Learning Decentralized Multi-Biped Control for Payload Transport
This addresses the challenge of adaptable and effective payload transport in rough terrains for robotics applications, representing a novel advancement rather than an incremental improvement.
The paper tackles the problem of payload transport over rough terrain using multi-biped robot carriers, achieving a scalable decentralized controller that works without retraining for varying numbers and configurations, as demonstrated with real-world systems of two and three Cassie robots.
Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly effective, and configurable. In this paper, our goal is to provide similar effectiveness and configurability for transport over rough terrain that is more suitable for legs rather than wheels. For this purpose, we consider multi-biped robot carriers, where wheels are replaced by multiple bipedal robots attached to the carrier. Our main contribution is to design a decentralized controller for such systems that can be effectively applied to varying numbers and configurations of rigidly attached bipedal robots without retraining. We present a reinforcement learning approach for training the controller in simulation that supports transfer to the real world. Our experiments in simulation provide quantitative metrics showing the effectiveness of the approach over a wide variety of simulated transport scenarios. In addition, we demonstrate the controller in the real-world for systems composed of two and three Cassie robots. To our knowledge, this is the first example of a scalable multi-biped payload transport system.