RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model

• URL: http://arxiv.org/abs/2412.19500v1
• Date: Fri, 27 Dec 2024 07:34:54 GMT
• Title: RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model
• Authors: Xiaohan Zhang, Xudong Mou, Rui Wang, Tianyu Wo, Ningbo Gu, Tiejun Wang, Cangbai Xu, Xudong Liu,
• Abstract summary: Redundant manipulators offer enhanced kinematic performance and versatility. Motion planning for these manipulators is challenging due to increased DOFs and complex, dynamic environments. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators.
• Score: 13.110235244912474
• License:
• Abstract: Redundant manipulators, with their higher Degrees of Freedom (DOFs), offer enhanced kinematic performance and versatility, making them suitable for applications like manufacturing, surgical robotics, and human-robot collaboration. However, motion planning for these manipulators is challenging due to increased DOFs and complex, dynamic environments. While traditional motion planning algorithms struggle with high-dimensional spaces, deep learning-based methods often face instability and inefficiency in complex tasks. This paper introduces RobotDiffuse, a diffusion model-based approach for motion planning in redundant manipulators. By integrating physical constraints with a point cloud encoder and replacing the U-Net structure with an encoder-only transformer, RobotDiffuse improves the model's ability to capture temporal dependencies and generate smoother, more coherent motion plans. We validate the approach using a complex simulator, and release a new dataset with 35M robot poses and 0.14M obstacle avoidance scenarios. Experimental results demonstrate the effectiveness of RobotDiffuse and the promise of diffusion models for motion planning tasks. The code can be accessed at https://github.com/ACRoboT-buaa/RobotDiffuse.

Related papers

• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce kinematically feasible trajectories. The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• FAST: Efficient Action Tokenization for Vision-Language-Action Models [98.15494168962563]
  We propose a new compression-based tokenization scheme for robot actions, based on the discrete cosine transform. Based on FAST, we release FAST+, a universal robot action tokenizer, trained on 1M real robot action trajectories.
  arXiv  Detail & Related papers  (2025-01-16T18:57:04Z)
• Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots [60.06216976204385]
  We present an efficient motion planning framework for simultaneously solving locomotion, grasping, and contact problems. We demonstrate our proposed framework in the hardware experiments, showing that the multi-limbed robot is able to realize various motions including free-climbing at a slope angle 45deg with a much shorter planning time.
  arXiv  Detail & Related papers  (2022-07-04T13:52:10Z)
• Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
  Planning motions that take human comfort into account is not a part of the human-robot handover process. We propose to generate smooth motions via an efficient model-predictive control framework. We conduct human-to-robot handover experiments on a diverse set of objects with several users.
  arXiv  Detail & Related papers  (2022-03-31T23:08:20Z)
• OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation [50.59541802645156]
  Operational Space Control (OSC) has been used as an effective task-space controller for manipulation. We propose OSC for Adaptation and Robustness (OSCAR), a data-driven variant of OSC that compensates for modeling errors. We evaluate our method on a variety of simulated manipulation problems, and find substantial improvements over an array of controller baselines.
  arXiv  Detail & Related papers  (2021-10-02T01:21:38Z)
• SABER: Data-Driven Motion Planner for Autonomously Navigating Heterogeneous Robots [112.2491765424719]
  We present an end-to-end online motion planning framework that uses a data-driven approach to navigate a heterogeneous robot team towards a global goal. We use model predictive control (SMPC) to calculate control inputs that satisfy robot dynamics, and consider uncertainty during obstacle avoidance with chance constraints. recurrent neural networks are used to provide a quick estimate of future state uncertainty considered in the SMPC finite-time horizon solution. A Deep Q-learning agent is employed to serve as a high-level path planner, providing the SMPC with target positions that move the robots towards a desired global goal.
  arXiv  Detail & Related papers  (2021-08-03T02:56:21Z)
• An advantage actor-critic algorithm for robotic motion planning in dense and dynamic scenarios [0.8594140167290099]
  In this paper, we modify existing advantage actor-critic algorithm and suit it to complex motion planning. It achieves higher success rate in motion planning with lesser processing time for robot to reach its goal.
  arXiv  Detail & Related papers  (2021-02-05T12:30:23Z)

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.