Sensorimotor Alignment predicts which robot models move well
A simple offline score hints which embodied models may perform, but results depend on robot setup and data.
- Publication
- arXiv
- Stage
- Preprint
- What we read
- Summary of the paper
- Authors
- Yunfei Xie, Howard H. Qian, Shiyi Lan, Kaiyu Hang, Chen Wei
- Universities and research institutions
- Not yet supplied in verified metadata; the Brief does not guess.
What they did and found
A training-free score called Sensorimotor Alignment measures how closely a robot’s camera view matches its future actions. Across twelve model families, higher alignment tended to match task success, but results varied with robot design and data sources.
Why it matters
This score can help you screen models before real tests, but expect mismatches if your hardware or data differ from the test setup.
Sensorimotor Alignment compares what the robot sees with how it moves, using stored data rather than real trials. It checks if similar sights tend to produce similar actions.
In practice, you would screen many different perception and action setups with Sensorimotor Alignment, then test a few in real tasks to confirm performance.
What remains uncertain
Correlation is not proof of cause; the measure is offline, and hardware or data differences can weaken the link.
Original sources · 1
- Sensorimotor Alignment: A Platonic Proxy for Embodied Policy Success ↗arXiv · 2026-10-22
Check the original paper for its authors, methods, version and access terms.
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