This work proposes SynthDemo-RL, a teacher-student framework in which an automated teacher converts simulator-privileged state into successful manipulation trajectories, a VLA student is distilled from them by supervised fine-tuning (SFT), and PPO with binary task-success rewards refines the student.
Hiroaki Kingetsu, Hiroaki Kurihara, Kaoru Yokoo et al.· 0 citations
PA-RL, a reinforcement-learning framework that uses artificial potential fields as the action representation, is proposed and is the only method to reach a 100% evaluation success rate within the allotted training time, while the best baseline reaches 92.6%.
This report describes our 2nd place solution to the HANDS 2026 workshop challenge (Dexterous Grasp Motion track) in conjunction with ECCV 2026. In this challenge, we address grasp motion generation for the 12-DoF LinkerHand O6, aiming to produce physically plausible reach-and-lift trajectories for unseen objects from r...
Muneeb A. Khan, Woojin Kim, Shinwoo Kim et al.· 0 citations
AtomEgo is presented, a systematic study of ego--robot co-training supported by a curated corpus of approximately 2,659 hours and a scalable data processing pipeline that reveals a simple principle: Data Scale * Alignment Quality -->Capability Gain; egocentric data can improve generalization, but their value depends on...
Di Wu, Dong-Chen Zheng, Jun-He Sheng et al.· 0 citations
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VLA-Scope is introduced, a two-stage framework that combines input-shift characterization with execution history to predict failure during OOD rollouts and achieves a higher ROC-AUC than the evaluated ActProbe and SAFE-MLP baselines.
A knowledge extraction and reasoning module based on a vision-language model that associates object and action descriptions with inferred task conditions, demonstration references, and permissible execution variations is developed, which translates into executable demonstrations that exhibit multimodal behavior through...
Zhi-Yuan Gao, Yan Zhan, Mohammad Khoshnazar et al.· 0 citations
This work proposes FOCAL-VLA, a framework that combines subtask-guided geometry distillation with implicit world modeling to learn representations of current spatial structure and future interaction dynamics, and shows that FOCAL-VLA outperforms baselines on both simulation benchmarks and real-world manipulation tasks.
Results show that endpoint correction improves the quality-latency trade-off of frozen flow-matching policies, and supports correction as an effective alternative to additional integration.
Zhi-Peng Tang, Xin-Da Chen, Wei-Ning Rao et al.· 2 citations
Results show that pose-stamped experience can serve directly as the environment representation for a learned planner, and that making attention depend on pose differences, rather than on temporal order, speeds up training and improves long-horizon navigation.
Bei-Ming Li, Jaime Romero, Jonathan Diller et al.· 0 citations
Plant growth and agricultural production form the foundation of a country's sustainable development and directly impact human livelihoods. Recent advances in frontier artificial intelligence have enabled scientific agriculture with strong potential to improve crop productivity. In this paper, we identify the importance...
Longchao Da, Xia-Oou Liu, Xing-Jian Li et al.· 0 citations
Perceptual planning tasks require two key capabilities: accurately perceiving uncertain scenes and planning valid action sequences following logical rules. Conventional methods convert perception into discrete symbolic facts and then plan, discarding perceptual uncertainty and severing task-level feedback to perception...
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
