BUMBLE: Unifying Reasoning and Acting with Vision-Language Models for Building-wide Mobile Manipulation

摘要

To operate at a building scale, service robots must perform long-horizon mobile manipulation tasks by navigating to different rooms, accessing multiple floors, and interacting with a wide and unseen range of everyday objects. We refer to these tasks as Building-wide Mobile Manipulation. To tackle these inherently long-horizon tasks, we introduce BUMBLE, a unified Vision-Language Model (VLM)-based framework integrating open-world RGB-D perception, a wide spectrum of gross-to-fine motor skills, and dual-layered memory. Our extensive evaluation (90+ hours) indicates that BUMBLE outperforms competitive baselines in long-horizon building-wide tasks that require sequencing up to 12 skills, spanning 15 minutes per trial. BUMBLE achieves 47.1% success rate averaged over 70 trials in different buildings, tasks, and scene layouts from various starting locations. Our user study shows 22% higher task satisfaction using our framework compared to state-of-the-art VLM-based mobile manipulation methods. Finally, we show the potential of using increasingly capable foundation models to improve the system performance further. For more information, see https://robin-lab.cs.utexas.edu/BUMBLE/