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

Antonio Langella, Giovanni Minelli, Giovanni Dessy, Lorenzo Amatucci, Giulio Turrisi, Victor Barasuol, Claudio Semini · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27998 · cited by 0 works (OpenAlex) · open-access PDF

Korean

로봇 무료 인터페이스는 대상 로봇과 독립적으로 동작할 수 있는 인터페이스를 필요로 하며, 동일한 유연성을 증가시켜 플랫폼-특정 명령, proprioception 및 몸-모든 관찰을 직접 캡처할 수 있는 데이터를 필요로 합니다. 로봇-무료 인터페이스는 목표 로봇과 독립적으로 캡처할 수 있습니다. 로봇-무료 인터페이스는 플랫폼-특정 명령, proprioception 및 몸-모든 관찰을 직접 캡처하지 않고도 동일한 피드백을 필요로 합니다. 로봇-무료 인터페이스는 완전한 시스템을 동시에 지휘할 수 있는 인터페이스를 필요로 합니다. 이 논문은 로봇-모션 로봇(LocoPilot)을 평가합니다. 두 번째 로봇은 리더와 두

Machine translation (Meta M2M100); the English original follows.

Abstract

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. On-robot teleoperation allows to capture these signals directly, but requires an interface capable of commanding the complete system concurrently. 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. A second 6-DoF Piper L arm, used as the leader and two handheld controllers provide simultaneous control of the base, body posture, manipulator, and gripper. The follower arm interaction forces are estimated without a dedicated force/torque sensor and rendered to the operator through a passivity-constrained haptic channel. A ROS~2 pipeline records synchronized multimodal observations together with a unified 11D action shared by teleoperation and policy deployment. 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. 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. Code available at: https://github.com/iit-DLSLab/LocoPilot

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Topics: Teleoperation, Haptic technology, Computer science, Interface (matter), Pipeline (software), Retargeting

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange