PANOS: ペイロードを考慮したオフロードナビゲーション
PANOS: Payload-Aware Navigation in Offroad Scenarios
脚式ロボットが様々な地形や積載重量に適応するため、固有感覚と外界感覚を統合した弱教師あり学習手法PANOSを提案し、安定性と振動コストを改善した。
著者: Kartikeya Singh, Yash Turkar, Christo Aluckal, Charuvarahan Adhivarahan, Karthik Dantu
分類: cs.RO
原文アブストラクト
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.
