DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
Overview
This research introduces DexTeleop-0, a system that enhances bimanual dexterous manipulation through tactile-driven adaptation. By integrating fingertip feedback into teleoperation pipelines, the framework performs real-time optimization to correct contact forces and positions. Evaluated in both simulation and real-world hardware, the system achieves 91% success rates for Ball Assembly and 62% for Stir in Cup tasks, outperforming force tracking alone. The project is open-sourced at https://henryhcliu.github.io/dexteleop-0.
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Control: teleoperated
“DexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 91 percent · reported trials · Differing source assertions
“predicted-contact-moment regularization, and bounded hand-joint residuals. We evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Controlled simulation replay yields final-stage success rates of 91% for Ball Assembly and 62% for Stir in Cup, versus 88% and 49% for force tracking alone. Fixed-input replay on two hand types, seven-operator teleoperation, and exte”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 62 percent · reported trials · Differing source assertions
“predicted-contact-moment regularization, and bounded hand-joint residuals. We evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Controlled simulation replay yields final-stage success rates of 91% for Ball Assembly and 62% for Stir in Cup, versus 88% and 49% for force tracking alone. Fixed-input replay on two hand types, seven-operator teleoperation, and exte”
Exact source · revision 1Source owner not reported
Reported quantity · robots: 2 other · Basis not reported
“predicted-contact-moment regularization, and bounded hand-joint residuals. We evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Controlled simulation replay yields final-stage success rates of 91% for Ball Assembly and 62% for Stir in Cup, versus 88% and 49% for force tracking alone. Fixed-input replay on two hand types, seven-operator teleoperation, and exte”
Exact source · revision 1Source owner not reported
Reported quantity · operators: 7 other · Basis not reported
“predicted-contact-moment regularization, and bounded hand-joint residuals. We evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Controlled simulation replay yields final-stage success rates of 91% for Ball Assembly and 62% for Stir in Cup, versus 88% and 49% for force tracking alone. Fixed-input replay on two hand types, seven-operator teleoperation, and exte”
Exact source · revision 1Source owner not reported
Reported quantity · tasks: 2 other · reported trials
“predicted-contact-moment regularization, and bounded hand-joint residuals. We evaluate this tactile-driven adaptation strategy across both simulated environments and real-world hardware. Controlled simulation replay yields final-stage success rates of 91% for Ball Assembly and 62% for Stir in Cup, versus 88% and 49% for force tracking alone. Fixed-input replay on two hand types, seven-operator teleoperation, and exte”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDexTeleop-0: Force-Aware Bimanual Dexterous Teleoperation with Ego-Centric Perception towards Shared Autonomy
This research introduces DexTeleop-0, a system that enhances bimanual dexterous manipulation through tactile-driven adaptation. By integrating fingertip feedback into teleoperation pipelines, the framework performs real-time optimization to correct contact forces and positions. Evaluated in both simulation and real-world hardware, the system achieves 91% success rates for Ball Assembly and 62% for Stir in Cup tasks, outperforming force tracking alone. The project is open-sourced at https://henryhcliu.github.io/dexteleop-0.
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