Learning Collision-Free Object Goal Pushing for Quadruped Robots with Safe Corridors

Abstract

While recent advancements in reinforcement learning have enabled quadrupedal robots to perform non-prehensile manipulation tasks like pushing, existing methods have largely overlooked the critical challenge of obstacle avoidance. In this paper, we address this significant limitation by introducing a novel reinforcement learning (RL) framework that controls a quadrupedal robot to push large objects in cluttered, real-world environments. In particular, obstacle avoidance is integrated as a primary objective directly into the policy training process. To achieve this, we propose to represent the traversable space with a low-dimensional safe corridor, a method that is both computationally efficient and highly effective. This approach avoids the need for complex and resource-intensive training pipelines typically required for processing high-dimensional sensor data. We validate our policy through extensive experiments in both simulation and the real world. The implementation code will be released to benefit the research community.