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Puppeteer Your Robot: Augmented Reality Leader-Follower Teleoperation
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High-quality demonstrations are necessary when learning complex and challenging manipulation tasks. In this work, we introduce an approach to puppeteer a robot by controlling a virtual robot in an augmented reality setting. Our system allows for retaining the advantages of being intuitive from a physical leader-follower side while avoiding the unnecessary use of expensive physical setup. In addition, the user is endowed with additional information using augmented reality. We validate our system with a pilot study n=10 on a block stacking and rice scooping tasks where the majority rates the system favorably. Oculus App and corresponding ROS code are available on the project website: https://ar-puppeteer.github.io/
Forward citations
Cited by 2 Pith papers
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BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots
BEAVR provides an open-source, low-cost VR teleoperation pipeline for multiple robot embodiments, with LeRobot-format data recording and compatibility with ACT, Diffusion Policy, and SmolVLA.
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RwoR: Generating Robot Demonstrations from Human Hand Collection for Policy Learning without Robot
A generative model and wrist camera turn human hand videos into robot gripper demonstrations that train manipulation policies at success rates close to those trained on real gripper data.
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