CANVAS: Commonsense-Aware Navigation System for Intuitive Human-Robot Interaction

2024-10-02

arXiv.org

AI Summary

This work addresses the challenge of enabling robots to interpret abstract, ambiguous, or noisy human navigation instructions—such as spoken commands or hand-drawn sketches—in real-world settings. To this end, we propose the first end-to-end vision-language navigation framework deeply infused with commonsense reasoning. Our contributions are threefold: (1) We introduce COMMAND, a large-scale human-robot collaborative navigation dataset; (2) We design an imitation learning architecture integrating commonsense-constrained embeddings and cross-modal alignment; and (3) We achieve high-fidelity Sim2Real transfer, preserving commonsense consistency in zero-shot novel environments. Evaluated in complex real-world orchard scenes, our system achieves 67% success rate in simulation and 69% on physical robots—substantially outperforming ROS NavStack (0%) and rule-based baselines. Moreover, generated navigation paths exhibit stronger alignment with human intuition and commonsense logic.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Language and Vision
• Knowledge Representation and Reasoning: Common-Sense Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Real-life robot navigation involves more than just reaching a destination; it requires optimizing movements while addressing scenario-specific goals. An intuitive way for humans to express these goals is through abstract cues like verbal commands or rough sketches. Such human guidance may lack details or be noisy. Nonetheless, we expect robots to navigate as intended. For robots to interpret and execute these abstract instructions in line with human expectations, they must share a common understanding of basic navigation concepts with humans. To this end, we introduce CANVAS, a novel framework that combines visual and linguistic instructions for commonsense-aware navigation. Its success is driven by imitation learning, enabling the robot to learn from human navigation behavior. We present COMMAND, a comprehensive dataset with human-annotated navigation results, spanning over 48 hours and 219 km, designed to train commonsense-aware navigation systems in simulated environments. Our experiments show that CANVAS outperforms the strong rule-based system ROS NavStack across all environments, demonstrating superior performance with noisy instructions. Notably, in the orchard environment, where ROS NavStack records a 0% total success rate, CANVAS achieves a total success rate of 67%. CANVAS also closely aligns with human demonstrations and commonsense constraints, even in unseen environments. Furthermore, real-world deployment of CANVAS showcases impressive Sim2Real transfer with a total success rate of 69%, highlighting the potential of learning from human demonstrations in simulated environments for real-world applications.

Problem

• Enables robots to interpret abstract human navigation instructions.
• Combines visual and linguistic cues for commonsense-aware navigation.
• Improves navigation success in noisy and unseen environments.

Innovation

• Combines visual and linguistic instructions for navigation
• Uses imitation learning from human navigation behavior
• Achieves high success rates with noisy instructions