robotics
A Humanoid Model Split Action Tokens by Body Part
Grouped discrete diffusion decoded end-effector, body, hand and kinematic actions for real-time control.
Summary
Grouped discrete diffusion decoded end-effector, body, hand and kinematic actions for real-time control.
Holo-M extends a language model's vocabulary with four action-token groups for end effectors, body, hands and kinematics. The design trains across humanoid teleoperation, egocentric human video and simulation, then decodes body-part groups with discrete diffusion rather than token-by-token autoregression. The authors report the highest success rates in both generalist and specialist evaluations on their SIMPLE humanoid benchmark. That ranking belongs to the submitted comparison set; code and weights are promised for release.
Why it matters
Grouped discrete diffusion decoded end-effector, body, hand and kinematic actions for real-time control.
Limits and context
No additional limitation was separately recorded.
Key claims
Grouped discrete diffusion decoded end-effector, body, hand and kinematic actions for real-time control.
Evidence: source-2026-09-29-009
Sources
- arXiv preprint 2609.35709arXiv · primary research
Corrections
No corrections have been recorded for this story.