University of California, Berkeley team finds language-guided robot navigation works in cluttered spaces
A new model trained in simulation and tested in real-world clutter aims to navigate but may still face obstacles.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Anqi Li, Yuxin Chen, Zhaobo Li, Zhuo Cao, Junli Ren, Masayoshi Tomizuka, Dhruv Shah
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
Institution metadata: OpenAlex record ↗
What they did and found
Researchers built a whole-body navigation system that uses language input and visual data to guide a humanoid robot. They trained it in computer simulations to plan collision-free moves with 29 degrees of freedom, then tested it in cluttered indoor scenes with a real robot.
Why it matters
If reliable, such systems could help service robots move safely around offices and homes, but stairs remain a challenge in some situations today.
The approach blends language understanding with full-body motion, so the robot plans moves that keep its arms, torso, and legs from hitting objects.
In practice, a robot might navigate a busy hallway more smoothly, but safety trade-offs exist and sensing must be robust beyond color cameras.
Original sources · 1
- TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model ↗arXiv · 2026-09-08
Check the original paper for its authors, methods, version and access terms.
What remains uncertain
Relies on color-camera vision and fast tracking; stairs, very low-light, or ambiguous scenes were not fully tested.
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