PACT: Posture-Aligned Co-Design Technique for Hand Configura
Overview
Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: PACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning. This research presents PACT, a method that jointly optimizes the mechanical design and posture retargeting of a 17-servo robot hand to better replicate human grasping postures. It also achieves an 86% grasping success ra…
Generated from attributed reports · Updated 2 hours ago
Event evidence and corrections
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Reported quantity · kinematic RMSE: 3.9 other · Basis not reported
“e DexYCB dataset, and is constrained by servo interference, assembly clearances, and flat-hand protrusion, so the resulting design can be physically assembled. We fabricate the PACT Hand and validate it through teleoperation experiments and an imitation-learning demonstration. The hand reproduces all human hand keypoints with a kinematic RMSE of 3.9 mm, compared to 12.8 mm for the LEAP Hand baseline, and keeps all fi”
Exact source · revision 1Source owner not reported
Reported quantity · payload lifted: 2 other · Basis not reported
“ngertips within 10 mm on every grasping posture, while the baseline achieves this for only 21.6% of the poses. Under matched per-joint torque limits, it lifts twice the baseline payload, 2 kg against 1 kg, and deforms a held paper cup seven times less, reaching a deformation level comparable to a human hand. It also achieves an 86% grasping success rate on YCB daily objects, compared to 64% for the baseline. Finally,”
Exact source · revision 1Source owner not reported
Reported quantity · deformation level: 7 other · Basis not reported
“ngertips within 10 mm on every grasping posture, while the baseline achieves this for only 21.6% of the poses. Under matched per-joint torque limits, it lifts twice the baseline payload, 2 kg against 1 kg, and deforms a held paper cup seven times less, reaching a deformation level comparable to a human hand. It also achieves an 86% grasping success rate on YCB daily objects, compared to 64% for the baseline. Finally,”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 75 percent · Basis not reported
“as a demonstration, an imitation-learning policy trained on demonstrations collected with the PACT Hand achieved a 75% success rate at held-out placements in a can pick-and-place task. Project page: https://pact-hand-2027.github.io”
Exact source · revision 1Source owner not reported
Reported quantity · baseline payload lifted: 1 other · Basis not reported
“ngertips within 10 mm on every grasping posture, while the baseline achieves this for only 21.6% of the poses. Under matched per-joint torque limits, it lifts twice the baseline payload, 2 kg against 1 kg, and deforms a held paper cup seven times less, reaching a deformation level comparable to a human hand. It also achieves an 86% grasping success rate on YCB daily objects, compared to 64% for the baseline. Finally,”
Exact source · revision 1Source owner not reported
Reported quantity · baseline deformation level: 1 other · Basis not reported
“ngertips within 10 mm on every grasping posture, while the baseline achieves this for only 21.6% of the poses. Under matched per-joint torque limits, it lifts twice the baseline payload, 2 kg against 1 kg, and deforms a held paper cup seven times less, reaching a deformation level comparable to a human hand. It also achieves an 86% grasping success rate on YCB daily objects, compared to 64% for the baseline. Finally,”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalPACT: Posture-Aligned Co-Design Technique for Hand Configuration and Retargeting in Multi-Finger Imitation Learning
This research presents PACT, a method that jointly optimizes the mechanical design and posture retargeting of a 17-servo robot hand to better replicate human grasping postures. It also achieves an 86% grasping success rate on YCB objects, with an imitation-learning policy achieving 75% success in a pick-and-place task.
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