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.

翻译：在未知地形上行走对于双足机器人应对现实世界中的新挑战至关重要，从而扩展其在灾害响应和探索任务中的实用性。本研究提出了一种轻量级框架，该框架学习一个单一的步行控制器，即可实现在多种地形上的运动。我们设计了一种基于扩散模型的实时机器人控制器，该控制器不仅能在单一策略中捕捉不同速度的多种行为，还能对未见地形展现出良好的泛化能力。我们的控制器通过离线数据进行学习，相较于在线学习，在可扩展性、训练方案简洁性等方面更具优势。我们在仿真环境中，基于我们定制的名为Stoch BiRo的双足机器人模型，设计并实现了一种基于扩散模型的策略控制器。实验表明，相对于需要大量计算资源的典型生成模型，我们的控制器具有优异的泛化能力，并能生成高频控制指令。