Models for contact-rich manipulation

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.
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.