LANCAR: Leveraging Language for Context-Aware Robot Locomotion in Unstructured Environments

TL;DR

The work tackles context-aware navigation for robots in unstructured environments under language ambiguity by formulating the task as an implicit POMDP and introducing LANCAR, which uses an LLM-based context translator to convert human observations into context embeddings that augment RL inputs. An ARS-based RL agent, evaluated against standard baselines on a Spot-mini quadrotor simulator, shows robust generalization and gains in episodic reward (e.g., at least $7.4\%$ over strong baselines) across diverse terrains. Key contributions include the LLM-driven context translator, an in-context learning prompt design, and extensive ablations demonstrating the benefits of embedding-based context over indexing approaches, with ARS providing the strongest backbone performance. The approach promises practical impact by enabling humans to guide robotic locomotion through natural language and facilitating extension to multimodal sensing and vision-language frameworks for real-world unstructured navigation.

Abstract

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/

Figures (4)

• Figure 1: Task Description. We consider two robot learning approaches for locomotion guided by ambiguous human descriptions. The first existing approach (TOP) is when the robot moves over diverse terrains with a trained policy without any context information. Given the complexity of the terrains, robots may face difficulties in developing a generalized policy to address all types of terrains, leading to the failure of its ultimate policy. Our proposed approach (BOTTOM) has the robot moving over diverse terrains with our trained policy and context information from human observers. Robots convert this interpreted context information into embeddings with LLM. With the extra context information added to robots' own perceptions from their sensors, robots could develop better policies with a better understanding of the environment.
• Figure 2: Context-Aware Reinforcement Learning Robot Locomotion. Our framework adds a context translator to the RL setup, enhancing navigation across varied terrains. Agents receive direct observations from the environment, while human observers provide context information, interpreting terrain properties into natural language. The LLM translator processes this into context embeddings, merged with direct observations for RL agent input. The agents then apply their control policies to these enriched inputs to determine and perform actions in the environment.
• Figure 3: An Example Prompt for LANCAR. The prompt for LANCAR consists of five sections. The first section outlines the high-level task for the LLM. The second provides details and examples of relevant terrain properties. The third includes in-context learning examples, featuring low-level terrain contexts with outputs derived from multiple-choice question-answering. The final two sections involve presenting inputs to the LLM to generate context embeddings and the corresponding outputs.
• Figure 4: Episodic Reward Curve for LANCAR (Indexing) and LANCAR (Embedding) with Different Backbones. All results are run over $2$ million time steps while each episode has $5000$ time steps in maximum. Conclusion. Both LANCAR (Indexing) and LANCAR (Embedding) have the highest episodic reward when using ARS (blue) backbone than using other backbone approaches.