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 96
A Physics-Informed Collision Learning Framework for Collaborative Robot Motion Generation
Close-proximity multi-arm manipulation requires collision models that are both geometrically accurate and differentiable enough for real-time optimization.LeWAM: A JEPA World Action Model with Diffusion-Steering-Based MPC
World action models (WAMs) predict actions and future observations, typically from a reconstruction-based representation that carries noisy, redundant information which can complicate downstream…GLIO2: A GPU-Parallelized Tightly-Coupled LiDAR-Inertial-GNSS System for Robust and Real-Time Global Localization and Mapping
Globally consistent, real-time state estimation in large-scale, perceptually degraded environments is essential for autonomous vehicles and aerial robots, and requires fusing LiDAR, inertial, and…RoboRSI: Stable, efficient, and reusable robot self-evolution in complex real-world environments
A generalist robot should not only perform diverse tasks but also improve through experience, turning what it learns during execution into capabilities that later tasks can reuse.Control-Ready Uncertainty for Trajectory Diffusion
Diffusion models can represent complex, multimodal trajectory distributions, but extracting uncertainty from them typically requires costly Monte Carlo sampling.FAITH: Feasibility-Aware Safety-Filtered RL for High-Dimensional Systems
Safe reinforcement learning commonly places safety and task performance in the same policy objective, where they can introduce competing updates.VioLA: Learning Generalist Humanoid Control Policies from Human Data
Teaching a humanoid to follow instructions with its whole body runs into two obstacles.Generative Neural Retargeting for Human-to-Robot Dexterous Manipulation
Human demonstrations are a scalable data source for learning dexterous manipulation, but the embodiment gap prevents human motion from being executed directly on robots.VersaCamVLA: Camera-Configurable VLA Policies for Robotic Manipulation
Vision-Language-Action (VLA) models have emerged as powerful foundations for robotic manipulation, but their reliance on fixed camera configurations during training makes them brittle to changes in…SpatialHarness: Test-Time Spatial Scaffolding for Fine Robotic Manipulation
Frontier multimodal foundation models (e.g., GPT-6 Astra) have recently shown strong potential for direct robotic control, yet their performance on fine manipulation remains limited.A Balanced Data Diet: Addressing the Exploration Bottleneck in Mega-Scale RL for Robot Control
General-purpose robots must perform a wide range of tasks from agile locomotion to dexterous manipulation.CSF: Contextual Safety Filtering for Motion Generators
Text-conditioned motion generators produce trackable whole-body motion, but they have no notion of scene-dependent safety: the same action may target an object or a person.DreamTrue: Action-Faithful Robot World Model with Counterfactual Post-Training
We present DreamTrue, a multi-view, cross-embodiment robot world model for action-faithful and physically plausible video prediction.Dex-One2Many: Learning Dexterous Manipulation from a Single Human Demonstration
While learning dexterous manipulation from a single human video offers a promising alternative to costly robot demonstrations, many recent methods predominantly imitate demonstrated motions.Fly-by-Code: Embodied Coding Agents for Aerial Manipulation with Active Visual and Physical Feedback
Embodied coding agents construct robot policies from natural-language instructions by generating executable programs and refining them from execution feedback.PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface
Humanoid robots have many degrees of freedom and contact surfaces, yet most loco-manipulation research restricts manipulation to the hands and avoids other body contacts with the environment.What Should Embodied Agents Remember? Benchmarking and Evaluating Memory Modality for Embodied Agents
Memory allows large language model (LLM) agents to use past experience, often stored as textual records, when responding to requests.CASTER: Formalizing Zero-Shot Manipulation from Synthetic Videos as Constrained Optimization
Prior research on learning from humans often requires real human demonstrations.Mind the Temporal Gap: Evaluating Video Models for Temporal Understanding in Driving
Modern video-language models are increasingly treated as general-purpose video reasoners, yet it remains unclear whether they robustly capture temporal dynamics in safety-critical settings such as…Graph-Based Heuristics for Minimum Constraint Removal in Cluttered Robot Motion Planning
Humanoid and service robots operating in clut- tered human environments may encounter motion planning problems that are infeasible when all surrounding objects are treated as fixed obstacles.