Nature has evolved humans to walk on different terrains by developing a detailed understanding of their physical characteristics. Similarly, legged robots need to develop their capability to walk on complex terrains with a variety of task-dependent payloads to achieve their goals. However, conventional terrain adaptation methods are susceptible to failure with varying payloads. In this work, we introduce PANOS, a weakly supervised approach that integrates proprioception and exteroception from onboard sensing to achieve a stable gait while walking by a legged robot over various terrains. Our work also provides evidence of its adaptability over varying payloads. We evaluate our method on multiple terrains and payloads using a legged robot. PANOS improves the stability up to 44% without any payload and 53% with 15 lbs payload. We also notice a reduction in the vibration cost of 20% with the payload for various terrain types when compared to state-of-the-art methods.

翻译：自然界通过使人类深刻理解不同地形的物理特性，进化出了在各类地形上行走的能力。类似地，足式机器人需要发展其在复杂地形上携带各类任务相关载荷行走的能力，以实现既定目标。然而，传统的地形适应方法在面对变化的载荷时容易失效。本研究提出PANOS，一种弱监督方法，通过整合来自机载传感的本体感知与外感受信息，使足式机器人在多种地形行走时实现稳定步态。我们的工作还证明了该方法对不同载荷的适应能力。我们使用足式机器人在多种地形和载荷条件下评估了该方法。PANOS在无载荷情况下将稳定性提升达44%，在15磅载荷下提升达53%。与现有先进方法相比，我们还观察到在不同地形类型下，携带载荷时的振动成本降低了20%。