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robotics

1,589 papers

#robotics Conference Sep 2025

Measurement and Potential Field-Based Patient Modelling for Model-Mediated Tele-ultrasound

This work introduces a method to update the internal potential field model of the patient with measured positions, forces and torques for more transparent model-mediated tele-ultrasound and shows the addition of measurements to the model reduced the force magnitude RMSE by an average of 7.42 N.

Ryan Yeung, David G. Black, S. Salcudean · 1 citation
#robotics Preprint Sep 2026

Comparative Evaluation of an XR Pen-based Control Interface for Semi-Autonomous Mobile Robot Navigation in Service Environments

This paper presents a control interface that uses a commercial XR pen to command a semi-autonomous mobile robot in Augmented Reality (AR), and shows that the XR pen significantly outperforms the other methods in task selection time with the most consistent selections, supporting XR-based control as an intuitive alterna...

Alicia Torc, Carl Tornberg, Éric Piette et al. · 0 citations
#robotics Preprint Sep 2026

Multi-Objective Human-in-the-Loop Bayesian Optimization of a Lower-Limb Exoskeleton

This work proposes Multi-Objective Human-in-the-loop Bayesian Optimization (MO-HILBO), which builds on explicit multi-objective Bayesian optimization to efficiently infer a personalized set of Pareto-optimal controllers.

Neil C. Janwani, Matthew T. Lerner, Aaron J. Young et al. · 0 citations

NAC: Neural Action Codec for Vision-Language-Action Models

The Neural Action Codec is introduced, which treats short robot action trajectories as multi-channel 1D signals and compresses them using a multi-scale RVQGAN architecture and achieves high reconstruction fidelity and higher average success rates than binning, FAST, and prior VQ-based tokenizers at comparable or better...

Ahad Jawaid, Yunan Xiang · 0 citations
#machine learning Preprint Sep 2026

Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control

An online model-based reinforcement learning framework that learns a probabilistic dynamics ensemble model from scratch for sampling-based model predictive control and achieves higher sample efficiency than the evaluated model-based reinforcement learning baselines is presented.

Claudio Canales, Nan Fang, Marco Hutter et al. · 0 citations

CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation

Under matched encoders and training settings, CoFL-S consistently outperforms baselines across planner frequencies in the continuous-time Habitat benchmark, and zero-shot real-world closed-loop deployment further shows its advantage over the evaluated baselines beyond simulation.

Haokun Liu, Zhaoqi Ma, Yi-Cheng Chen et al. · 0 citations

Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring

This paper proposes Hide-and-Seek, a framework that formulates VLA failure detection as a coarsely supervised learning problem that achieves state-of-the-art multi-task failure detection performance with a practical accuracy--timeliness trade-off under conformal prediction, and generalizes well to both seen and unseen...

S. Park, Wendi Li, Changdae Oh et al. · 8 citations

Topology-Driven Anti-Entanglement Control for Soft Robots

A topology-driven Multi-Agent Reinforcement Learning (TD-MARL) framework to coordinate multi-robot systems to avoid entanglement is proposed and the full simulation experiments show that the method is better than the current advanced deep reinforcement learning (DRL) method in terms of convergence and anti-winding effe...

H. Le, Sheng-Xuan Wang, Mo Chen et al. · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Nov 5, 2026

Clearing up a robot’s fuzzy memory

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.

Google DeepMind Blog Oct 13, 2026

Gemini 4 Argon for Robotics

Discover Gemini 4 Argon for robotics: leading the ERQA benchmark with physical reasoning, zero-shot control, Real2Sim, and agentic workflows.

Microsoft Research Blog Sep 23, 2026

Offloaded inference for real-world physical AI robotics

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.

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