PANOS: Payload-Aware Navigation in Offroad Scenarios

TL;DR

PANOS is a weakly supervised method enabling legged robots to adapt their gait for stable walking across diverse terrains and payloads, significantly improving stability and reducing vibrations compared to existing approaches.

Contribution

The paper introduces PANOS, a novel proprioception and exteroception-based approach for payload-aware terrain adaptation in legged robots, enhancing stability and vibration reduction.

Findings

Improves stability by up to 44% without payload and 53% with payload.

Reduces vibration cost by 20% across various terrains.

Demonstrates adaptability to different payloads and terrains.

Abstract

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.