Vision-Language-Action (VLA) models that couple pretrained Vision-Language Models (VLMs) with continuous action experts have achieved strong manipulation performance, yet generalization to out-of-distribution (OOD) language instructions remains poor. A known challenge is the structural imbalance in VLA data, where language is far less diverse than visual and action content, making policies prone to visual shortcuts. While discrete-action methods mitigate this through vision-language co-training, continuous action experts lack such protection: they start from random initialization and learn entirely from imbalanced data, producing noisy gradients that corrupt the VLM and fail to exploit its language capability. We address this from a Bayesian perspective, factorizing the policy into a language-agnostic Vision-Action (VA) prior and a language-conditioned VLA likelihood, and propose APT, a two-stage training method emphasizing Action expert PreTraining. In Stage 1, the action expert is pretrained as a VA prior on vision-action pairs from a frozen VLM, bypassing the language imbalance. In Stage 2, language tokens are injected through a gated fusion mechanism that integrates VLM features while preserving the learned visuomotor prior. APT applies to mainstream VLA architectures, including the $π$ and GR00T-style architectures. Comprehensive experiments validate that APT achieves consistent gains on unseen instructions and compositional tasks. Project Page: https://xukechun.github.io/papers/APT/
UniJEPA: Enhancing Robot Policy via Unified Continuous and Discrete Representation Learning
Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China.（清华大学交叉信息研究院）；Shanghai Qi Zhi Institute, Shanghai, China（上海启智研究院）；Peking University, Beijing, China（北京大学）；Shanghai AI Lab, Shanghai, China（上海人工智能实验室）
Progress-Think: Semantic Progress Reasoning for Vision-Language Navigation
School of Information, Renmin University of China（中国人民大学信息学院）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（北京未来区块链与隐私计算先进创新中心）；Horizon Robotics
ST-Booster: An Iterative SpatioTemporal Perception Booster for Vision-and-Language Navigation in Continuous Environments
Robotics and Control Laboratory, the School of Advanced Manufacturing and Robotics, and the State Key Laboratory of Turbulence and Complex Systems, Peking University（机器人与控制实验室、先进制造与机器人学院、湍流与复杂系统国家重点实验室，北京大学）；Defense Innovation Institute, Academy of Military Sciences（国防创新研究院，军事科学院）；Tianjin Artificial Intelligence Innovation Center（天津人工智能创新中心）；Harbin Institute of Technology（哈尔滨工业大学）
