Open datasets describing human behavior in environments shared with mobile robots remain limited, particularly for retail activities that combine locomotion, reaching, object handling, and robot guided movement. This paper introduces OHRID Retail, an open, human centered multimodal dataset collected from 16 healthy adu...
Xiang-Rui Wang, Yue-Tong Wu, Jalen Beeman et al.· 0 citations
World Action Models (WAMs) advance beyond conventional visuomotor policies by jointly predicting future world states and robot actions, enabling the policy to learn physical dynamics that support effective control. However, recent tactile WAMs often rely on large-scale pretrained generative backbones to capture contact...
Hanchu Zhou, Brendan Lynch, Raman Goyal et al.· 0 citations
As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving. End-to-end driving policies are pre-trained in open loop with behavior cloning on human demonstrations. However, compounding errors during closed-loop de...
D. Da Col, M. Igl, Peter Karkus et al.· 1 citation
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MILER, an end-to-end policy framework with zero-shot sim-to-real transfer of both perception and control, is presented and extensively evaluated on a diverse test track comprising numerous challenges.
Thomas Steinecker, Denis Trescher, Alexander Bienemann et al.· 0 citations
Daily locomotion encompasses diverse activities and frequent transitions between them, yet most exoskeleton controllers are designed for a single activity or a narrow set of related movements. Changes in activity therefore typically require explicit mode switching and separately tuned or retrained controllers. Simulati...
Yi-Fei Yuan, Jakob H. Wolf, Ghaith J. Androwis et al.· 0 citations
Trie-GRPO, a novel reinforcement learning algorithm based on action prefix trees, which enables step-level advantage estimation, is introduced, which resolves the credit assignment problem by isolating intermediate correct decisions from downstream errors, while effectively balancing exploration efficiency and depth co...
Fei-Fan Wang, Zong-Bing Zhang, Yu Zhang et al.· 0 citations
Three key findings are shown: co-designing body and gait is necessary to satisfy locomotion constraints; actuator-envelope feasibility, rather than locomotion success alone, determines realizable payload capacity; and canonical animal gaits emerge naturally in most designs from morphology and constraints alone.
PONDERPOUNCE reuses an MLLM's native causal context and demonstrates real-world applicability on four tasks under asynchronous execution, with 60.98% mean success versus 40.67% for FrameSamp+Modul.
Suhwan Choi, Jaeyoon Jung, Sungkyung Kim et al.· 0 citations
Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for building general-purpose robotic agents. However, the VLA landscape remains highly fragmented and complex: as existing approaches vary substantially in architectures, training data, embodiment configurations, and benchmark-specific en...
Jinhui Ye, Ning Gao, Senqiao Yang et al.· 0 citations
Aerodynamic promptness quantifies how rotor-speed variations generate multirotor wrench variations, but its Euclidean formulation assigns the same local cost to a given rotor acceleration at every operating speed. This work develops a capacity-aware extension for redundant multirotors with arbitrary numbers of heteroge...
Real-robot demonstrations are prohibitively expensive, while simulation data and real-world human demonstrations are both scalable but each leaves a distinct gap: simulation suffers from a sim-to-real visual gap, and human data suffers from a human-to-robot embodiment gap. In this work, we identify a natural yet undere...
Kaipeng Fang, Weiqing Liang, Yuyang Li et al.· 0 citations
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.
Gemini Robotics ER 2 helps robots reason, collaborate, and solve real-world tasks. It represents a step change in video understanding, tool orchestration, and multi-robot collaboration for robotic applications.
From feet to fingertips — we are teaching robots intelligent whole-body control, fine dexterity, and teamwork to complete a broad range of complex tasks.
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