DreamVLA: From Generated Human Motions to Humanoid Vision-Language-Action Policies
Overview
This paper introduces DreamVLA, a framework that leverages generated motion trajectories to guide reinforcement learning in simulation for training vision-language-action (VLA) policies on humanoid robots. The method addresses the data bottleneck of human teleoperation by using reference-conditioned DAgger distillation to aggregate successful corrective trajectories after failed learner rollouts. Evaluated on tasks including picking from a platform, opening a drawer, pressing a button, and walking while picking, the approach trains VLA models in simulation without requiring new task-specific teleoperation data.
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Environment: simulation
“s, we propose DreamVLA, which uses generated motion trajectories to guide reinforcement learning (RL) in simulation, converting them into reliable task execution trajectories. These trajectories train VLA models in simulation without new task-specific teleoperation data. Our central contribution is a reference-conditioned DAgger distillation procedure: after a failed learner rollout, we restart the RL expert from a s”
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- arXiv Robotics — research abstractsSignalDreamVLA: From Generated Human Motions to Humanoid Vision-Language-Action Policies
This paper introduces DreamVLA, a framework that leverages generated motion trajectories to guide reinforcement learning in simulation for training vision-language-action (VLA) policies on humanoid robots. The method addresses the data bottleneck of human teleoperation by using reference-conditioned DAgger distillation to aggregate successful corrective trajectories after failed learner rollouts. Evaluated on tasks including picking from a platform, opening a drawer, pressing a button, and walking while picking, the approach trains VLA models in simulation without requiring new task-specific teleoperation data.
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