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 221
Identifying Habit, Physics, and Nuisance in Robot World Models
Teleoperated demonstrations are often multimodal even when the underlying dynamics are nearly deterministic given the executed action.Geometry Conditioning in an Embodied SLM: Training Controls and Robustness Diagnostics in a 0.8B Hybrid Model
We study how physical-state inputs affect a 0.8B hybrid language model adapted for manipulation with 6.2M trainable parameters.Closed-Loop Evaluation of Bird's-Eye-View Maps from Cross-View Transformers as Inputs to Behavior-Cloning Policies
In autonomous driving, Bird's-Eye View (BEV) representations provide a structured, top-down abstraction of the vehicle's surroundings and have become a key input modality for Behavioral Cloning (BC)…FALCON-S: Fixed-wing ground-effect Aerodynamics Simulator and Flight Control Learning Suite
We present a modular, high-fidelity simulation framework for the development and benchmarking of flight control strategies in fixed-wing aerial robots operating near the ground.EgoNeMo: Transferable Map of Pedestrian Dynamics via Egocentric LiDAR Scan
This paper proposes a transferable Map of Dynamics (MoD) framework that generalizes to unknown environments using only egocentric 3D LiDAR point clouds to overcome the long-standing limitation of…MobileVLA-R1 2.0: RL-Enhanced Reasoning for Mobile Robot Control
Grounding natural-language instructions into reliable and executable actions remains a fundamental challenge for vision-language-action (VLA) systems on mobile robots, due to the persistent gap…Bundle Length Tradeoffs in Decentralized Multi-Robot Task Allocation Under Degraded Communications
Bundle length B is commonly fixed when configuring multi-task multi-robot task allocation (MRTA) algorithms.IM-ENGINE: Image Editing for Embodied Data Generation
Learning-based manipulation requires supervision that is both semantically meaningful and physically executable, but current data pipelines often provide only one of these properties.CST-WM: A Causally Structured World Model for Embodied Visual Tracking
Embodied visual tracking requires a robot not only to react to the current view, but to choose actions that preserve or recover future evidence of a moving target under ego-motion, occlusion, and…A Schema Bounded Language Model for Refining Robot Policies Without Destabilizing Local Learning
This paper addresses navigation by composite heterogeneous robots in a decentralized system when policy reasoning and local control operate at different update levels.RoboSPA: Can VLA Models Go Beyond Simple Scenes and Short-Horizon Tasks?
Vision-Language-Action (VLA) models have shown promising progress in language-conditioned robotic manipulation.Towards Neuro-Symbolic Procedural Reasoning for Long-Horizon Vision-Language-Action Manipulation
Vision-language-action (VLA) models can execute short manipulation skills, but remain brittle in long-horizon procedures requiring persistent task state, dependency-aware reasoning, conditional…What Matters, When? Diagnosing and Improving Conditional Visual Grounding in Visuomotor Imitation Policies
Visuomotor imitation policies can achieve high performance under in-distribution visual conditions yet fail when visually similar objects or receptacles are introduced.CrossDepth: Geometry-Constrained Attention for Generalizable Multi-View Surround Depth Estimation
Reliable 3D understanding of the surrounding environment is a core requirement for autonomous driving.Same Trajectory, Contradictory Rewards (ROBORMBENCH): Paraphrase Fragility in Vision Language Reward Models
Vision-language models are increasingly used as reward functions for robotic learning, but this role requires paraphrase invariance: the same trajectory should receive the same reward under…GE-Act 2.0: Pretraining and Scaling a World-Action Model for Robotic Manipulation
World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction.Adaptive Cost-Sensitive Machine Learning for Autonomous Robot Navigation Failure Prediction: When Not All Errors Are Equal
Autonomous robot navigation failures differ not only in categorical severity but also in the physical context in which they occur.Time-Aware Assistive Navigation
Can interactive vision-and-language agents learn not just what to say but also \textbf{\textit{when}} to say it?