HERO demonstrates self-improving manipulation without human demonstrations
Researchers introduced HERO, a hierarchical embodied agent that autonomously gathers experience and consolidates reusable manipulation policies.
Researchers introduced HERO, a hierarchical embodied agent that autonomously gathers experience and consolidates reusable manipulation policies.
A controlled study found prediction objectives determine whether robot world models retain mass, stiffness, drag, and force information.
Samsung formed a robotics strategy unit and outlined research hubs in the United States, China, and Japan.
Noetra, Sony, SoftBank, NEC, Honda, AIST, and partners started full-scale development of a sovereign multimodal model.
The financing targets foundation-model iteration, data-collection equipment and industrial validation.
The on-device model was reported to adapt rapidly across robots with differing sensors and embodiments.
Generalist AI was reported to be discussing a funding round at a three-billion-dollar valuation.
DLAM introduces temporal constraints intended to make latent robot actions more coherent across sequential control decisions.
The paper proposes converting failed actions into localized corrections for flow-matching vision-language-action policies.
The work uses action-conditioned predictive models to improve generalizable embodied decision-making.