AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models
This addresses the problem of high human effort in robot policy development for researchers and practitioners, though it appears incremental as it builds on existing RL and sim-to-real techniques.
The paper tackles the challenge of reducing human intervention in training and deploying reinforcement learning policies for bipedal robots by introducing an end-to-end framework guided by Large Language Models, which autonomously develops and refines controlling strategies for locomotion.
Training and deploying reinforcement learning (RL) policies for robots, especially in accomplishing specific tasks, presents substantial challenges. Recent advancements have explored diverse reward function designs, training techniques, simulation-to-reality (sim-to-real) transfers, and performance analysis methodologies, yet these still require significant human intervention. This paper introduces an end-to-end framework for training and deploying RL policies, guided by Large Language Models (LLMs), and evaluates its effectiveness on bipedal robots. The framework consists of three interconnected modules: an LLM-guided reward function design module, an RL training module leveraging prior work, and a sim-to-real homomorphic evaluation module. This design significantly reduces the need for human input by utilizing only essential simulation and deployment platforms, with the option to incorporate human-engineered strategies and historical data. We detail the construction of these modules, their advantages over traditional approaches, and demonstrate the framework's capability to autonomously develop and refine controlling strategies for bipedal robot locomotion, showcasing its potential to operate independently of human intervention.