DeCAL is presented, a physically-grounded dexterous vision-language-action model that unifies understanding, imagination and action generation for contact-rich dexterous manipulation and introduces Adaptive Visuo-Tactile Fusion that dynamically regulates tactile interactions via a contact-aware gating strategy.
Yan-Kai Fu, Ning Chen, Jun-Kai Zhao et al.· 0 citations
This paper proposes SUccessor-to-Novelty (SUN), an indicator derived from successor value functions to identify goals that are both novel and reachable and presents an adaptive goal-selection strategy that leverages these properties, and an accurate yet lightweight pseudocount to avoid the overhead of classic methods.
Wen-Yan Yang, A. Mustafin, Dominik Baumann et al.· 0 citations
This work introduces Chunk-Aligned Semantic Distillation (CASD), which derives semantic targets for entire action chunks from the current observation, robot state, and task instruction, and freezes the generator to train a policy conditioned on its predictions.
Ting-He Ding, Jia-Hao Li, He Wang· 0 citations
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AirAnchor is proposed, a new paradigm that bridges local and global spatial information through spatial anchors and integrates both into a shared navigation framework, enabling comprehensive spatial grounding for decision-making.
Shan-Wei Fan, Bin Zhang, Zhi-Wei Xu et al.· 0 citations
Simulation and real-robot experiments show that the automatically generated interaction annotations are comparable to curated annotations, generated assets provide effective sim-to-real supervision, and tabletop cousins can improve transfer beyond training on a single reconstructed scene.
Jing-Xuan Zhu, Jing-Yuan Li, Liang-Liang Chen et al.· 1 citation
Experimental evaluation on real-world data across diverse critical scenarios, representative of challenging edge cases also in urban driving, confirms the effectiveness of the proposed pipeline and its suitability to support safe and adaptive planning decisions.
Davide Malvezzi, Michele Pestarino, Vittoria Cavicchioli et al.· 0 citations
Predicting 3D consistent waypoints (3DWay) from multi-view images as generating multi-view consistent 2D waypoints followed by geometric triangulation enables explicit 3D motion specification while preserving the strong priors of pretrained VLMs.
Zi-Qin Huang, Ying-Yue Li, Chenyangguang Zhang et al.· 0 citations
A dual-layer semantic-spatial belief mapping framework that transforms transient VLM observations into persistent spatial guidance is proposed, and object-conditioned visual reasoning with conservative evidence qualification is introduced to improve observation reliability before spatial accumulation.
Jian-Qiang Xiao, Xiang Deng, Yue-Xuan Sun et al.· 1 citation
A robot that can be taught a new task from a handful of demonstrations has to work out for itself what it still cannot do, and then ask for exactly that. Interactive imitation learning takes a step in that direction by letting a policy practice on its own and calling an expert when it goes wrong. Existing methods decid...
Suyog Khanal, V. ArunKumarA., Santu Rana· 1 citation
Foundation Model-Guided Semantic and Goal-Oriented Communication (FMSGOC), a framework that uses broad visual–linguistic Foundation Model priors to mitigate overfitting and improves rate efficiency by concentrating bits on sparse, goal-aligned anchors and relying on generative foundation-model priors to reconstruct the...
Bo-Liang Liu, Wint Yi Poe, Riccardo Trivisonno et al.· ICC 2026 - IEEE Internationa...· 2 citations
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.
