existing methods often struggle to achieve 3D-consistent fine-grained hand articulation.<n>We propose a novel framework that generates egocentric videos from a single reference frame.
  arXiv  Detail & Related papers  (2026-03-12T10:02:23Z)
• Universal Pose Pretraining for Generalizable Vision-Language-Action Policies [83.39008378156647]
  Existing Vision-Language-Action (VLA) models often suffer from feature collapse and low training efficiency.<n>We propose Pose-VLA, a decoupled paradigm that separates VLA training into a pre-training phase for extracting universal 3D spatial priors.<n>Our framework follows a two-stage pre-training pipeline, establishing fundamental spatial grounding via poses followed by motion alignment.
  arXiv  Detail & Related papers  (2026-02-23T11:00:08Z)
• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration [58.4036440289082]
  Hand-object motion-capture (MoCap) offer large-scale, contact-rich demonstrations and hold promise for dexterous robotic scopes.<n>We introduce Dexplore, a unified single-loop optimization that performs repositories and tracking to learn robot control policies directly from MoCap at scale.
  arXiv  Detail & Related papers  (2025-09-11T17:59:07Z)
• AnchorDP3: 3D Affordance Guided Sparse Diffusion Policy for Robotic Manipulation [8.603450327406879]
  AnchorDP3 is a diffusion policy framework for dual-arm robotic manipulation.<n>It is trained on large-scale, procedurally generated simulation data.<n>It achieves a 98.7% average success rate in the RoboTwin benchmark.
  arXiv  Detail & Related papers  (2025-06-24T03:03:26Z)
• CoDA: Coordinated Diffusion Noise Optimization for Whole-Body Manipulation of Articulated Objects [14.230098033626744]
  Whole-body manipulation of articulated objects is a critical yet challenging task with broad applications in virtual humans and robotics.<n>We propose a novel coordinated diffusion noise optimization framework to achieve realistic whole-body motion.<n>We conduct extensive experiments demonstrating that our method outperforms existing approaches in motion quality and physical plausibility.
  arXiv  Detail & Related papers  (2025-05-27T17:11:50Z)
• Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation [88.83749146867665]
  Existing approaches learn a policy to predict a distant next-best end-effector pose.<n>They then compute the corresponding joint rotation angles for motion using inverse kinematics.<n>We propose Kinematics enhanced Spatial-TemporAl gRaph diffuser.
  arXiv  Detail & Related papers  (2025-03-13T17:48:35Z)
• GraMMaR: Ground-aware Motion Model for 3D Human Motion Reconstruction [61.833152949826946]
  We propose a novel Ground-aware Motion Model for 3D Human Motion Reconstruction, named GraMMaR. GraMMaR learns the distribution of transitions in both pose and interaction between every joint and ground plane at each time step of a motion sequence. It is trained to explicitly promote consistency between the motion and distance change towards the ground.
  arXiv  Detail & Related papers  (2023-06-29T07:22:20Z)
• Decoupling Skill Learning from Robotic Control for Generalizable Object Manipulation [35.34044822433743]
  Recent works in robotic manipulation have shown potential for tackling a range of tasks. We conjecture that this is due to the high-dimensional action space for joint control. In this paper, we take an alternative approach and separate the task of learning 'what to do' from 'how to do it' The whole-body robotic kinematic control is optimized to execute the high-dimensional joint motion to reach the goals in the workspace.
  arXiv  Detail & Related papers  (2023-03-07T16:31:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.