Heterogeneous Skill Learning for Asynchronous Multi-Robot Relay Pushing in Complex Environments

Abstract

This paper presents a heterogeneous skill learning framework for asynchronous multi robot relay pushing in complex and cluttered environments. To support cooperative relay transportation, we construct a skill library comprising room robot pushing, corridor helper pushing, and standby behaviors. We further propose a geometry aware pushing strategy that enables contact rich manipulation without relying on external force sensors. For the room robot, curriculum learning is adopted to decompose training into an approach to parcel phase and a parcel to target pushing phase, thereby improving training stability and task progression. For long horizon transportation in constrained corridors, an affordance network is introduced to model the local feasibility of pushing actions, providing structured guidance that improves policy learning efficiency. The overall framework combines Soft Actor Critic (SAC) with Dijkstra based reachability maps to coordinate the ``Room Robot Pushing'' and ``Corridor Helper Pushing'' skills. Experimental results demonstrate high success rates across progressive curriculum lessons, suggesting that the proposed framework provides an effective skill primitive for cooperative multi robot transportation.