首个真实复杂场景下的机器人避障任务基准，验证强化学习在现实中的局限性
Learning Reach-Avoid Task with Reinforcement Learning: Vectorized Simulation and Benchmark
- 用MuJoCo MJX和Brax实现并行化仿真，构建无简化的真实场景环境
- UR5e与Franka机械臂在静态避障任务中分别达86.8%和95.2%成功率
- 揭示现有DRL方法在真实场景中性能严重下降，需新研究突破
深度强化学习（DRL）长期用于解决机器人臂的避障任务，但其在简化桌面环境外的真实复杂场景中的有效性仍不明确。本文首次提出一个全面的避障任务基准，精确还原现实复杂性，无需简化。通过MuJoCo MJX物理引擎与Brax库并行化仿真环境及DRL算法，构建多样化机器人臂避障场景，用于评估DRL研究。实验结果显示，在可达任务中，UR5e与Franka机械臂的成功率分别为96.1%和98.8%；在静态避障任务中，对应成功率为86.8%和95.2%。结果表明，尽管以往工作在简化环境中可完美解决可达任务，但在真实场景中代理性能显著下降。本研究指出，当前DRL尚无法真正解决机器人臂避障任务。环境与基准代码已开源。
原文摘要 · Abstract (English)
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 achieved this by utilizing the MuJoCo MJX physics engine and parallelizing both the simulation environment and DRL algorithms using the Brax library. We achieved state-of-the-art results with success rates of 96.1% (UR5e) and 98.8% (Franka Emika Robot) for the reach task and 86.8% (UR5e) and 95.2% (Franka) for the static reachavoid task. Our results indicate that while in previous works DRL agents could solve, for example, a reach task in a simplified setting perfectly, their agents performance collapses when evaluated in realistic scenarios. Overall, this work identifies that additional research is still required to claim the successful resolution of the robotic arm reach-avoid task using DRL. The environment and benchmarking code is available as open source at the following link
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