Locomotion on unknown terrains is essential for bipedal robots to handle novel real-world challenges, thus expanding their utility in disaster response and exploration. In this work, we introduce a lightweight framework that learns a single walking controller that yields locomotion on multiple terrains. We have designed a real-time robot controller based on diffusion models, which not only captures multiple behaviours with different velocities in a single policy but also generalizes well for unseen terrains. Our controller learns with offline data, which is better than online learning in aspects like scalability, simplicity in training scheme etc. We have designed and implemented a diffusion model-based policy controller in simulation on our custom-made Bipedal Robot model named Stoch BiRo. We have demonstrated its generalization capability and high frequency control step generation relative to typical generative models, which require huge onboarding compute.

在未知地形上行走是为了使双足机器人能够处理新型现实世界挑战、并扩展其在灾难应对和探索中的实用性而必不可少的。本文介绍了一个轻量级框架，学习单一行走控制器在多种地形上实现行走。我们设计了一个基于扩散模型的实时机器人控制器，不仅能在单一策略中捕捉不同速度的多种行为，而且对未知地形具有良好的泛化能力。与在线学习相比，我们的控制器通过离线数据进行学习，在可扩展性和训练方案的简单性等方面更加优越。我们在仿真中使用基于扩散模型的策略控制器在我们自己制造的双足机器人模型Stoch BiRo上进行了设计与实现，展示了它在泛化能力和高频率控制步骤生成方面相对于典型生成模型的优势，后者需要庞大的计算资源。

BiRoDiff：用于未知地形下双足机器人行走的扩散策略