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 191
RTK-Vision PPO for Autonomous Micro UAV Recovery on an Airborne Carrier
Autonomous recovery of a micro unmanned aerial vehicle (UAV) onto a moving airborne carrier enables reusable deploy-mission-recover operation, but couples long-range rendezvous, close-range…TraceFlow: Guiding Frozen Flow-Matching Robot Policies with Success and Failure Traces
A vision-language-action (VLA) policy with a flow-matching action expert generates each action chunk (a short command sequence) by integrating a learned velocity field; once its weights are fixed…SkipVLA: Skipping VLA Steps with Classical Planning for Fast Robot Manipulation
Vision-Language-Action (VLA) models are a class of generalist robot policies that map camera images and language instructions directly to robot actions.DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation
Learning predictive models of contact-rich dexterous manipulation requires dense tactile interaction, but such data are costly to scale on real robots and remain tied to embodiment-specific sensors.HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface
Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains challenging.Learning Foresight without Explicit Trajectories for 3D Diffusion Policies
3D diffusion policies are strong at generating geometrically grounded actions from current observations, but successful manipulation requires not only knowing what motion is feasible now, but also…MAGNETAR: Multipath-Guided Spatial Posteriors for Transmitter Pose Inference in the Upper Mid-Band
Robots that localize a radio transmitter need more than a point estimate: in cluttered rooms, one measurement is often consistent with several transmitter locations and, because upper-mid-band…FunArt: Decoding Functional Structure and Articulation from Generative 3D Latents
To operate effectively in human environments, robots must identify articulated objects, segment their movable and interactive parts, and estimate their kinematic models.Towards Scaling Marine Perception with Synthetic Data
Scalable machine learning in challenging underwater environments is strongly limited by the lack of labeled real-world training data.Custom PX4 firmware for autonomous hybrid aerial-marine missions
Mapping and monitoring aquatic environments can benefit from hybrid aerial-amphibious drones able to combine flight and water-surface navigation within the same mission.HOPHY: A Hierarchical Hypergraph Representation for Off-Road Path and Mission Planning
Mission-level autonomy for disaster response, search and rescue, and tactical UGV operations requires repeated path and mission planning as terrain conditions, agent types, and objectives change.MoWAM: Explicit Future Motion Prediction for Efficient World Action Models
World Action Models (WAMs) improve robot policy learning by incorporating future dynamics, yet explicitly generating future videos at inference introduces substantial computational overhead.Underwater Visual Target Tracking with Target-Specific Depth Estimation and Adaptive Model-Fusion Predictive Control
Vision-based underwater target tracking is challenged by unreliable depth measurements and unknown target motion.MILER: Semantic Mid-Level Representation for Sim-to-Real Reinforcement Learning in Unstructured Autonomous Driving
Reinforcement learning constitutes a promising approach owing to its potential for superhuman performance and self-learned policies.OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving.Agile-WAM: An Agile Tactile World Action Model for Contact-Rich Robot Control
World Action Models (WAMs) advance beyond conventional visuomotor policies by jointly predicting future world states and robot actions, enabling the policy to learn phys- ical dynamics that support…GeoAAC: Geometry-Based Adaptive Action Chunking from Denoising Trajectories in VLA Policies
Action chunking is widely used for action generation and execution in Vision-Language-Action (VLA) policies, yet existing approaches commonly use a fixed action horizon.StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation
Hierarchical planning frameworks combine skills from multiple robot control policies for long-horizon task execution, where determining when to terminate the current skill and advance to the next…FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations
Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence.Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision
Complex robotic manipulation tasks frequently require a long-term memory of past events and actions.