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Models for contact-rich manipulation
Choosing robot learning models for insertion, force-sensitive manipulation, tactile reasoning, and contact-rich control loops.
Contact-rich tasks often fail because the model never sees the right signals, not because the network is too small.
What matters most
- Signal fusionTactile, force, and proprioceptive signals often matter more than bigger vision backbones.
- Recovery behaviorModels must handle retries, slip, and micro-adjustments.
- Latency toleranceReal control loops need predictable inference behavior.
Useful starting points
Main takeaway
For contact-rich work, better supervision and signal design usually beat chasing the largest general model.
Need help with contact-rich policy design?
We can scope sensing, data, and evaluation for harder manipulation tasks.
Every VLA here can be fine-tuned on your own demonstrations.