A 202-scenario benchmark with Reference Decisions under a seven-class taxonomy is introduced and Decision Alignment and safety-specific error metrics are evaluated, finding structured LLM decisions are insufficient as a standalone safety mechanism and deployment requires an independent enforcement layer.
Diba Afroze, Xing-Li Zhang, Ya-Zhou Tu et al.· 0 citations
This work presents EgoPHI, the first method that jointly estimates dense contact maps and 3D force distributions on hand and object meshes from a single monocular RGB image and object geometry, and demonstrates that EgoPHI improves force estimation over existing approaches while generalizing to unseen datasets.
Andela Ilic, Rachel Schuchert, Yi-Jing Jiang et al.· 0 citations
A user study examining the difference in human behavior between an HRT scenario conducted in a virtual vs real environment and found differences in participants's strategy, their mental model of robots, and the type of trust they had for robots between modalities.
Sean Dallas, Absalat Getachew, Motaz Abuhijleh et al.· 0 citations
Teaching robotics relies on screen-based simulation, showing robot motion in an abstract coordinate frame rather than at real scale in the learner's own space, while access to physical hardware is limited by cost, safety, and scheduling constraints. We present MR-Robotics LAB, a mixed-reality (MR) platform that replays...
Santiago Berrezueta-Guzman, Habiba-Loai Khalil, A. Koshelev et al.· 0 citations
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Experimental results support the study's hypothesis and indicate that incorporating this real-time feedback function can simultaneously improve task success and the user experience.
Ryo Terashima, Yuga Yano, Koshun Arimura et al.· IEEE International Symposium...· 0 citations
Rollback-Induced Reflection (RIR) is proposed, a unified recovery framework that restores execution to a selected prior state while carrying forward reusable knowledge distilled from the abandoned trajectory to guide subsequent decisions.
In this controlled setting, object-centric completion and execution-aware selection reduced approach collisions and improved the reliability of language-guided grasping for supervised field manipulation.
Dilermando Almeida, J. Negri, Guilherme Lazzarini et al.· 0 citations
We present a learnable physics-based model that predicts motion of the robot end effector and reaction force-torque in contact-rich manipulation. The model represents the end effector and the environment as interacting meshes in a graph structure, and conditions its prediction explicitly on the applied control input. I...
Zongyao Yi, Joachim Hertzberg, Martin Atzmueller· 0 citations
This work introduces a curvature-aware update law for Expected Free Energy and demonstrates it on a system identification problem in a Van der Pol oscillator and proves that Expected Free Energy has unbiased convergence guarantees for concave functions.
A. Meera, Wouter M. Kouw· IEEE Control Systems Letters· 1 citation
VLA-ULAP partitions inference across decision times, interleaving remote VLA calls with predictions from an Ultra-Lightweight Local Action Predictor (ULAP), a single ULAP that combines current views, proprioception, and executed action history to predict chunks in one pass and surpasses local acceleration alternatives.
De-Yu Cao, Ryuji Oi, Kosuke Matsushima et al.· 0 citations
This work introduces ActiveScale, a framework that advances active perception through coordinated model, data, and hardware designs, and introduces Active-perception Mobile-manipulation Platform (AMP), a robotic platform that supports active perception and mobile manipulation through single-operator teleoperation, enab...
Shuai-Tao Zhou, Kai-Sheng Pang, Wen-Xuan Song et al.· 1 citation
RecMorph is introduced, a topology-guided spatial recurrent architecture that uses recurrent sequence computation to jointly perform cross-limb communication and representation transformation and shows that topology-guided recurrent transformation provides an effective and efficient communication mechanism for Generali...
Quan-Rui Rao, Yong Liu, Xue-Ming Xiao et al.· 0 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.