EgoCPT: Learning Dexterous Manipulation from Ego-Centric Video
Overview
EgoCPT learns dexterous hand movements from first-person videos, achieving 67.3% success on noisy data and 92.0% on high-quality reconstructions, outperforming baselines in robotic manipulation tasks.
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Reported quantity · Reported success rate: 67.3 percent · Basis not reported · Differing source assertions
“od is particularly robust to noisy hand poses attributed to our novel contact-aware optimization framework, which iteratively optimizes the contact points between the robot hands and objects. Across five manipulation tasks, our method achieves overall success rates of 67.3% on noisy EgoDex hand-pose reconstructions and 92.0% on high-quality TACO reconstructions collected using motion-capture hardware, outperforming m”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 92 percent · Basis not reported · Differing source assertions
“od is particularly robust to noisy hand poses attributed to our novel contact-aware optimization framework, which iteratively optimizes the contact points between the robot hands and objects. Across five manipulation tasks, our method achieves overall success rates of 67.3% on noisy EgoDex hand-pose reconstructions and 92.0% on high-quality TACO reconstructions collected using motion-capture hardware, outperforming m”
Exact source · revision 1Source owner not reported
Reported quantity · manipulation tasks: 5 other · Basis not reported
“od is particularly robust to noisy hand poses attributed to our novel contact-aware optimization framework, which iteratively optimizes the contact points between the robot hands and objects. Across five manipulation tasks, our method achieves overall success rates of 67.3% on noisy EgoDex hand-pose reconstructions and 92.0% on high-quality TACO reconstructions collected using motion-capture hardware, outperforming m”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsEgoCPT: Learning Dexterous Manipulation from Ego-Centric Video with Contact-Aware Optimization
EgoCPT learns dexterous hand trajectories from ego-centric videos. It outperforms baselines with 67.3% success on noisy data and 92.0% on high-quality reconstructions.
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