RedFlow Turns Robot Failures Into Action-Level Corrections
The paper proposes converting failed actions into localized corrections for flow-matching vision-language-action policies.
The paper proposes converting failed actions into localized corrections for flow-matching vision-language-action policies.
DLAM introduces temporal constraints intended to make latent robot actions more coherent across sequential control decisions.
Researchers mapped attacks across sensing, world modelling, planning, control and human interaction in embodied-AI systems.