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Results show that directly steering internal robot-policy representations can improve the performance of robot policies at inference time and show that directly steering internal robot-policy representations can improve the performance of robot policies at inference time.
This systematic study of vision-tactile representations across 12 contact-rich manipulation tasks finds that representations preserving spatial structure and temporal continuity consistently support more accurate prediction and stronger planning performance.
TAC-LOCO is proposed, a tactile-augmented unified reinforcement learning framework that encodes tactile array observations from compliant grippers into a compact latent representation and joins it with proprioception for unified control of the legs, arm, and gripper.
PHR-VLA introduces a lightweight auxiliary future head that, during training, aligns the VLA's internal representations with latent dynamics extracted from future observations, demonstrating that privileged latent dynamics alignment provides an effective training signal for improving anticipatory reasoning in VLA polic...
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