Reinforcement learning achieves safer obstacle avoidance with fixed safety margins
Positive Safety Margins from Artificial Potential Field Reward Augmentation for Size-Robust Obstacle Avoidance under Partial Observability
Robotics
Summary
Robots that learn to avoid obstacles often fail when obstacles are bigger or in different positions than they have seen before. The authors studied whether adding a simple safety reminder to the robot’s reward, without telling it exact obstacle details, can help. Their method taught a robot arm to keep a safe distance from obstacles even when it didn’t fully know their size or location. This made the robot much less likely to bump into things and perform well even outside its training scenarios.
What this means in practice
- •For robotics engineers: Enhance robot arm controllers to maintain a fixed safety distance around unknown-sized obstacles in cluttered environments using reward augmentation.
- •For industrial automation teams: Implement safer object reaching tasks in partially known factory settings by training policies that avoid collisions with imperfect obstacle data.
Authors
Ziyi Hao
Abstract
Collision-free reaching is central to manipulation, yet real cells often leave the size and exact position of an obstacle unobserved, which excludes reinforcement learning controllers that take obstacle geometry as input. A reward limited to a collision penalty does not depend on the clearance outside the obstacle, so under a step cost its learned policy skims the obstacle surface and fails once the obstacle radius exceeds its training value. We investigate whether a single analytic reward term can induce a safety margin without exposing the obstacle to the policy. We augment the reward of a simulated Franka Emika Panda reaching task with a horizontal artificial potential field that penalizes proximity within a safety margin around each of two pillars (fixed nominal layout, random drift). Under hidden observation, policies trained with this term maintain a safe success rate above 88% across a radius sweep from 0.03 m to 0.055 m (30 seeds), while policies without it fall below 10% at the maximum tested radius; the term induces a minimum clearance of 47.9 mm against 9.6 mm without it and eliminates training collisions at least eight times earlier (median steps). The advantage is statistically significant at all out-of-distribution radii and holds across three reinforcement learning solvers and under full observation. Its safe success rate is comparable to that of a classical potential-field planner given the pillar positions. The margin is a fixed buffer rather than an adaptation to the radius, and it lengthens the path by about 19%.