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Causal Imprint is introduced, which learns future-relevant scene changes from training-only future supervision and provides predictive representations directly to the action expert without future-video rollout at inference.
MILE, a teleoperation-based data-collection system comprising the wearable MILE exoskeleton and the mechanically corresponding MILE-Tac robotic hand, and trained paired ACT and DP policies with and without tactile input on MILE-collected demonstrations for downstream imitation learning.
This work introduces InternW0, the first instantiation of the InternW physical world model series from Shanghai AI Laboratory, built around omnimodal interfaces, asynchronous multi-frequency processing, and local physical modeling under partial observations and external influences.
The FWBC-VLA is proposed, a force-aware framework that bridges task-level VLA action generation and low-level whole-body compensation control for wheeled-legged robots and demonstrates the effectiveness of the FWBC-VLA in contact-rich loco-manipulation.
The Kinematic-Aware Articulation Interface (KAI), a structured intermediate representation that captures the kinematic structure of articulated objects, is introduced, a structured intermediate representation that provides a strong inductive bias aligned with the underlying structure of articulated motion.
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