ReKep encodes robotic tasks as optimizable Python functions over 3D keypoints that are generated automatically from language and RGB-D input, enabling real-time hierarchical planning on single- and dual-arm platforms without task-specific data.
Keypoint action tokens enable in-context imitation learning in robotics
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SynthICL trains flow-matching transformer policies for in-context imitation learning entirely from synthetic RGB data and reports 79% average success on 16 unseen real manipulation tasks with one test-time demonstration.
Decompose and Recompose decomposes seen robotic demonstrations into skill-action alignments and recomposes them via visual-semantic retrieval and planning to enable zero-shot cross-task generalization.
J-PARSE modifies the Jacobian via aspect-ratio thresholding and directional projection to enable stable first-order inverse kinematic velocity control through kinematic singularities in serial manipulators.
Proposes a Red Team-Blue Team adversarial gamification architecture to generate synthetic hazardous scenarios for learning robot safety policies.