A Unified Visual-Tactile-Action Model is proposed that maps both embodiments into aligned tactile and action representations and jointly predicts future action and tactile trajectories and achieves an average success rate of 70%, outperforming the strongest visual-tactile baseline, which achieves 29%, and an architectu...
Wen-Qiao Li, Qian-You Zhao, Jia-Wen Hao et al.· 0 citations
Autonomous driving requires \textit{world models} that can understand the physical world, reason and plan, and operate safely. In this paper, we first systematically evaluate existing action-conditioned joint-embedding predictive architecture (JEPA) world models, including LeWM, DINO-WM, and JEPA-WM for end-to-end auto...
Hao-Ran Zhu, Wan-Cong Zhang, Yann LeCun et al.· 0 citations
Robotic-GST is presented, a geometry-aware spatio-temporal behaviour representation and evaluation framework that constructs a Gaussian-SAM robotic environment for real-to-sim policy verification and improves the reliability of real-world manipulation deployment.
Si-Chao Liu, Ze-Kun Wang, Li-Xuan Tang et al.· 0 citations
This work uses a frozen action-conditioned world model to predict action-conditioned consequences and construct feedback based on consistency between the two future predictions and alignment with the terminal goal to show that cross-model prediction discrepancy can provide useful feedback for improving robot policies u...
Yu-Heng Qiao, Zi-Ran Wei, Xiao-Hang Wang et al.· 0 citations
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CodeActionBench is introduced, a benchmark of 25 manipulation tasks that evaluates this capability through agentic Code-as-Policy and provides a controlled testbed for measuring how general-purpose models translate their capabilities into manipulation behavior and for examining typical failure scenarios in that process...
Yiheng Lyu, Xueying Jiang, Wen-Hao Li et al.· 3 citations
This work introduces eVTA, which learns success probabilities from mixed-quality policy rollouts through temporal-difference-style bootstrapping, without expert demonstrations or intermediate annotations, and introduces RL with Evolving Rewards (RLER), a closed-loop framework that adapts eVTA using newly collected roll...
Duo Wu, Hai-Feng Wang, Rongwei Lu et al.· 1 citation
The results demonstrate the feasibility of harness distillation for robotics: intervention experience can be accumulated, refined through execution, and reused across agents to improve manipulation.
Seungyeon Kim, Junhoo Lee, Minkyu Kim et al.· 1 citation
World Action Models (WAMs) are becoming increasingly important and useful for embodied intelligence, as they enable robots to anticipate the consequences of candidate actions before interacting with the physical environment. However, underwater robots are usually subject to passive dynamics, such as inertia, buoyancy,...
Cun-Hao Zhu, Yi-Feng Wang, Dong-Liang Xu et al.· 0 citations
VehDyn provides a systematic foundation for developing driving world models that are physically consistent and visually realistic, and benchmarks 12 state-of-the-art video world models.
Tian-Yi Wang, Wang-Sheng Du, Jia-Zhou Chen et al.· 0 citations
A symmetric Dual-Arm Expert (DAE) architecture built upon a shared Vision-Language Model (VLM) backbone with decoupled, arm-specific expert towers is proposed, providing preliminary evidence of emergent skill generalization from single- to dual-arm tasks (as well as the reverse), together with cross-arm motion-domain s...
Yong-Shen Zhao, Han Gao, Bao-Ping Cheng et al.· 0 citations
DPP enables real-time dynamic manipulation on a single consumer GPU without additional training on dynamic data and constructs a counterfactual observation that places a predicted target position in a familiar robot context, allowing the model to invoke an existing manipulation skill rather than generate a recovery beh...
Sunwoo Park, Won-Sang Lee, Seonghyun Jin et al.· 1 citation
A new system identifies and corrects unreliable object descriptions stored in a robot’s memory, improving the robot’s ability to interact with its environment.
Robots are getting smarter, but how can their hardware match that growth? New Microsoft Research findings show that moving AI inference beyond the robot can improve task success, boost efficiency, and support more advanced physical AI workloads. The post Offloaded inference for real-world physical AI robotics appeared first on Microsoft Research.
