SID: Sliding into Distribution for Robust Few-Demonstration Manipulation

Generalizing robotic manipulation across object poses, viewpoints, and dynamic disturbances is difficult, especially with only a few demonstrations. End-to-end visuomotor policies are expressive but data-hungry, while planning and optimization satisfy explicit constraints but do not directly capture the interaction strategies demonstrated by humans. We propose Sliding into Distribution (SID), a structured framework that learns an object-centric motion field from canonicalized demonstrations to iteratively slide the system toward the demonstrated manifold and into the reliable operating region of a lightweight egocentric execution policy, mitigating out-of-distribution (OOD) execution. The motion field provides large corrective motions when far from the demonstration manifold and naturally vanishes near convergence, enabling robust reaching under substantial pose and viewpoint shifts. Within the reached regime, an egocentric policy trained with conditioned flow matching performs task-specific manipulation, supported by kinematically consistent point-cloud reprojection augmentation that preserves action-observation consistency. Across six real-world tasks, SID achieves approximately 90% success under OOD initializations with only two demonstrations, with under a 10% drop under distractors and external disturbances. Overall, SID provides a new paradigm for few-shot manipulation: explicitly managing distribution shift via online distribution recovery.

IG-RFT: An Interaction-Guided RL Framework for VLA Models in Long-Horizon Robotic Manipulation

Vision-Language-Action (VLA) models have demonstrated significant potential for generalist robotic policies; however, they struggle to generalize to long-horizon complex tasks in novel real-world domains due to distribution shifts and the scarcity of high-quality demonstrations. Although reinforcement learning (RL) offers a promising avenue for policy improvement, applying it to real-world VLA fine-tuning faces challenges regarding exploration efficiency, training stability, and sample cost. To address these issues, we propose IG-RFT, a novel Interaction-Guided Reinforced Fine-Tuning system designed for flow-based VLA models. Firstly, to facilitate effective policy optimization, we introduce Interaction-Guided Advantage Weighted Regression (IG-AWR), an RL algorithm that dynamically modulates exploration intensity based on the robot's interaction status. Furthermore, to address the limitations of sparse or task-specific rewards, we design a novel hybrid dense reward function that integrates the trajectory-level reward and the subtask-level reward. Finally, we construct a three-stage RL system comprising SFT, Offline RL, and Human-in-the-Loop RL for fine-tuning VLA models. Extensive real-world experiments on four challenging long-horizon tasks demonstrate that IG-RFT achieves an average success rate of 85.0%, significantly outperforming SFT (18.8%) and standard Offline RL baselines (40.0%). Ablation studies confirm the critical contributions of IG-AWR and hybrid reward shaping. In summary, our work establishes and validates a novel reinforced fine-tuning system for VLA models in real-world robotic manipulation.