GraspLDP: Towards Generalizable Grasping Policy via Latent Diffusion

• URL: http://arxiv.org/abs/2602.22862v1
• Date: Thu, 26 Feb 2026 10:56:01 GMT
• Title: GraspLDP: Towards Generalizable Grasping Policy via Latent Diffusion
• Authors: Enda Xiang, Haoxiang Ma, Xinzhu Ma, Zicheng Liu, Di Huang,
• Abstract summary: This paper focuses on enhancing the grasping precision and generalization of manipulation policies learned via imitation learning.<n>Existing imitation learning techniques for grasping often suffer from imprecise grasp executions, limited spatial generalization, and poor object generalization.
• Score: 44.168491831527355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper focuses on enhancing the grasping precision and generalization of manipulation policies learned via imitation learning. Diffusion-based policy learning methods have recently become the mainstream approach for robotic manipulation tasks. As grasping is a critical subtask in manipulation, the ability of imitation-learned policies to execute precise and generalizable grasps merits particular attention. Existing imitation learning techniques for grasping often suffer from imprecise grasp executions, limited spatial generalization, and poor object generalization. To address these challenges, we incorporate grasp prior knowledge into the diffusion policy framework. In particular, we employ a latent diffusion policy to guide action chunk decoding with grasp pose prior, ensuring that generated motion trajectories adhere closely to feasible grasp configurations. Furthermore, we introduce a self-supervised reconstruction objective during diffusion to embed the graspness prior: at each reverse diffusion step, we reconstruct wrist-camera images back-projected the graspness from the intermediate representations. Both simulation and real robot experiments demonstrate that our approach significantly outperforms baseline methods and exhibits strong dynamic grasping capabilities.

Related papers

• Diffusion Policy through Conditional Proximal Policy Optimization [6.836651088754774]
  diffusion policies have shown strong potential in modeling multi-modal behaviors.<n>A key challenge is the difficulty of computing action log-likelihood under the diffusion model.<n>We propose a novel and efficient method to train a diffusion policy in an on-policy setting.
  arXiv  Detail & Related papers  (2026-03-05T04:12:13Z)
• SeFA-Policy: Fast and Accurate Visuomotor Policy Learning with Selective Flow Alignment [7.446137164545049]
  Selective Flow Alignment (SeFA) is an efficient and accurate visuomotor policy learning framework.<n>SeFA resolves this challenge by a selective flow alignment strategy.<n>It achieves superior accuracy and robustness while reducing inference latency by over 98%.
  arXiv  Detail & Related papers  (2025-11-11T18:59:39Z)
• Learning Generalizable Visuomotor Policy through Dynamics-Alignment [13.655111993491674]
  Recent approaches leveraging video prediction models have shown promising results by learning rich representations from large-scale datasets.<n>We propose a Dynamics-Aligned Flow Matching Policy (DAP) that integrates dynamics prediction into policy learning.<n>Our method introduces a novel architecture where policy and dynamics models provide mutual corrective feedback during action generation, enabling self-correction and improved generalization.
  arXiv  Detail & Related papers  (2025-10-31T02:29:33Z)
• 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)
• Reinforcement Learning via Implicit Imitation Guidance [49.88208134736617]
  A natural approach is to incorporate an imitation learning objective, either as regularization during training or to acquire a reference policy.<n>We propose to use prior data solely for guiding exploration via noise added to the policy, sidestepping the need for explicit behavior cloning constraints.<n>Our approach achieves up to 2-3x improvement over prior reinforcement learning from offline methods across seven simulated continuous control tasks.
  arXiv  Detail & Related papers  (2025-06-09T07:32:52Z)
• Maximum Total Correlation Reinforcement Learning [23.209609715886454]
  We introduce a modification of the reinforcement learning problem that additionally maximizes the total correlation within the induced trajectories.<n>In simulated robot environments, our method naturally generates policies that induce periodic and compressible trajectories.
  arXiv  Detail & Related papers  (2025-05-22T14:48:00Z)
• Towards Principled Unsupervised Multi-Agent Reinforcement Learning [49.533774397707056]
  We present a scalable, decentralized, trust-region policy search algorithm to address the problem in practical settings.<n>We show that optimizing for a specific objective, namely mixture entropy, provides an excellent trade-off between tractability and performances.
  arXiv  Detail & Related papers  (2025-02-12T12:51:36Z)
• ImitDiff: Transferring Foundation-Model Priors for Distraction Robust Visuomotor Policy [39.06557194970261]
  ImitDiff is a diffusion-based imitation learning policy guided by fine-grained semantics.<n>Our method transforms high-level instructions into pixel-level visual semantic masks.<n>ImitDiff exhibits strong generalization in zero-shot settings involving novel objects and visual distractions.
  arXiv  Detail & Related papers  (2025-02-11T14:03:57Z)
• Don't Start from Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion [16.44141792109178]
  Diffusion models learn to shape a policy by diffusing actions (or states) from standard Gaussian noise. The target policy to be learned is often significantly different from Gaussian and this can result in poor performance when using a small number of diffusion steps. We contribute both theoretical results, a new method, and empirical findings that show the benefits of using an informative source policy.
  arXiv  Detail & Related papers  (2024-02-25T12:19:21Z)
• Learning a Diffusion Model Policy from Rewards via Q-Score Matching [93.0191910132874]
  We present a theoretical framework linking the structure of diffusion model policies to a learned Q-function.<n>We propose a new policy update method from this theory, which we denote Q-score matching.
  arXiv  Detail & Related papers  (2023-12-18T23:31:01Z)
• Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning [70.20191211010847]
  Offline reinforcement learning (RL) aims to learn an optimal policy using a previously collected static dataset. We introduce Diffusion Q-learning (Diffusion-QL) that utilizes a conditional diffusion model to represent the policy. We show that our method can achieve state-of-the-art performance on the majority of the D4RL benchmark tasks.
  arXiv  Detail & Related papers  (2022-08-12T09:54:11Z)
• Off-Policy Imitation Learning from Observations [78.30794935265425]
  Learning from Observations (LfO) is a practical reinforcement learning scenario from which many applications can benefit. We propose a sample-efficient LfO approach that enables off-policy optimization in a principled manner. Our approach is comparable with state-of-the-art locomotion in terms of both sample-efficiency and performance.
  arXiv  Detail & Related papers  (2021-02-25T21:33:47Z)

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.