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General Flow as Foundation Affordance for Scalable Robot Learning
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We address the challenge of acquiring real-world manipulation skills with a scalable framework. We hold the belief that identifying an appropriate prediction target capable of leveraging large-scale datasets is crucial for achieving efficient and universal learning. Therefore, we propose to utilize 3D flow, which represents the future trajectories of 3D points on objects of interest, as an ideal prediction target. To exploit scalable data resources, we turn our attention to human videos. We develop, for the first time, a language-conditioned 3D flow prediction model directly from large-scale RGBD human video datasets. Our predicted flow offers actionable guidance, thus facilitating zero-shot skill transfer in real-world scenarios. We deploy our method with a policy based on closed-loop flow prediction. Remarkably, without any in-domain finetuning, our method achieves an impressive 81\% success rate in zero-shot human-to-robot skill transfer, covering 18 tasks in 6 scenes. Our framework features the following benefits: (1) scalability: leveraging cross-embodiment data resources; (2) wide application: multiple object categories, including rigid, articulated, and soft bodies; (3) stable skill transfer: providing actionable guidance with a small inference domain-gap. Code, data, and supplementary materials are available https://general-flow.github.io
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Cited by 9 Pith papers
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Deep Sensorimotor Control by Imitating Predictive Models of Human Motion
A predictive model of human hand motion, trained on human interaction data, can reward a robot policy for tracking predicted future keypoints and enable learning of dexterous manipulation from sparse rewards.
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A robot policy that propagates a retrieved contact point through predicted object poses, producing a time-varying affordance trajectory, improves manipulation success over static affordance and pose-only baselines.
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$\pi\mathbf{R}^2$: Reactive Real-time Flow Policies
πR² makes flow-matching VLA policies reactive by splitting conditioning into fresh proprioception and stale vision-language features and using a one-step-per-call staircase noise schedule, reaching ~25 Hz closed-loop ...
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KAM-WM: Kinematic Affordance Maps from Latent World Models for Robot Manipulation
A single-step latent velocity from a frozen Flow Matching video model acts as a first-order kinematic affordance prior that improves low-data robot manipulation without future-frame rollout.
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AMPLIFY: Actionless Motion Priors for Robot Learning from Videos
A three-stage pipeline that turns keypoint tracks into discrete motion tokens, predicts them from action-free video, and decodes them into actions yields large few-shot and zero-shot policy improvements in robot manipulation.
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GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation
GenManip is a benchmark and simulation platform with LLM-generated scene graphs for testing how robot policies generalize to new instructions, layouts, and objects.
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A two-stage pipeline trains a robot policy that accepts a human demonstration video as a prompt and generalizes beyond its robot training tasks, with success rates of up to 79 percent on known task variations and unde...
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3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model
A diffusion world model predicts 3D optical flow as an embodiment-agnostic action plan, and constrained optimization converts the flow into robot arm actions.
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