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.
@article{arxiv.2409.16566,
title = {PANOS: Payload-Aware Navigation in Offroad Scenarios},
author = {Kartikeya Singh and Yash Turkar and Christo Aluckal and Charuvarahan Adhivarahan and Karthik Dantu},
journal= {arXiv preprint arXiv:2409.16566},
year = {2024}
}