SUGARv2: Learning Diverse Visual Humanoid Loco-Manipulation Skills from Synthesized Interaction References
Overview
This research presents SUGARv2, a framework for acquiring diverse visual humanoid loco-manipulation skills by synthesizing interaction references and using a shared training pipeline. The authors adapt reusable task templates to different spatial configurations to generate coordinated robot and object motions at low cost. By employing reference-based imitation as a common learning objective, they train privileged teachers first, then distill visual policies through reinforcement learning with mimic rewards, leveraging diverse object instances and visual domain randomization for generalization and sim-to-real transfer. Across five humanoid tasks, SUGARv2 achieves a 91.0% mean success rate in simulation and shows successful sim-to-real transfer on a real robot for whole-body manipulation.
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Environment: simulation
“rough teacher distillation and reinforcement learning with the same reference-based mimic rewards, using diverse object instances and visual domain randomization to support visual generalization and sim-to-real transfer. Across five humanoid loco-manipulation tasks, SUGARv2 achieves a mean success rate of 91.0% in simulation. Deployment on a real humanoid robot further demonstrates successful sim-to-real transfer for”
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Reported quantity · Reported success rate: 91 percent · reported trials
“rough teacher distillation and reinforcement learning with the same reference-based mimic rewards, using diverse object instances and visual domain randomization to support visual generalization and sim-to-real transfer. Across five humanoid loco-manipulation tasks, SUGARv2 achieves a mean success rate of 91.0% in simulation. Deployment on a real humanoid robot further demonstrates successful sim-to-real transfer for”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalSUGARv2: Learning Diverse Visual Humanoid Loco-Manipulation Skills from Synthesized Interaction References
This research presents SUGARv2, a framework for acquiring diverse visual humanoid loco-manipulation skills by synthesizing interaction references and using a shared training pipeline. The authors adapt reusable task templates to different spatial configurations to generate coordinated robot and object motions at low cost. By employing reference-based imitation as a common learning objective, they train privileged teachers first, then distill visual policies through reinforcement learning with mimic rewards, leveraging diverse object instances and visual domain randomization for generalization and sim-to-real transfer. Across five humanoid tasks, SUGARv2 achieves a 91.0% mean success rate in simulation and shows successful sim-to-real transfer on a real robot for whole-body manipulation.
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