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Matching papers

Breaking the Vision-Action Shortcut: Latent Interface Training for Generalizable Robotics Foundation Models

62 upvotes · 11 SEP 2026 · Jianman Lin, Shailesh Shailesh, Zhongyi Luo et al.

This paper proposes a method called Latent Interface Training (LIT) to improve the generalization of robotics foundation models by preventing them from relying on visual shortcuts when learning to generate actions from pre-trained visual representations. This is important for robots to perform well in new, unseen environments.

continual learningroboticsvision-action modelslatent interfacesaction generationvisual conditioning

Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories

59 upvotes · 16 JUL 2026 · Xiaomi Robotics Team, Jun Guo, Piaopiao Jin et al.

This paper introduces a vision-language-action model that can perform mobile manipulation tasks in unseen environments with minimal training data, and how it can be scaled up to achieve better performance. Practitioners might care about this model for building robots that can adapt to new tasks with minimal fine-tuning.

vision-language-action modelscontinual learningroboticsaction generationtrajectory dataauto-labeling