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 144
Learning High-Risk High-Precision Motion Control
Deep reinforcement learning (DRL) algorithms for movement control are typically evaluated and benchmarked on sequential decision tasks where imprecise actions may be corrected with later actions…ECHO: Event-Augmented Context with Hindsight and Outlook for Wrist-Only Manipulation
Learning-based manipulation policies relying on RGB cameras often suffer from degraded observations under extreme exposure.Don't Throw Away the Tail: Action Upcycling for Policy Acceleration
Modern robot policies predict a chunk of future actions from a single observation, execute only a prefix, and discard the rest before replanning.Adjoint Guidance Flow: Amortized Critic Guidance for VLA Policies
Flow-based Vision-Language-Action (VLA) policies are typically trained by behavior cloning and thus do not explicitly optimize long-term task return.RoboFL: Federated Expert Assembly for World Action Models
Vision-language-action and world-action models are increasingly popular, yet remain bottlenecked by physical interaction data that is scarce, institutionally siloed, and task-heterogeneous.NavJev: Efficient Vision-Language Navigation via Action-Centric Visual Compression and Discriminative Action-Semantic Memory
Recent zero-shot Vision-and-Language Navigation (VLN) methods increasingly rely on multimodal large language models (MLLMs) to reason over visual observations, navigation instructions, and candidate…What Makes World Action Models Generalize? An Empirical Study of Test-Time Future Modeling
World action models (WAMs) predict the future alongside actions during training.ActionUNet: Improving Robustness of VLA Models with Efficient Multi-scale Fine-tuning
Vision-Language-Action (VLA) models have shown great promise for robotic manipulation by mapping multi-modal semantics to physical actions.Graph-Based Simultaneous Path and Foothold Planning for Multi-Limbed Intra-Vehicular Robots in Space Stations
Robot-aided operations in space stations are essential for reducing the workload of astronauts and improving the efficiency of on-orbit activities.Learning to Act under Visual Interruptions with Vision-Language-Action Models
Vision-language-action (VLA) models have demonstrated strong capabilities in robotic manipulation, but they are typically developed and evaluated with all camera streams available throughout task…JRDB-AVR: An Active Visual Reasoning Benchmark for Embodied Agents in Real-World Environments
In complex embodied visual reasoning scenarios, an agent often has only a limited field of view, and the evidence needed to answer a question may be distributed across time, viewpoint, and…Do Not Cut When Uncertain: Rejectable and Calibrated Decision Heads for VLA Policies in Robotic Harvesting
Vision-Language-Action (VLA) policies trained with behavior cloning or flow matching are optimized to output an action trajectory, but they cannot express "I don't know" or "I should not act.EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning
A robot should be able to learn through experiments how unfamiliar objects behave and interact, then plan with that knowledge.QuadHand: A Compact Quadrotor Aerial Manipulator with MRC-SDF-Based Whole-Body Motion Planning
Uncrewed aerial manipulators (UAMs) integrate robotic arms with aerial platforms for three-dimensional physical interaction.Task-Oriented Communications for Edge-Assisted Multi-View Localization
Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) often lose satellite positioning in urban canyons, indoor facilities, and jammed or spoofed environments, making vision-based…ReCAT: Remember, Count, and Time: Structured Recurrent Memory for Robot Manipulation
Memory-dependent manipulation requires robots to make decisions using information that is no longer available to their current sensors, such as recalling an earlier visual cue, tracking task…Zero-Shot Reactive Obstacle Avoidance for Generative Robot Policies
We propose NUDGE (Nudge Update via Differentiable GEometry), a training-free obstacle-avoidance procedure that can be incorporated in any robot policy based on diffusion or flow matching, including…Spatial Grafting: Grounding 3D Features for Flow-Matching Robot Policies
Pretrained robot manipulation policies such as vision-language-action models (VLAs) or world-action models (WAMs) leave interaction-relevant metric geometry implicit.Repair Before You Fuse: Frozen-Host Adaptation for Corrupted-but-Present Sensors
Camera-LiDAR detectors can continue to consume unreliable features even when both sensors remain present, synchronized, and calibrated.GuardPIBT: Counterfactually Gated Neural Guidance for Ultra-Large-Scale 3D Multi-Agent Path Finding
Large-scale 3D multi-agent path finding becomes increasingly difficult under dense traffic.