TriPilot-FF: Coordinated Whole-Body Teleoperation with Force Feedback
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Abstract (arXiv)
Mobile manipulators broaden the operational envelope for robot manipulation. However, the whole-body teleoperation of such robots remains a problem: operators must coordinate a wheeled base and two arms while reasoning about obstacles and contact. Existing interfaces are predominantly hand-centric (e.g., VR controllers and joysticks), leaving foot-operated channels underexplored for continuous base control. We present TriPilot-FF, an open-source whole-body teleoperation system for a custom bimanual mobile manipulator that introduces a foot-operated pedal with lidar-driven pedal haptics, coupled with upper-body bimanual leader-follower teleoperation. Using only a low-cost base-mounted lidar, TriPilot-FF renders a resistive pedal cue from proximity-to-obstacle signals in the commanded direction, shaping operator commands toward collision-averse behaviour without an explicit collision-avoidance controller. The system also supports arm-side force reflection for contact awareness and provides real-time force and visual guidance of bimanual manipulability to prompt mobile base repositioning, thereby improving reach. We demonstrate the capability of TriPilot-FF to effectively ``co-pilot'' the human operator over long time-horizons and tasks requiring precise mobile base movement and coordination. Finally, we incorporate teleoperation feedback signals into an Action Chunking with Transformers (ACT) policy and demonstrate improved performance when the additional information is available. We release the pedal device design, full software stack, and conduct extensive real-world evaluations on a bimanual wheeled platform. The project page of TriPilot-FF is http://bit.ly/46H3ZJT.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Feb 2026.
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Citing papers per year: 2026: 5.
5 papers.
- 2026Rewind-IL: Online Failure Detection and State Respawning for Imitation Learningcited by 9
- 2026PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulationcited by 2
- 2026AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policiescited by 2
- 2026ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Modelscited by 2
- 2026TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitationcited by 1
- 2026ContactGuard: Pre-Contact Execution Monitoring with Action-Conditioned Latent World Models13 Aug 2026
- 2026AutoIntervene: Calibrated Intervention for Action-Chunking Imitation Learning Policies7 Aug 2026
- 2026PATCH: Action-Chunk-Conditioned Latent Patch Innovation Monitoring for Robot Manipulation15 Jun 2026
- 2026TRACE: Trajectory-Routed Causal Memory for Delayed-Evidence Visuomotor Imitation12 Jun 2026
- 2026Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning17 Apr 2026