Neural Configuration-Space Barriers for Manipulation Planning and Control

• URL: http://arxiv.org/abs/2503.04929v2
• Date: Tue, 06 May 2025 04:11:29 GMT
• Title: Neural Configuration-Space Barriers for Manipulation Planning and Control
• Abstract summary: Planning and control for high-dimensional robot manipulators in cluttered, dynamic environments require both computational efficiency and robust safety guarantees.<n>We propose a unified framework for motion planning and control that formulates safety constraints as CDF barriers.<n>A CDF barrier approximates the local free configuration space, substantially reducing the number of collision-checking operations during motion planning.
• Score: 14.758869638207251
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Planning and control for high-dimensional robot manipulators in cluttered, dynamic environments require both computational efficiency and robust safety guarantees. Inspired by recent advances in learning configuration-space distance functions (CDFs) as robot body representations, we propose a unified framework for motion planning and control that formulates safety constraints as CDF barriers. A CDF barrier approximates the local free configuration space, substantially reducing the number of collision-checking operations during motion planning. However, learning a CDF barrier with a neural network and relying on online sensor observations introduce uncertainties that must be considered during control synthesis. To address this, we develop a distributionally robust CDF barrier formulation for control that explicitly accounts for modeling errors and sensor noise without assuming a known underlying distribution. Simulations and hardware experiments on a 6-DoF xArm manipulator show that our neural CDF barrier formulation enables efficient planning and robust real-time safe control in cluttered and dynamic environments, relying only on onboard point-cloud observations.

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