Positive Safety Margins from Artificial Potential Field Reward Augmentation for Size-Robust Obstacle Avoidance under Partial Observability
Positive Safety Margins from Artificial Potential Field Reward Augmentation for Size-Robust Obstacle Avoidance under Partial Observability
This research addresses the challenge of collision-free reaching when obstacles are only partially observed, specifically regarding their size and exact position.
The study demonstrates that augmenting reinforcement learning rewards with an artificial potential field term significantly improves obstacle avoidance robustness under partial observability, achieving a safe success rate above 88% across varying obstacle radii compared to less than 10% for baseline policies.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
- Environment
- SimulationOpen source S3
Reported numbers
Reported success rate
88%
Reported trials
View original evidence
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 si
Open source S4minimum clearance
47.9
View original evidence
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 si
Open source S4minimum clearance without term
9.6
View original evidence
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 si
Open source S4training collisions eliminated earlier (median steps)
≥8
Reported trials
View original evidence
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 si
Open source S4radius sweep lower bound
0.03–0.055
View original evidence
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 si
Open source S4radius sweep upper bound
0.03–0.055
View original evidence
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 si
Open source S4seeds
30
Reported trials
View original evidence
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 si
Open source S4
- paper: not_reported
What remains unknown
Not established in the collected evidence: Control, Data origin.
Reported performance applies to the described task. It does not establish general autonomy or deployment readiness.
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
cle 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
Open source S3
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 si
Open source S4
gnificant 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%.
Open source S5
Source：arXiv Robotics — research abstracts · arxiv.org