Kimodo-Policy: Learning Kinematic Action Models for Humanoid Loco-Manipulation
Overview
This paper presents Kimodo-Policy, an approach that treats whole-body motion as a direct pretraining target using a frozen text-to-motion backbone and lightweight visual conditioning. The method achieves a 72.14% overall success rate on the multi-task HumanoidArena benchmark and demonstrates competitive performance on SIMPLE and six real-world tasks with 60% mean success across eight tasks, utilizing 678M parameters (92M trainable).
Generated from attributed reports · Updated 9 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
No current evidence-backed claims. Missing information remains not reported.
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalKimodo-Policy: Learning Kinematic Action Models for Humanoid Loco-Manipulation
This paper presents Kimodo-Policy, an approach that treats whole-body motion as a direct pretraining target using a frozen text-to-motion backbone and lightweight visual conditioning. The method achieves a 72.14% overall success rate on the multi-task HumanoidArena benchmark and demonstrates competitive performance on SIMPLE and six real-world tasks with 60% mean success across eight tasks, utilizing 678M parameters (92M trainable).
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).