发表机构
Tübingen university（图宾根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究深度强化学习在机器人手臂到达-回避任务中的应用，利用MuJoCo MJX物理引擎和Brax库构建基准，展示多种任务设置，取得最优结果，发现此前深度强化学习在现实场景中性能不佳，强调解决该任务仍需更多研究。
AI 中文摘要
深度强化学习在解决到达-回避任务问题上有着悠久传统，尤其用于控制机器人手臂。虽然该任务是研究社区的基线环境，但深度强化学习在复杂现实场景中有效学习到达-回避任务的能力仍不确定。本文首次提出了一个全面的到达-回避任务基准，能准确捕捉现实世界复杂性而不简化。通过利用MuJoCo MJX物理引擎并使用Brax库并行化仿真环境和深度强化学习算法，展示了多种机器人手臂到达-回避任务设置用于评估深度强化学习研究。在到达任务中，UR5e成功率达96.1%，Franka Emika Robot达98.8%；在静态到达-回避任务中，UR5e为86.8%，Franka为95.2%，取得了最优结果。结果表明之前深度强化学习智能体在简化设置中能完美解决到达任务，但在现实场景中性能会崩溃。总体而言，这项工作表明仍需更多研究来用深度强化学习成功解决机器人手臂到达-回避任务。环境和基准测试代码可在链接开源获取。
英文摘要
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
