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 75
OccWAM: Occupancy World-Action Models with Joint Scene-to-Ego Flow Matching
Occupancy world models provide a structured, metric representation of future driving scenes, making them a promising basis for end-to-end autonomous driving.HelpMeOut: A Neuropsychology-Grounded Benchmark for Error-Aware Monitoring of Household Task Execution
We introduce HelpMeOut, an embodied AI benchmark for error-aware monitoring of household task execution.Where Small Mistakes Matter: Quantifying State Criticality for Imitation Learning
Imitation learning typically treats every demonstration state as equally important, optimizing a behavior-cloning objective uniformly across the dataset.The Human--VLA Communication Gap: What Humans Leave Implicit and What Policies Cannot Recover
A robot may know how to perform a task yet fail when a human asks for it naturally.I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation
Policies trained offline from teleoperated demonstrations can degrade outside their training distribution.Learning Robot Policies for Whole-Body Mobility Assistance via Dynamic Human Motion Generation
Robotic assistance for human mobility could benefit care recipients and reduce caregiver burden during physically demanding tasks such as Sit-to-Stand (STS) and Lift-and-Transfer (L&T).Autonomous 3D Visual-Thermal Inspection for Utility-Scale Solar Farms
Utility-scale photovoltaic (PV) inspection is dominated by aerial surveys, which are effective for identifying module-level hot cells but provide limited visibility of critical components located…DexStitch: Learning and Composing Dexterous Skills from Human Videos via Contact-Aware Robotic Inpainting
A long-horizon manipulation task consists of primitive skills and the transitions connecting them.Beyond Safe Execution: Safe Online Reinforcement Learning for Autonomous Driving with Value Calibration and Adaptive Guidance
Online reinforcement learning (RL) enables driving policies to adapt to changing deployment conditions, but requires safe exploration throughout training.Beyond the End-Effector: Learning Whole-Arm Contact Manipulation from Human Demonstrations
Robot manipulation has traditionally relied on end-effector-centric interactions.Which Trajectory Is Safer? Combining Rule and Learning for Safety Evaluation
Collision rate remains a dominant metric for evaluating autonomous driving trajectory safety, but its binary nature obscures substantial safety differences among collision-free trajectories.Physics-Aware Object-Centric SE(3) Forecasting for Real-Time Proactive Dynamic Grasping
Proactive dynamic grasping requires robots to predict the future state at which physical contact will occur, rather than reacting to the currently observed object pose.LAG-IO: Multi-IMU State Estimation for Legged Robots Using Factor Graphs
Robust proprioceptive state estimation is crucial for legged robots, particularly in challenging environments where exteroceptive sensors are unreliable.Information-based Unified Design Framework for NMR Magnetic Tracking System
Nuclear magnetic resonance (NMR) magnetic tracking offers a promising alternative to magnetic resonance imaging (MRI)-based localization for medical robotic guidance.Agentic Robot Creation through Generation, Verification, and Revision
Can an AI agent turn high-level user intent into a functioning robot and teach it how to behave?DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving
Vision-Language Models (VLMs) and world models have demonstrated strong capabilities in semantic understanding and future scene evolution for autonomous driving.WoVR+: World Model as Reliable Simulator for Versatile VLA Policy Optimization with RL
Action-conditioned world models can reduce costly real-robot interaction in reinforcement learning for Vision-Language-Action models (VLA-RL), but hallucinations can mislead policy optimization.GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation
Autonomous navigation in real-world environments often requires long-horizon operation under partial observability and evolving goals.Locomotion with Online Awareness of Dynamics for Precision Footholds
Unknown payloads alter robot dynamics and make precise contact execution difficult when foothold margins are small.ReEnvision: Future-Guided Selective Action Repair for Long-Horizon Manipulation
Long-horizon robotic manipulation requires robust execution over sequences of interdependent actions.