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RoboHarn-Evo is introduced, a dual-loop harness that evolves Hierarchical Physical Knowledge (HPK) from physical experience that demonstrates that physical interaction can be accumulated into reusable knowledge for improving subsequent manipulation.
Results show that initial-state performance does not determine recovery performance and policies exhibit different recovery strengths across scenarios, and RoboRecover establishes recovery from execution-induced intermediate states as a distinct dimension of robot policy evaluation.
JEPA-WAM, a latent WAM built in a pretrained V-JEPA space, which couples latent transition prediction with continuous action generation through a shared predictor, predicts a spatially structured joint current-future target that captures task-shared visual temporal structure between current and future observations, whi...
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