Potential Based Diffusion Motion Planning (2407.06169v1)

Abstract: Effective motion planning in high dimensional spaces is a long-standing open problem in robotics. One class of traditional motion planning algorithms corresponds to potential-based motion planning. An advantage of potential based motion planning is composability -- different motion constraints can be easily combined by adding corresponding potentials. However, constructing motion paths from potentials requires solving a global optimization across configuration space potential landscape, which is often prone to local minima. We propose a new approach towards learning potential based motion planning, where we train a neural network to capture and learn an easily optimizable potentials over motion planning trajectories. We illustrate the effectiveness of such approach, significantly outperforming both classical and recent learned motion planning approaches and avoiding issues with local minima. We further illustrate its inherent composability, enabling us to generalize to a multitude of different motion constraints.

• The paper introduces a neural network approach that learns optimized potentials to improve motion planning in high-dimensional spaces.
• It demonstrates significant performance gains in avoiding local minima compared to classical and recent planning methods.
• The method’s composability allows seamless integration of various motion constraints for versatile robotic applications.

The paper "Potential Based Diffusion Motion Planning" addresses a significant challenge in robotics: effective motion planning in high-dimensional spaces. Traditional potential-based motion planning methods offer the advantage of composability, allowing various motion constraints to be integrated by adding corresponding potentials. However, a major hurdle in these methods is the need for global optimization across the configuration space, which often encounters issues such as local minima.

This research introduces a novel approach that leverages neural networks to learn and capture potentials that are more easily optimized for motion planning trajectories. This approach addresses the common pitfalls of local minima by training a neural network to create a landscape of potentials where optimization can be performed more effectively.

By outperforming both classical and recent learned motion planning strategies, the proposed method demonstrates a significant improvement in avoiding local minima. Additionally, the paper highlights the method's intrinsic composability, which allows it to generalize across various motion constraints seamlessly.

Overall, this research contributes a promising solution to long-standing problems in motion planning, particularly in high-dimensional settings, by integrating learning-based methods with traditional potential-based techniques.