[2602.10594] Flow-Enabled Generalization to Human Demonstrations in Few-Shot Imitation Learning

Computer Science > Robotics arXiv:2602.10594 (cs) [Submitted on 11 Feb 2026 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Flow-Enabled Generalization to Human Demonstrations in Few-Shot Imitation Learning Authors:Runze Tang, Penny Sweetser View a PDF of the paper titled Flow-Enabled Generalization to Human Demonstrations in Few-Shot Imitation Learning, by Runze Tang and Penny Sweetser View PDF HTML (experimental) Abstract:Imitation Learning (IL) enables robots to learn complex skills from demonstrations without explicit task modeling, but it typically requires large amounts of demonstrations, creating significant collection costs. Prior work has investigated using flow as an intermediate representation to enable the use of human videos as a substitute, thereby reducing the amount of required robot demonstrations. However, most prior work has focused on the flow, either on the object or on specific points of the robot/hand, which cannot describe the motion of interaction. Meanwhile, relying on flow to achieve generalization to scenarios observed only in human videos remains limited, as flow alone cannot capture precise motion details. Furthermore, conditioning on scene observation to produce precise actions may cause the flow-conditioned policy to overfit to training tasks and weaken the generalization indicated by the flow. To address these gaps, we propose SFCrP, which includes a Scene Flow prediction model for Cross-embodiment learning (SFCr) and a Flow and Cr...