RobotDiffuse: Motion Planning for Redundant Manipulator based on Diffusion Model
This addresses motion planning challenges for redundant manipulators in applications like manufacturing and surgical robotics, representing an incremental improvement over existing deep learning methods.
The paper tackled motion planning for redundant manipulators in complex environments by proposing RobotDiffuse, a diffusion model-based approach that integrates physical constraints and uses a transformer encoder, resulting in improved generation of smoother and more coherent motion plans as validated in simulations.
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.