RobotDiffuse: Diffusion-Based Motion Planning for Redundant Manipulators with the ROP Obstacle Avoidance Dataset

TL;DR

RobotDiffuse introduces a diffusion model-based motion planning method for redundant manipulators, leveraging a new dataset and transformer architecture to improve planning accuracy and smoothness in complex environments.

Contribution

The paper presents a novel diffusion model approach for motion planning in redundant robots, integrating physical constraints and a transformer encoder, and provides a large obstacle avoidance dataset.

Findings

RobotDiffuse outperforms traditional methods in complex simulations.

The new dataset enables extensive testing of motion planning algorithms.

Diffusion models show promise for stable and coherent robot motion planning.

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, Robot-obtalcles-panda (ROP), with 35M robot poses and 0.14M obstacle avoidance scenarios. The highest overall score obtained in the experiment demonstrates the effectiveness of RobotDiffuse and the promise of diffusion models for motion planning tasks. The dataset can be accessed at https://github.com/ACRoboT-buaa/RobotDiffuse.