用在线修正机制让机器人在新障碍物下安全操作
Towards Online Safety Corrections for Robotic Manipulation Policies
- 运行时用逆运动学优化纠正强化学习动作
- 实验中对新障碍物零碰撞，任务成功率高
- 适合需要实时安全控制的工业机器人场景
近期强化学习（RL）在机器人控制中的成功表明其是构建机器人控制器的可行方法。然而，RL控制器在执行过程中遇到新出现的障碍物时会产生大量碰撞，这在安全关键场景中构成问题。本文提出一种混合方法 iKinQP-RL，利用逆运动学二次规划（iKinQP）控制器在运行时修正由RL策略提出的动作，从而确保在训练时未见过的新障碍物存在情况下仍能安全执行。初步实验表明，该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。
原文摘要 · Abstract (English)
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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