Multi-agent path planning (MAPP) in continuous environments often relies on roadmaps to balance safety and search efficiency. However, traditional roadmap generation methods, such as lattice grids or standard sampling-based approaches, frequently face a trade-off between graph density and the likelihood of finding feas...
Brandon Ho, Nikola Rogers, Seung-Kyum Choi· 0 citations
Reinforcement learning can improve vision-language-action (VLA) policies beyond supervised fine-tuning, although this typically involves further updates to the policy parameters. For flow-matching policies, iterative action generation provides an additional opportunity to incorporate task information during inference....
Liuhaichen Yang, Zhuang Jiang, Chenchao Sheng et al.· 0 citations
Generative dynamics models enable planning in challenging systems, but safe deployment requires detecting policy-induced out-of-distribution (OOD) transitions. Existing methods typically treat learned dynamics as fixed and rely on post hoc support surrogates for OOD detection. This overlooks a critical failure mode: le...
Hongzhan Yu, Chenghao Li, Ruipeng Zhang et al.· 0 citations
Autonomous driving in complex traffic requires planners that generalize beyond hand-crafted rules, motivating data-driven approaches that learn behavior from expert demonstrations. Diffusion-based trajectory planners have recently shown strong closed-loop performance by iteratively denoising a full-horizon plan, but th...
Eugene Ku, Yiwei Lyu· 0 citations
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The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging class of dexterity, requiring grasping thin objects, in-hand object rotations, and forceful interactions. Since collecting teleoperation data for these behaviors is challenging, si...
Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg et al.· 0 citations
Physics-based human motion control can make a simulated character walk, sit, and manipulate objects with high physical realism. Almost always, though, this happens in short, isolated clips that are re-initialized between interactions. We instead aim for continuous, reset-free long-horizon motion: a physically simulated...
Haozhuo Zhang, Jingkai Sun, Michele Caprio et al.· 0 citations
This work introduces a calibration pipeline that combines learned stereo depth with a joint factor graph, pooling all episodes collected from the same physical robot to recover its shared kinematic parameters alongside per-scene extrinsics, and trains DepthWorld, a Stable Video Diffusion-based world model that jointly...
Jai Bardhan, Josef Sivic, Vladimír Petrík· 1 citation
Egocentric human data offer a path to scaling robot learning beyond costly robot demonstrations, yet the embodiment gap makes raw human trajectories a poor supervisory target for control. Our key insight is that, although low-level actions are embodiment-specific, their underlying motion intent can capture task-relevan...
Li-Han Zha, Shresth Grover, Tenny Yin et al.· 0 citations
Contact with contaminated objects can spread hazards through a household robot's grippers, tools, and shared surfaces, while new contacts can make an existing plan unsafe. Existing benchmarks do not jointly assess how planners identify hygiene risks from contact history and plan safe continuations after new contact eve...
Yurun Chen, Josh Qixuan Sun, Jason Qin et al.· 0 citations
Early stress detection in crops is a necessity today to improve efficiency and reduce waste of time, money, and effort. However, most modern techniques, such as hyperspectral imaging and AI-based systems, are too costly and complex for medium and small-scale farmers to implement. This paper showcases CropSentry, a low-...
This framework, Coordinated Multi-Agent Diffusion Steering (CMDS), treats frozen pretrained diffusion models as reusable generative primitives and coordinates their reverse processes through a learned control, forming coordination as a stochastic optimal control problem.
Riccardo Barbano, Vincent Pauline, Run-Chang Li et al.· 0 citations
The experiments show that MNP can successfully achieve safe sim-to-real transfer without sacrificing control performance, and it is demonstrated that integrating Evolution Strategy and Statistical Model checking for policy search can drastically reduce neural network size without compromising safety and robustness.
Hong-Peng Cao, Riccardo Curcio, Daniele Ottaviano et al.· 0 citations
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.
