NaVILA: Legged Robot Vision-Language-Action Model for Navigation
On the map: Vision-language-action models → Generalist VLA models and benchmarks
Abstract (arXiv)
This paper proposes to solve the problem of Vision-and-Language Navigation with legged robots, which not only provides a flexible way for humans to command but also allows the robot to navigate through more challenging and cluttered scenes. However, it is non-trivial to translate human language instructions all the way to low-level leg joint actions. We propose NaVILA, a 2-level framework that unifies a Vision-Language-Action model (VLA) with locomotion skills. Instead of directly predicting low-level actions from VLA, NaVILA first generates mid-level actions with spatial information in the form of language, (e.g., "moving forward 75cm"), which serves as an input for a visual locomotion RL policy for execution. NaVILA substantially improves previous approaches on existing benchmarks. The same advantages are demonstrated in our newly developed benchmarks with IsaacLab, featuring more realistic scenes, low-level controls, and real-world robot experiments. We show more results at https://navila-bot.github.io/
Key facts. Cited by 211 papers in the map (data through 14 Oct 2026). Placed in Generalist VLA models and benchmarks, one of 13 directions in Vision-language-action models. Builds on 96 of its 110 references that are in the map. First posted on arXiv 5 Dec 2024.
Builds on
96 of 110 references.
Closest ancestors · 3
Same direction, newest first
- 2024OpenVLA: An Open-Source Vision-Language-Action Model13 Jun
- 2023QUAR-VLA: Vision-Language-Action Model for Quadruped Robots22 Dec
- 2023RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control28 Jul
Other references · 93
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- 2024Grounding Image Matching in 3D with MASt3Rcited by 983
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- 2024SpatialVLM: Endowing Vision-Language Models with Spatial Reasoning Capabilitiescited by 706
- 2023VILA: On Pre-training for Visual Language Modelscited by 692
- 2020Learning Quadrupedal Locomotion over Challenging Terraincited by 604
- 2017End-to-end Driving via Conditional Imitation Learningcited by 485
- 2020Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environmentscited by 441
- 2020Object Goal Navigation using Goal-Oriented Semantic Explorationcited by 429
- 2020Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Groundingcited by 414
- 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
- 2023An Embodied Generalist Agent in 3D Worldcited by 365
- 2021ScanQA: 3D Question Answering for Spatial Scene Understandingcited by 346
- 2018Speaker-Follower Models for Vision-and-Language Navigationcited by 340
- 2019Deep Modular Co-Attention Networks for Visual Question Answeringcited by 339
- 2019REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environmentscited by 333
- 2020Learning to Explore using Active Neural SLAMcited by 316
- 2024SpatialRGPT: Grounded Spatial Reasoning in Vision Language Modelscited by 311
- 2024LongVILA: Scaling Long-Context Visual Language Models for Long Videoscited by 309
- 2018Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigationcited by 305
- 2023Orbit: A Unified Simulation Framework for Interactive Robot Learning Environmentscited by 295
- 2024VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generationcited by 295
- 2021History Aware Multimodal Transformer for Vision-and-Language Navigationcited by 271
- 2023NoMaD: Goal Masked Diffusion Policies for Navigation and Explorationcited by 248
- 2019Learning to Navigate Unseen Environments: Back Translation with Environmental Dropoutcited by 244
- 2024NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigationcited by 233
- 2023LL3DA: Visual Interactive Instruction Tuning for Omni-3D Understanding, Reasoning, and Planningcited by 210
- 2023Multimodal C4: An Open, Billion-scale Corpus of Images Interleaved with Textcited by 208
- 20233D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignmentcited by 204
- 2020Neural Topological SLAM for Visual Navigationcited by 195
- 2020Improving Vision-and-Language Navigation with Image-Text Pairs from the Webcited by 177
- 2019Self-Monitoring Navigation Agent via Auxiliary Progress Estimationcited by 176
- 2023Chat-Scene: Bridging 3D Scene and Large Language Models with Object Identifierscited by 175
- 2024Scene-LLM: Extending Language Model for 3D Visual Understanding and Reasoningcited by 175
- 2022GNM: A General Navigation Model to Drive Any Robotcited by 171
- 2023ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environmentscited by 169
- 2022Rapid Locomotion via Reinforcement Learningcited by 168
- 2023Towards Learning a Generalist Model for Embodied Navigationcited by 138
- 2022Bridging the Gap Between Learning in Discrete and Continuous Environments for Vision-and-Language Navigationcited by 132
- 2021Airbert: In-domain Pretraining for Vision-and-Language Navigationcited by 130
50 most cited of 93 All 93 (JSON)
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Citing papers per year: 2024: 1; 2025: 86; 2026: 124.
211 papers.
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50 most cited of 211 All 211 (JSON)
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50 newest of 211 All 211 (JSON)
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