LocoPilot: Single-Operator Bilateral Teleoperation for Loco-Manipulation Policy Learning

Key points

• Robot-free interfaces allow demonstrations to be collected independently of the target robot, increasing flexibility but without directly capturing platform-specific commands, proprioception, and body-mounted observations.1
• This paper presents LocoPilot, a single-operator bilateral teleoperation and data-collection framework implemented on a 12-DoF Unitree Go2 quadruped equipped with a 6-DoF AgileX Piper L manipulator, an Intel RealSense D435 wrist camera, and an Intel RealSense D405 head camera.1
• Compared to prior work, LocoPilot uniquely unifies robot-in-the-loop legged data collection, force feedback without motion-capture instrumentation, and the same action interface at deployment.1
• We evaluate the interface across multiple loco-manipulation tasks and validate the complete collection-to-deployment pipeline by fine-tuning MolmoAct2 \cite{MOLMO} on pick-and-place demonstrations and deploying on the physical robot without action retargeting or additional inverse kinematics.1

Sources (1)

• [1]LocoPilot: Single-Operator Bilateral Teleoperation for Loco-Manipulation Policy Learning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 10:37 AM

  Learning loco-manipulation policies requires demonstrations that capture coordinated base and arm motion on the target embodiment.

  Robot-free interfaces allow demonstrations to be collected independently of the target robot, increasing flexibility but without directly capturing platform-specific commands, proprioception, and body-mounted observations.

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