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OpenFMNav: Towards Open-Set Zero-Shot Object Navigation via Vision-Language Foundation Models
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Object navigation (ObjectNav) requires an agent to navigate through unseen environments to find queried objects. Many previous methods attempted to solve this task by relying on supervised or reinforcement learning, where they are trained on limited household datasets with close-set objects. However, two key challenges are unsolved: understanding free-form natural language instructions that demand open-set objects, and generalizing to new environments in a zero-shot manner. Aiming to solve the two challenges, in this paper, we propose OpenFMNav, an Open-set Foundation Model based framework for zero-shot object Navigation. We first unleash the reasoning abilities of large language models (LLMs) to extract proposed objects from natural language instructions that meet the user's demand. We then leverage the generalizability of large vision language models (VLMs) to actively discover and detect candidate objects from the scene, building a Versatile Semantic Score Map (VSSM). Then, by conducting common sense reasoning on VSSM, our method can perform effective language-guided exploration and exploitation of the scene and finally reach the goal. By leveraging the reasoning and generalizing abilities of foundation models, our method can understand free-form human instructions and perform effective open-set zero-shot navigation in diverse environments. Extensive experiments on the HM3D ObjectNav benchmark show that our method surpasses all the strong baselines on all metrics, proving our method's effectiveness. Furthermore, we perform real robot demonstrations to validate our method's open-set-ness and generalizability to real-world environments.
Forward citations
Cited by 6 Pith papers
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VTM-Nav: Harnessing Cross-Episode Experience for Object-Goal Navigation with Hierarchical Visual-Topological Memory
A hierarchical room-and-object memory that persists across independent ObjectNav episodes yields small success-rate gains, but most of the gain comes from within-episode memory rather than the cross-episode component.
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DRIVE-Nav: Directional Reasoning, Inspection, and Verification for Efficient Open-Vocabulary Navigation
Organizing zero-shot object navigation around tracked directional exits with 240° inspection and VLM verification yields 50.2% SR / 32.6% SPL on HM3D-OVON and best SPL on HM3Dv2 and MP3D.
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OpenGuide: Assistive Object Retrieval in Indoor Spaces for Individuals with Visual Impairments
OpenGuide combines vision-language value maps, frontier exploration, and POMDP planning to locate multiple objects in unfamiliar indoor spaces, reaching about 55% success in simulation and 54% in real-world trials.
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Imaginative World Modeling with Scene Graphs for Embodied Agent Navigation
SGImagineNav uses an imagined hierarchical scene graph, filled in by an LLM, that guides a robot to unseen objects and achieves 65.4% and 66.8% success on HM3D and HSSD.
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SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending
SkillBlender pretrains reusable goal-conditioned skills and blends them with softmax per-joint weights to solve simulated humanoid loco-manipulation tasks with one or two reward terms.
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N2M: Bridging Navigation and Manipulation by Learning Pose Preference from Rollout
N2M predicts preferable base poses for manipulation policies from ego-centric point clouds, learned from rollouts, lifting success from 3% to 54% in the PnPCounterToCab task.
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