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

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arxiv 2410.06237 v1 pith:KLB32CVU submitted 2024-10-08 cs.RO cs.AI

Rutav Shah , Albert Yu , Yifeng Zhu , Yuke Zhu , Roberto Martín-Martín This is my paper · ORCID
classification cs.ROcs.AI
keywords bumbletasksdifferentmanipulationmobilebuilding-widelong-horizonfloors
verification ladder T0 review T1 audit T2 compute T3 formal
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To operate at a building scale, service robots must perform very long-horizon mobile manipulation tasks by navigating to different rooms, accessing different 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 RGBD perception, a wide spectrum of gross-to-fine motor skills, and dual-layered memory. Our extensive evaluation (90+ hours) indicates that BUMBLE outperforms multiple baselines in long-horizon building-wide tasks that require sequencing up to 12 ground truth skills spanning 15 minutes per trial. BUMBLE achieves 47.1% success rate averaged over 70 trials in different buildings, tasks, and scene layouts from different starting rooms and floors. Our user study demonstrates 22% higher satisfaction with our method than state-of-the-art mobile manipulation methods. Finally, we demonstrate the potential of using increasingly-capable foundation models to push performance further. For more information, see https://robin-lab.cs.utexas.edu/BUMBLE/

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Forward citations

Cited by 5 Pith papers

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    Fail2Progress generates failure-targeted simulation data via Stein variational inference and fine-tunes skill effect models, improving long-horizon manipulation success rates and generalizing to unseen object counts a...

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  3. Robix: A Unified Model for Robot Interaction, Reasoning and Planning

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    A three-stage-trained VLM unifies robot planning and dialogue, and beats commercial VLMs on the authors' interactive-task benchmarks.

  4. Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models

    cs.RO 2025-06 conditional novelty 6.0 of 10

    A VLM-powered assistive teleoperation system infers diverse user intents from teleoperation snippets and executes them with a skill library, outperforming baselines on real-world mobile manipulation tasks.

  5. GraphPad: Inference-Time 3D Scene Graph Updates for Embodied Question Answering

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    Allowing a vision-language model to edit its own 3D scene graph during inference improves embodied question answering from 52.3% to 55.3% on OpenEQA.

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