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 139
EgoAlign: Bridging the Human-Humanoid Gap for Long-Range Loco-Manipulation
Egocentric human demonstrations offer an accessible source of task experience, but differences in body scale and controller response, together with missing robot states, limit their value as…A QCQP-Representable IMU Pre-Integration Factor for Certifiable State Estimation
We propose a QCQP-representable IMU pre-integration factor that enables certifiable estimation with pre-integrated inertial measurements.Pow3R-SLAM: Real-Time RGB-D SLAM with 3D Reconstruction Priors
We present Pow3R-SLAM, a real-time RGB-D simultaneous localization and mapping (SLAM) system that uses Pow3R for tracking and mapping.EVO-WAM: Evolving World Action Models through Video-Action Verification
Improving robot policies on new tasks without collecting additional expert demonstrations remains a central challenge in robot learning.WorldLine: Action-Driven Visual Simulation for Robotic Manipulation
Real-world robot learning is constrained by the cost of collecting experience and evaluating candidate behaviors.MotorMind: Scaffolding General Vision Language Models for Zero-Shot Robot Manipulation
Vision-language-action (VLA) models have advanced robotic manipulation, but their zero-shot generalization in new tasks and environments remains limited, and their reliance on specialized training…CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments
Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to imitate, a pose to reach, or a reward to…FORM: Robot Manipulation through Direct Material Law Identification
When interacting with an unfamiliar deformable material, a robot lacks prior knowledge of its physical properties and how it will respond to applied forces and motion.Multi-Agent Flow Matching with Decoupled Generative Guidance
Generative modeling is widely used for producing diverse objects from complex, multimodal distributions.Rethinking Representations for World-Action Modeling
World-action models jointly learn robot policies and predict future observations, making the representation space an interface between control and prediction.Rho: A Foundation for Efficiently Adaptable VLA Models
General-purpose physical AI models must combine broad visual and linguistic capabilities with precise control across robot embodiments and efficient adaptation to downstream tasks.Counterfactual Video Generation Enables Scalable Humanoid Loco-Manipulation
Teaching humanoids loco-manipulation skills, such as carrying diverse objects, via visual imitation is a promising path toward generalist robots.In-context Robot Learning Made Simple: A Democratized Recipe for Manipulation Tasks
We study robotic in-context learning (ICL), an emerging paradigm that enables robots to infer and execute tasks from visual demonstrations.Skill-Space Shooting for Autonomous Robot Policy Improvement
Robots deployed in the physical world must be able to improve beyond their initial training as they encounter new situations and failures.TALK-Dem: Benchmarking Embodied Task Planning under Dementia-Associated Communication Patterns
Existing LLM-driven robot task planners rely on a taken-for-granted assumption of an ideal user whose instructions are clear, complete, and task-focused.Learning to Plan from Random Exploration
Random exploration reveals how an environment can be traversed before a goal is specified.Unified Optimality Conditions for Stochastic Optimal Control in the Rough Path and Itô Frameworks
Stochastic differential equations (SDEs) can be studied via Itô calculus and rough path theory.GestAdapt: Workspace-Conditioned Co-Speech Gesture Generation for Humanoid Robots
Co-speech gestures for robots must adapt not only to speech and embodiment, but also to the workspace available for performing the motion.Memorize, Adapt, Ignore: Diagnosing Robot Learning Mechanisms under Training Data Variation
Training data variation, whether through designing a domain randomization (DR) scheme in simulation or curating demonstrations for imitation learning, is a primary lever for improving the robustness…Draft: A Parametric Tool for Robot Design Exploration
Robot performance is often limited by the cost of iterating on morphology and control together, since every computer-aided design (CAD) change has to be carried into a simulation-ready model before…