Distance Field Driven Reinforcement Learning for Reactive and Robust Collision Avoidance
Robust and reactive collision avoidance remains a central challenge for deploying Reinforcement Learning (RL) policies on real robotic manipulators, particularly when transferring behaviours learned in simulation to the physical world.
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Key points
- This paper presents an RL framework that integrates the Euclidean Distance Field (EDF) directly into the observation space, providing the agent with continuous geometric cues that support stable and responsive avoidance behaviours.
- By leveraging the rich spatial information encoded in the EDF, the method enables a streamlined reward formulation that achieves effective obstacle avoidance both with and without Artificial Potential Field guidance.
- The proposed approach is validated on a 7-DoF redundant manipulator, demonstrating zero-shot transfer from simulation to real hardware.
- These results highlight the potential of EDF-based representations to mitigate the sim-to-real gap and support reliable and real-time collision-aware behaviour in complex and changing environments.
Sources (1)
- [1]Distance Field Driven Reinforcement Learning for Reactive and Robust Collision AvoidancearXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 21, 09:10 AM
Robust and reactive collision avoidance remains a central challenge for deploying Reinforcement Learning (RL) policies on real robotic manipulators, particularly when transferring behaviours learned in simulation to the physical world.
This paper presents an RL framework that integrates the Euclidean Distance Field (EDF) directly into the observation space, providing the agent with continuous geometric cues that support stable and responsive avoidance behaviours.
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