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Learning Generalizable Feature Fields for Mobile Manipulation
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An open problem in mobile manipulation is how to represent objects and scenes in a unified manner so that robots can use both for navigation and manipulation. The latter requires capturing intricate geometry while understanding fine-grained semantics, whereas the former involves capturing the complexity inherent at an expansive physical scale. In this work, we present GeFF (Generalizable Feature Fields), a scene-level generalizable neural feature field that acts as a unified representation for both navigation and manipulation that performs in real-time. To do so, we treat generative novel view synthesis as a pre-training task, and then align the resulting rich scene priors with natural language via CLIP feature distillation. We demonstrate the effectiveness of this approach by deploying GeFF on a quadrupedal robot equipped with a manipulator. We quantitatively evaluate GeFF's ability for open-vocabulary object-/part-level manipulation and show that GeFF outperforms point-based baselines in runtime and storage-accuracy trade-offs, with qualitative examples of semantics-aware navigation and articulated object manipulation.
Forward citations
Cited by 7 Pith papers
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QuASH: Using Natural-Language Heuristics to Query Visual-Language Robotic Maps
Querying VLM robot maps with an SVM trained on LLM-generated synonym/antonym embeddings outperforms cosine-threshold and single-antonym baselines on images and OpenSeg maps, but not consistently on LSeg maps.
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Learning Multi-Stage Pick-and-Place with a Legged Mobile Manipulator
A simulation-trained teacher-student policy with progressive policy expansion achieves 78.3% real-world success on a long-horizon mobile pick-and-place task.
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SIREN uses semantic features in Gaussian Splatting maps to register and fuse maps from multiple robots without camera poses, images, or an initial transform.
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OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
A vision-language model fine-tuned on 572K synthetic simulation examples improves open-world mobile manipulation action decisions and object grounding over GPT-4o, with 21.9% full-task success in simulation and 90% ac...
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MoTo: A Zero-shot Plug-in Interaction-aware Navigation for General Mobile Manipulation
MoTo turns existing fixed-base manipulation models into mobile manipulators by using VLM-picked contact keypoints and trajectory optimization to find docking points, with no training of MoTo itself.
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