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 137
V-JEPA Policy: Building Effective World-Action Models on Predictive Visual Latents
World-action models (WAMs) couple future visual-state prediction with action generation.Recovering the View: Benchmarking Physical Active Vision for Occlusion Recovery in Robotic Manipulation
Physical active vision allows robots to change their viewpoint when task-relevant observations become unreliable, yet existing manipulation benchmarks provide limited support for studying how…Why Cross-Skeleton Retargeting Is Non-Identifiable: Structural Limits of Generative Motion Models
Cross-skeleton motion generation trains generative models to carry action structure and motion intention from one body to another.Remember What You Did: Action-History Memory with Dual-Expert Denoising for Long-Horizon Vision-Language-Action Policies
Vision-language-action (VLA) models have driven rapid progress in robotic manipulation, demonstrating strong fine-grained control and promising performance on long-horizon tasks.Battery-Aware Reinforcement Learning for Aggressive Quadrotor Flight
Agile flight tasks such as drone racing and pursuit-evasion require strong acceleration and precise turns, but the available thrust changes as the battery discharges and voltage drops under load.Taming VLAs under Robot Execution Errors: Self-Compensation and Stress Testing
Vision-language-action (VLA) policies often fail when a robot's executed motion deviates from their commanded action.DROM: A Language-Guided Diffusion Framework for Multi-Skill Robotic Manipulation
Learning robust manipulation policies for diverse, long-horizon tasks from limited demonstrations remains a fundamental challenge in robotics.Encore: Few-Shot Agentic Discovery of Manipulation Strategies
Coding agents can now write, run, and debug programs with little human help.Direct Experience World-Model Optimization: Learning the World Beyond Action Imitation
World-Action Models (WAMs) couple action generation with predictions of how physical interactions unfold.Towards Spatial Perception for Heterogeneous Robot Collaboration in Subterranean Mining Environments
The autonomous extraction of deep mineral deposits in abandoned underground mines is fundamentally a multi-agent integration problem.FP2: Equipping Robotic Foundation Models with Force Control
Robotic foundation models (RFMs) are increasingly capable of general-purpose manipulation, yet reliable physical interaction remains challenging in contact-rich settings.Anisotropic Representations Improve Planning in JEPA World Models
Latent world models learn action-conditioned dynamics in representation space and often score candidate actions by Euclidean distance to a goal representation.Video2STL: Grounding VLM-Generated Temporal Specifications for Robot Learning
Video-based policy learning is particularly promising, as it illustrates target behaviors without requiring action annotations or embodiment-matched demonstrations.RawVLA: Embodied Neural Image Signal Processor For Robotic Manipulation
Vision-language-action (VLA) models typically operate on RGB images produced by a fixed camera image signal processor (ISP), leaving the imaging pipeline outside the learning and evaluation loop.Wrench-ACT: Enhancing Robot Policies for Contact Rich Behavior Using Direct Wrench Control
While contact-rich manipulation requires deliberate regulation of interaction forces, recent approaches to robot manipulation learning predominantly represent actions as target positions or poses.Risk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning
Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructions are ambiguous, unsupported by the…RoboFin3D: A Sim-to-Real Platform for Robotic Surface Finishing
Grinding and sanding are fundamental processes in industrial robotic surface finishing.RoboHarn-Evo: Evolving Hierarchical Physical Knowledge for Self-Improving Robotic Manipulation
Vision-language models can coordinate long-horizon robot manipulation, yet successful task reasoning still depends on whether local physical interactions produce the intended effects.Credit-Guided Policy Improvement for Test-time Adaptive Vision-Language Navigation
Test-time adaptation for vision-language navigation (TTA-VLN) enables pretrained policies to adapt online to unseen environments using only test-time observations and interaction history.BlenDAgger: Blended Shared Control for Interactive Imitation Learning
Robot policies are frequently trained from human corrections, yet teleoperating a robot to provide corrections is burdensome, and human demonstrators are not always optimal.