A collaborative dataset spanning 22 robots and 527 skills enables RT-X models that transfer capabilities across different robot embodiments.
Avid: Learning multi-stage tasks via pixel-level translation of human videos
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A 1,362-hour multi-source egocentric human dataset and multi-lab study show co-training improves robot manipulation, but only when aligned human–robot data anchors transfer from diverse human data.
Video-to-robot control methods cluster into three interface families, and the field’s main bottleneck is grounding video-derived predictions into dependable closed-loop robot behavior.
RIGVid shows that filtered AI-generated videos can serve as effective supervision for complex robotic manipulation tasks without any real demonstrations.
A low-cost whole-body teleoperation system enables effective imitation learning for complex bimanual mobile manipulation by co-training on mobile and static demonstration datasets.
XIPER creates a reward signal for cross-domain video imitation learning by training a video prediction model that maps agent views to the expert domain and scoring prediction likelihood.
citing papers explorer
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Open X-Embodiment: Robotic Learning Datasets and RT-X Models
A collaborative dataset spanning 22 robots and 527 skills enables RT-X models that transfer capabilities across different robot embodiments.
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EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World
A 1,362-hour multi-source egocentric human dataset and multi-lab study show co-training improves robot manipulation, but only when aligned human–robot data anchors transfer from diverse human data.
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From Video to Control: A Survey of Learning Manipulation Interfaces from Temporal Visual Data
Video-to-robot control methods cluster into three interface families, and the field’s main bottleneck is grounding video-derived predictions into dependable closed-loop robot behavior.
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Robotic Manipulation by Imitating Generated Videos Without Physical Demonstrations
RIGVid shows that filtered AI-generated videos can serve as effective supervision for complex robotic manipulation tasks without any real demonstrations.
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Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation
A low-cost whole-body teleoperation system enables effective imitation learning for complex bimanual mobile manipulation by co-training on mobile and static demonstration datasets.
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Reinforcement Learning from Cross-domain Videos with Video Prediction Model
XIPER creates a reward signal for cross-domain video imitation learning by training a video prediction model that maps agent views to the expert domain and scoring prediction likelihood.