This work pretrain MM-ABC, a foundation model built around Seeing, Coordinating, and Imagining Arm-Base Collaboration, which combines sparse multi-level VLM features for spatial perception; a training-only future branch that uses world imagination and geometric intent as extra supervision, strengthening perception and...
Qi-Wei Liang, Guang-Yu Chen, Shao-Long Zhu et al.· 0 citations
MoPA is presented, a framework that aligns perceptual conditioning with mobility and manipulation while preserving coordination at the action level, and achieves state-of-the-art performance across all three task suites.
Guang-Yu Chen, Qi-Wei Liang, Shao-Long Zhu et al.· 2 citations
GeoLAM, a framework for learning geometry-grounded latent actions from action-free human videos, combines future-frame reconstruction through a frozen geometric feature hierarchy with motion supervision from a training-only 4D geometry teacher and requires neither the geometry teacher nor future-video generation.
Yi-Fan Xie, He-Kun Tian, Jin-Kun Liu et al.· 0 citations
PACT-WAM is introduced, a world-action model that jointly generates a 16-step action trajectory and its temporally corresponding visual forecast through conditional flow sampling, andlations show that hierarchical history allocation and joint action-visual generation improve control success.
Yu-Shan Liu, Jing-Jing Fan, Shou-Jie Li et al.· 3 citations
CheckVLA is proposed, which verifies execution with a separately trained, frozen action-conditioned world model, and results support action-conditioned verification as a way to restore feedback during chunked execution while keeping the repair consistent with inference latency.
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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