Applied & Human-Centered · Computer Science & AI
Robotics
The newest papers in Robotics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
4,418 papers, from 4 September 2026 to 5 November 2026: 4,418 arXiv preprints.
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Newest papers, page 78
UniW(A)M: A Unified Mixture-of-Experts Framework for World and World-Action Models
World models (WMs) and world-action models (WAMs) focus on future world prediction and executable action generation, respectively.RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories
Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world models.Steertuning Policies from a Few Human Videos
Current recipes for robot learning require massive amounts of robot demonstrations to obtain robust and adaptive policies.GreenWAM: A World Foundation Model for Robotics
A robot needs a world model that predicts how its actions will change the scene and a policy that chooses those actions.EqC: Equilibrium Energy Control for Introspective Lifelong Robot Learning
Long-term robot autonomy depends on more than effective action generation: a policy must recognize when its behavior is no longer sufficiently supported and use assistance to improve.SEAR: Self-Evolving Agentic Robotics
Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for the next task.Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning
In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives.EgoMatrix: Physically Faithful Dexterous Robot Demonstrations from Egocentric Human Videos
Egocentric human videos offer a scalable alternative to robot teleoperation for collecting dexterous manipulation data.WorldOracle: Benchmarking Action-Conditioned World Models against Real-World Executions
We introduce WorldOracle, a benchmark specifically designed to evaluate action-conditioned world models (ACWMs) against paired real-world executions.RoboSimStudio: An Interactive Toolkit for Human and Agent Rapid Real2Sim of Robot Manipulation Tasks
It has been difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics, coupled contact dynamics, deformations…Who Improves the Robot? Toward Physical Recursive Self-Improvement
Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven.LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models
Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, calibrations drift, controllers run at different…Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration
In-context robot learning enables robots to acquire new behaviors from demonstrations without test-time parameter updates.Collaborative Planning for UAV-UGV Teams under Operational Constraints via Masked Error-bounded Neuro-Symbolic Diffusion
Collaborative planning under operational constraints is essential for heterogeneous robot teams in large-scale missions.ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation
Expert demonstrations show how to complete contact-rich tasks, but provide limited coverage of how interaction forces respond to different actions.Interaction Latents as Actions for Contact-Rich Manipulation
Contact-rich manipulation requires robots to coordinate not only where to move, but also how contact and force should evolve throughout an interaction.AutoAgent0: An Agentic Runtime for Safe Closed-Loop Driving
Safe autonomous driving requires more than accurate planning at a single frame: it demands reliable closed-loop execution in dynamic environments.EmbodiedEvo: Self-Evolving Coding Agent For Physical Intelligence
Coding agents can construct executable programs and revise them through feedback, offering a foundation for embodied control without target-task fine-tuning.What Matters in Quality-Aware VLA Pretraining: A Benchmark and Empirical Study
How should demonstration quality be used during vision-language-action (VLA) pretraining to improve performance after target-task fine-tuning?EgoExplorer: A Vision-Motion-Touch Observatory for Human Loco-Manipulation In Situ
Learning humanoid loco-manipulation from human demonstrations rests on observing how locomotion, posture, and object interaction evolve together as tasks unfold.