Applied & Human-Centered · Computer Science & AI
Robotics
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4,418 papers, from 4 September 2026 to 5 November 2026: 4,418 arXiv preprints.
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Newest papers, page 95
UNITAS: A 3D-Native World Action Model for Embodied Manipulation
World action models (WAMs) aim to answer a coupled physical question: given a task instruction, what motion should the robot execute, and how will that motion change the surrounding world?PIER: An Evidence-Gated Execution Interface for Robotic Manipulation
Generating a plausible robot action does not establish that current observations justify its execution.SuperNav: An Agentic Navigation System for Any Task in Any Scene
General-purpose service robots need navigation systems that can handle diverse human requests in unfamiliar environments, combining task generality with scene generality.Leveraging Human-In-The-Loop Demonstrations in Reinforcement Learning for Digital Twin-Driven Robot Flexibility
Growing automation makes collaborative robots work in more variable environments, increasing the need for adaptation.Instance-anchored interaction evidence: Grounding robot plans in human pointing and handling
A robot that assists people must often act on what a person has shown rather than said: which of several identical cartons was pointed at, or which box was handled.Unifying Policy Learning and State Prediction through Spatial Language Modeling
Learning how actions change scene geometry can provide complementary supervision for goal-directed manipulation.RESETTLE: Robotic Recovery through Disagreement-Triggered Retrieval and Efficient Corrective Control
Reliable robotic manipulation requires timely intervention to correct emerging deviations and restore progress after execution errors.MiniWAM: Learning Compact Future Targets for Efficient World-Action Modeling
World modeling has emerged as an effective co-training objective for robot policies, giving rise to World Action Models (WAMs) that jointly predict actions and future states.MAP2: Model- and Acceleration-Based Pursuit with MPC and Gaussian Process Residual Learning for Autonomous Racing
Autonomous racing requires accurate trajectory tracking near the handling limits of a vehicle while maintaining low computational latency.Sim-to-Real RL for ASVs using SysID
Autonomous Surface Vehicles (ASVs) operating in dynamic marine environments require robust control policies for tasks such as path following and station keeping, making reinforcement learning (RL) a…Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning
Learning from demonstration has enabled impressive robot behaviors.From Language to Motion: Task-Conditioned Focal-Stack Trajectory Integration for Microscopic Robots
Microscopic robots require accurate task geometry despite changes in language, parts, and focus.Fixed-Reference Pose Residuals for Measuring Cross-Dataset Cue Transfer in Human-Robot Interaction Anticipation
Social and service robots in public spaces need to anticipate which nearby person is about to approach and touch them, so that a response can be prepared before contact.Real-Time Motion Planning with Dynamic Hazards: Classical vs. Learning-Based Methods
We study real-time motion planning in dynamic hazard fields through a controlled comparison between classical planning and learning-based methods.Walking on Roofs: Exploring the Potential of Walking Robots for Construction Work on Roofs
This paper investigates the feasibility of deploying quadruped walking robots for the automation of work in roof environments.Toward Lunar Legged Robots: Field Deployment Lessons at LUNA
Legged robots are promising candidates for future lunar surface missions because they can traverse steep, loose, and obstacle-rich terrain that challenges conventional wheeled rovers.PLaW-VLA: Predictive Latent World Modeling for Vision-Language-Action Policies
Learning to predict how the world evolves can provide vision-language-action (VLA) policies with predictive context for long-horizon control, but its effectiveness depends on what future…LiteNWM: Efficient Latent World Models for Onboard Visual Navigation in the Wild
Direct visual navigation policies generate trajectories efficiently but do not explicitly evaluate their future consequences.Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement
Most robot policies keep a model in the control loop: a VLA maps observations to actions, and an Agent Harness, such as Agent-as-Policy or Harness VLA queries a VLM for decision making at run time.ARC: A Reasoning Recipe for Robot Foundation Models
The prevailing approach to improving robot foundation models (RFMs) relies on larger models, more robot demonstrations, and costly training at scale.