Learning Reach-Avoid Task with Reinforcement Learning: Vectorized Simulation and Benchmark
Deep reinforcement learning (DRL) has a longstanding tradition in addressing the reach-avoid task problem, especially for controlling robotic arms. While this task serves as a baseline environment within the research community, the ability of DRL to effectively learn the each-avoid task in complex and realistic scenarios beyond simplified and restricted tabletop settings remains uncertain. In this paper, we present, for the first time, a comprehensive benchmark for the reachavoid task that accurately captures real-world complexities without simplifications. We demonstrate a diverse range of settings for robotic arm reach-avoid task, which can be used for evaluating DRL research. We achiev...
| Authors | Jonas Weihing, Shahram Eivazi |
|---|---|
| Primary category | cs.RO |
| Published | 17 July 2026 |
| Last updated at source | 17 July 2026 |
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