实现机器人操纵政策的在线安全修正

Ariana Spalter, Mark Roberts, Laura M. Hiatt
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引用次数: 0

摘要

最近,强化学习(RL)在机器人技术中的成功应用表明,它是构建机器人控制器的一种可行方法。然而,在执行过程中出现新障碍物的环境中,RL 控制器可能会产生许多碰撞。这给安全关键环境带来了问题。我们提出了一种名为 iKinQP-RL 的混合方法,它使用逆运动学二次编程(iKinQP)控制器来纠正运行时由 RL 策略提出的动作。这确保了在出现训练期间未出现的新障碍时的安全执行。初步实验表明,我们的 iKinQP-RL 框架完全消除了与新障碍物的碰撞,同时保持了较高的任务成功率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards Online Safety Corrections for Robotic Manipulation Policies
Recent successes in applying reinforcement learning (RL) for robotics has shown it is a viable approach for constructing robotic controllers. However, RL controllers can produce many collisions in environments where new obstacles appear during execution. This poses a problem in safety-critical settings. We present a hybrid approach, called iKinQP-RL, that uses an Inverse Kinematics Quadratic Programming (iKinQP) controller to correct actions proposed by an RL policy at runtime. This ensures safe execution in the presence of new obstacles not present during training. Preliminary experiments illustrate our iKinQP-RL framework completely eliminates collisions with new obstacles while maintaining a high task success rate.
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