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.
Affordance-guided reinforcement learning via visual prompting.arXiv preprint arXiv:2407.10341, 2024
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Success Visitation Matching uses a discriminator to turn sparse outcome rewards into dense process rewards by matching visitations of successful episodes, provably preserving the optimal policy and speeding up robotic RL finetuning.
VLMs generalize affordance inference to non-humanoid robots but produce inconsistent results with a conservative bias of low false positives and high false negatives, especially for novel object manipulations.
citing papers explorer
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ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation
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.
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Learning Process Rewards via Success Visitation Matching for Efficient RL
Success Visitation Matching uses a discriminator to turn sparse outcome rewards into dense process rewards by matching visitations of successful episodes, provably preserving the optimal policy and speeding up robotic RL finetuning.
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Assessing VLM-Driven Semantic-Affordance Inference for Non-Humanoid Robot Morphologies
VLMs generalize affordance inference to non-humanoid robots but produce inconsistent results with a conservative bias of low false positives and high false negatives, especially for novel object manipulations.