Towards Online Safety Corrections for Robotic Manipulation Policies

TL;DR

This paper introduces iKinQP-RL, a hybrid method combining inverse kinematics quadratic programming with reinforcement learning to ensure robotic safety by preventing collisions with new obstacles during task execution.

Contribution

The paper proposes a novel hybrid approach that corrects RL-predicted actions in real-time using iKinQP, enhancing safety in robotic manipulation tasks.

Findings

Eliminates collisions with new obstacles during execution.

Maintains high task success rate.

Ensures safe operation in dynamic environments.

Abstract

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.