HALO synthesizes vision-language-motion data for humanoid robots
HALO achieves 91 percent success on composite tasks in unmapped real homes by scaling simulation data rather than human teleoperation.
Stories per day · 30 days
2 RSS
Relatedhumanoid-robots1monte-carlo1neural-operators1robotics1simulation-data1stochastic-control1vision-language1zero-shot-transfer1
4 stories, newest first · one card per story
Stories tagged #motion-planning
PUN neural operator plans under control noise in unseen geometries
On a 4D replanning benchmark it reports 95-100% success across four noise levels and the lowest runtime among compared planners.
RobotUse: Allocating Computation, Context, and Decisions
RobotUse lets robot agents revise physical actions, reaching 45% task success on RoboLab.
PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving
PlannerForge unifies scenario-based testing of autonomous driving motion planners in one LLM-agent framework, outperforming prior baselines.