Simulation and real-robot experiments show that PRISM preserves or improves general-task performance while raising mean end-to-end success from 38.3% to 86.7% across drive connector insertion, bimanual connector mating, and screw tightening, substantially extending the precision boundary of existing embodied policies w...
Cheng-Hao Yue, Pei-Yan Li, Hao Wang et al.· 0 citations
Vision-Language-Action (VLA) models have become a central paradigm for robot policy learning, which predict actions in three forms: raw action chunks, discrete action tokens, or continuous action latents. However, existing action representations primarily model action trajectories, with limited consideration of the vis...
Yuan Xu, Yi-Xiang Chen, Qi-Sen Ma et al.· 0 citations
Constructing action targets from measured robot motion is an established approach in imitation learning. Under interaction constraints, however, command-state discrepancy may reflect control demands that motion alone does not capture. We investigate when this information matters and how to exploit it. Across three real...
Pei-Yan Li, Yue-Ran Tao, Enhao Zhang et al.· 0 citations
BridgeVLA++ is developed by equipping BridgeVLA with a unified spatio-temporal memory architecture that models persistent spatial context and temporal interaction history that can reason over observation histories while preserving BridgeVLA's data efficiency and generalization capabilities.
A systematic shortcut audit of EmoPrefer using content-blind probes shows that the current scores can be reached without verifying either description against the video, and recommends source-balanced pairing, strict length control, counter-stereotypical sliced reporting, and multi-annotator consensus for future cross-g...
XEWorld is introduced, a controlled cross-embodiment testbed for world models that isolates embodiments by evaluating held-out robots within physically identical scenes, highlighting that achieving true cross-embodiment generalization requires architectural innovations that decouple visual appearance from underlying ph...
Yixiang Chen, Jiabing Yang, Yuan Xu et al.· 0 citations
Xiao-Robotics-1 serves as a strong robot foundation policy that can be efficiently fine-tuned on complex, dexterous tasks with high data efficiency and across multiple simulation benchmarks, Xiaomi-Robotics-1 outperforms state-of-the-art methods.
Xiaomin Guo, Piao-Piao Jin, Jason Li et al.· arXiv.org· 16 citations· ⚡2
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