LANCAR: 言語を活用した非構造環境における文脈認識型ロボット移動
LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments
人間の観察者からの言語による文脈情報を大規模言語モデルで解釈し、強化学習エージェントと組み合わせることで、非構造地形でのロボット移動を文脈認識型に改善した研究。
著者: Chak Lam Shek, Xiyang Wu, Wesley A. Suttle, Carl Busart, Erin Zaroukian, Dinesh Manocha, Pratap Tokekar, Amrit Singh Bedi
分類: cs.RO
原文アブストラクト
Navigating robots through unstructured terrains is challenging, primarily due to the dynamic environmental changes. While humans adeptly navigate such terrains by using context from their observations, creating a similar context-aware navigation system for robots is difficult. The essence of the issue lies in the acquisition and interpretation of context information, a task complicated by the inherent ambiguity of human language. In this work, we introduce LANCAR, which addresses this issue by combining a context translator with reinforcement learning (RL) agents for context-aware locomotion. LANCAR allows robots to comprehend context information through Large Language Models (LLMs) sourced from human observers and convert this information into actionable context embeddings. These embeddings, combined with the robot's sensor data, provide a complete input for the RL agent's policy network. We provide an extensive evaluation of LANCAR under different levels of context ambiguity and compare with alternative methods. The experimental results showcase the superior generalizability and adaptability across different terrains. Notably, LANCAR shows at least a 7.4% increase in episodic reward over the best alternatives, highlighting its potential to enhance robotic navigation in unstructured environments. More details and experiment videos could be found in http://raaslab.org/projects/LLM_Context_Estimation/
