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 89
What Visual Representations Do World Action Models Need?
World Action Models (WAMs) inherit powerful priors from pretrained video generation models, but their variational autoencoder (VAE) representations are optimized for visual reconstruction rather…Failure-Resilient Multi-Robot Coordination with $k$-Connectivity Constrained Submodular Maximization
Multi-robot coordination is essential in applications such as coverage, target tracking, and information gathering, where robots must jointly select actions to maximize team performance.Towards Safer Cities: A Design Process for Perception Datasets in Urban Driving Scenarios
Urban environments present significant challenges for the perception systems of automated driving due to their high complexity, characterized by dense traffic, diverse obstacles, or a wide range of…VICAR: Video Imitation with Contact-Aware Retargeting for Humanoid Interactive Skills
Third-person human videos offer a scalable alternative to task-specific reward engineering, teleoperation, and manually scripted demonstrations for humanoid loco-manipulation.Belief-Overlap Partial Inversion: Object-Centric Flow Replanning for Humanoid Loco-Manipulation
People often learn how an unfamiliar object moves through a small exploratory push, using its response to guide their next motion.ReAT: Residual Action Tokenization for Autoregressive Robot Policies
Autoregressive robot policies rely on action tokenizers to convert continuous action chunks into compact sequences of discrete prediction targets.Forward Dynamics Models
Vision-language-action (VLA) models and world action models (WAMs) have advanced robotic policy learning by building on pretrained vision-language models (VLMs) and video generative models (VGMs)…The value of information and the emergence of communication in evolved e-puck robots
We study when communication emerges between autonomous agents and what role an innate action selection architecture plays.Human2Dex: Dexterous Robot Learning from Wrist-Centric Human Demonstrations
Collecting dexterous manipulation demonstrations for each robot hand requires repeated task-specific data acquisition.Low-Latency Mapless ESDF Queries for MAV Reactive Collision Avoidance
Reactive collision avoidance in unknown, cluttered environments must perceive obstacles and correct motion within milliseconds.Grounded-Dreamer: Robot Learning from World Model Synthetic Data with Physical Grounding
Scaling robot manipulation policies requires large amounts of diverse, action-labeled data, yet collecting such data in the real world remains expensive and difficult to scale.Prediction-Informed Landing Window Planning for UAV Landing on USV in High Sea States
Autonomous landing of unmanned aerial vehicle (UAV) on unmanned surface vehicle (USV) is difficult in high sea states.PATACON: GPU-Parallel Tangent-Bundle RRT-Connect for Constrained Motion Planning
Fast whole-body motion planning enables continuous replanning for humanoid robots in dynamic environments while satisfying collision avoidance and kinematic constraints.Beyond Actions: Learning Future Operation Targets for Vision-Language-Action Models
Actions specify what a robot should execute, but do not describe what those commands will achieve.Dual-Stream Memory for Vision-Language-Action Policies
Memory-dependent manipulation requires a policy to track execution state and recover visual evidence that is no longer observable.Sonic-AMG: Streaming Human Motion Generation from Spatial Audio
Modeling human reactions to spatial audio requires understanding both what is heard and where it comes from.NovaSplat: Model-Free 6D Object Pose Estimation via 3D Gaussian Splatting Onboarding and Refinement
6D pose estimation of novel objects without CAD models remains an open challenge: model-free methods must build the object representation from scratch during onboarding and have long trailed their…Evo-RL: Iterative Robot Reinforcement Learning from Selective Experience
Existing real-world reinforcement learning methods improve robot policies by combining human corrections with autonomous rollouts.AlphaNIST: Emergent Assembly Skills from Binary Task Rewards
The NIST Assembly Task Board #1 remains a challenging benchmark for learning-based methods in robotic manipulation.PartiGen: Part-Aware Representations for Language-Conditioned Humanoid Motion Generation
Translating language into humanoid motion requires coordinating body parts, determining motion duration, and producing references suitable for robot execution.