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 135
Inferring Soil Friction Angle from Robot Foot-Ground Force Histories: A Bayesian Inverse Approach to Proprioceptive Soil Sensing
Foot-ground interaction signals recorded by quadruped robots may enable spatially distributed, in situ characterization of soil strength.Cooperative Multi-Agent Vision-Language-Action Models via Reinforced Fine Tuning
We study reinforcement learning (RL) methods for cooperative multi-agent Vision-Language-Action (VLA) models.Simple Agentic Memory for Generalist Robot Policies
Visual-memory systems commonly retain or compress past observations.HACo: Learning Haptic Active Compliance for Force-Aware Dexterous Manipulation
Contact-rich dexterous manipulation requires policies that translate physical feedback into motion commands while regulating interaction loads across evolving multi-contact interactions.Executor-aware Candidate Selection via a Feasibility Certificate
Modular robotic systems often separate motion planning from a downstream executor that enforces state-dependent hard constraints.OTRetarget: Joint Robot and Object Motion Retargeting via Optimal Transport
Transferring human motion to humanoid robots requires adapting the demonstrated motion to the robot morphology while preserving interactions with the environment.RoboChrono: A Real Robot Benchmark for Streaming Task Understanding
Understanding ongoing robot manipulation requires models to interpret visual observations in relation to interaction history and task progress.Foundation-Model-Guided Topology-Aware Semantic Risk Fields for Manipulation
Robot motion planning in everyday environments must satisfy hard geometric constraints while accounting for context-dependent semantic risk.Where Predictive Supervision Goes Shapes What VLA Policies Learn
Future prediction is increasingly used to improve vision-language-action (VLA) policies, based on the premise that anticipating scene evolution encourages representations useful for control.Kinematic Nonlinear Spatio-Temporal Trajectory Warping for Contact-Rich Dexterous Manipulation Demonstrations
We present a straightforward but effective method for repurposing existing contact-rich dexterous manipulation demonstrations.T$^2$Mem: Learning Test-Time Memory for Robotics
Memory-dependent robotic manipulation requires policies to use information that is no longer available in the current observation.Degeneracy-Orthogonal Geometric Constraints for LiDAR SLAM
Autonomous robot navigation relies on simultaneous localization and mapping (SLAM) to estimate motion and maintain an accurate pose within an environment.LexiconVLA: Learning Reusable Atomic Action Codebooks for Unseen Tasks
Vision-language-action (VLA) models struggle to reuse recurring interactions in unseen tasks.DRHeC: Differentiable Rendering for Hand-Eye Calibration with RGB-Based Gradients
Accurate hand-eye calibration is crucial for precision manipulation.Scale-Invariant Manipulability Shape Tracking Across Heterogeneous Manipulators
When transferring manipulability across systems with different sizes and kinematic structures, matching absolute ellipsoid scale may be unnecessary when the goal is to reproduce orientation and…TaRL: Learning General and Physical Rewards from Tactile Demonstrations
Contact-rich manipulation requires robots to sequence precise contacts, maintain stable grasps, and apply directed forces.Spotter: Let the Embodied Model Lead, and the VLM Reflect for It
Current embodied models do not respond to their own failures, although what just went wrong could inform a small adjustment on the next attempt, the kind of reflection behind the gains of thinking…Diffusion Policy Improvement with Proposal-Conditioned Refinement Flows
Diffusion and flow policies can model complex behaviors in offline reinforcement learning (RL).GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion
Transparent surfaces are ubiquitous in built environments, yet they remain a persistent failure case for robotic perception.VidAct: Learning Manipulation from In-the-Wild Videos with Object-Centric 3D Awareness
Video demonstrations offer a scalable alternative to costly robot data for learning manipulation, yet existing reconstruction-based approaches often rely on constrained camera viewpoints or…