Dormant tree pruning is labor-intensive yet essential for maintaining modern high-productivity fruit orchards. In this work, we focus on pruning of modern planar tree training systems - V-Trellis apples and UFO cherries - where trunks and primary branches are trained into approximately planar walls. We introduce an end...
Abhinav Jain, Cindy Grimm, Stefan Lee· IEEE Transactions on Field R...· 0 citations
This work presents an SE(3) neural potential field learned from posed RGB images and supervised with a navigation function, the geodesic distance to the grasp through free space recovered from those same images during training, which removes both failures.
Jeffrey Eiyike, Masoud Ataei, Elvis Gyaase et al.· 0 citations
This work presents Uranus, a data-driven robot simulator built around a joint-trajectory-conditioned autoregressive diffusion model, providing a unified interface for synchronized multi-view generation across diverse robot embodiments and camera configurations.
Wen-Kang Qin, Yu-Kun Zhou, Noah Shen et al.· 0 citations
Humanoid robots are increasingly being popular and developed for human-centered applications, yet their ability to provide intelligent conversations and natural interactive knowledge assistance remains constrained by traditional rule-based dialogue systems, pre-defined responses and limited knowledge repositories. Larg...
Han-Xiao Chen· 0 citations
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Touch2Robot, a framework that lets humans collect demonstrations while seeing how the target robot hand would contact the object, shows that bringing robot touch into the human demonstration loop improves both the quality and efficiency of scalable dexterous data collection.
Sheng-Cheng Luo, Xiao Cheng, Hong Ying et al.· 1 citation
Autonomous parking in nonconvex and narrow environments remains challenging. Although optimal-control methods can explicitly enforce vehicle dynamics and collision constraints, nonconvexity compromises solver robustness and can cause failures. Large language models (LLMs) exhibit strong semantic reasoning capabilities,...
Recent 4D LiDAR language models aim to reason about objects and their evolving spatial relationships. Yet, in our evaluation, always selecting the same option nearly matches the multiple-choice accuracy of two B4DL-derived configurations. We introduce LiDAR-Hallu, a geometry-referenced benchmark and diagnostic protocol...
Run-Yi Yang, Murat Akkoyun, Di Wen et al.· 0 citations
Low-bit vision-language-action inference must reduce observation-to-action latency while preserving robot behavior. We present FoldQuantVLA, a post-training quantization framework that carries a consistent activation representation through calibration, weight rounding, and native integer execution. It combines channel...
Hung T. Ho, Khanh-Binh Nguyen, Quan Nguyen et al.· arXiv.org· 1 citation
Tactile-JEPA is an efficient self-supervised pre-training method that uses the spatial arrangement of tactile sensors to learn topology-aware representations, and is trained to predict the embeddings of masked sensing elements from the unmasked remainder, using the sensor connectivity graph to guide spatial masking.
E. Kovtun, M. Konovalov, Andrey Sakhovskiy et al.· 1 citation
This work presents vla.simd, a CPU inference engine that combines shared SIMD micro-kernels, reusable computation, and target-specific optimization, and introduces IMPACT, an ACT-based policy with cached text representations and language-modulated visual features.
Khanh Duy Nguyen, Hoang M. Truong, A. T. Le· 0 citations
The ActiveArena-Sim, an active-perception simulator with controllable viewpoints and large-scale workspaces as the foundation, is introduced, and ActiveArena-Bench is proposed, which comprises 35 tasks across 5 fine-grained categories, covering visual exploration and interactive information acquisition.
Yi-Bo Li, En-Shen Zhou, Rui Chen et al.· 2 citations
ReVeal is presented, a real-to-sim assessment framework combining workspace reconstruction, reconstruction-level assessment, and matched closed-loop policy evaluation that develops PGSR-D, a reconstruction pipeline incorporating monocular depth supervision to improve geometry where multi-view visual cues are limited.
Xin-Yi Wang, Heng Hao, Wen-Jun Hu 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.
