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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Continuous-Time Safe Planning and Control for Agile Quadrupeds
Agile quadruped navigation in dynamic environments demands trajectories that are both dynamically feasible and provably collision-free.FISRF: Flexible Instructional Scaffolding Reflection Framework for Robotics Task Planning with Large Language Model
Large Language Models (LLMs) are widely adopted for reflective error correction in task planning; however, existing approaches primarily rely on autonomous exploration, rendering them prone to…TrySee: Modular Perception-Enhanced VLM Grounding for Closed-Loop Robot Task Planning in Specialized Domains
Vision Language Models (VLMs) have shown impressive capabilities in high-level reasoning and task planning for robots.SPECOLA: A Tool for Specification Driven Robot Evaluation
Simulation benchmarks are a useful way to measure progress in robotics; typically they provide success rate as the dominant measure of performance.Multi-Objective Reinforcement Learning Using Routed Ensembles and Trajectory Attribution
Multi-objective reinforcement learning is a fundamental problem for modern AI, with applications spanning post-training, reasoning models, and robotic control.Pretraining Knows What, Simulation Teaches How: Compositional Generalization in a Sim-Trained Robot Agent
A robot policy needs to understand what a task is asking for in a scene, and how to move the robot to accomplish that task.Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy
Frontier multimodal large language models (MLLMs) can control robots without robot-specific training, but how their capabilities compare and interact with post-trained vision-language-action (VLA)…OSWAM: An Effective World Action Model with One-Step Action Generation
Direct-policy World Action Models (WAM) have demonstrated the benefits of decoupling action generation from explicit future visual prediction for efficient inference in autonomous driving and…Nonprehensile Robotic Catching Using Virtual Constraints
The problem of nonprehensile robotic catching is addressed using virtual nonholonomic constraints (VNHCs); the motion of the robot end-effector is designed and controlled directly in terms of the…Differentiable Contact Dynamics for Legged Locomotion: Policy Learning and Contact-Implicit Trajectory Optimization
This paper proposes a smooth analytic gradient for frictional contact and applies it to policy learning and contact-implicit trajectory optimization for legged locomotion.Learning Force-Reactive Humanoid Loco-Manipulation
Many everyday tasks require controlling contact force as well as motion.E0.5: From Reconstructive to Future-Predictive 3D Foundations for Robotic World-Action Modeling
World-action models (WAMs) offer a promising framework for embodied intelligence, yet mainstream approaches remain centered on video foundation models or vision-language models.Rollback: Experience Backtracking in World Models for Policy Improvement
Action-conditioned world models offer a scalable alternative to costly real-world interaction for improving robot policies.Closed-Loop Aware Planning via Imitation-Free Speed Scoring for End-to-End Autonomous Driving
End-to-end autonomous driving has predominantly relied on open-loop imitation learning for training, which presents two limitations.Program-Level Adversarial Co-Adaptive Self-Play for Robotic Security
Coding agents can adapt robotic software by revising executable programs, but successful responses to an evolving opponent may not remain effective.cuArms: GPU-Accelerated Conflict-Based Planning for Multi-Arm Manipulation
Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks.Learning Multi-Phase Robot Loco-Manipulation via Phase-Conditioned Policy Optimization
Complex whole-body loco-manipulation requires executing diverse maneuvers, such as crouching, grasping, and locomotion, within a single long-horizon task.ASPEN: Adaptive Semantic Reasoning via Command Persistence Prediction for Efficient Navigation VLAs
Navigation vision-language-action (VLA) policies repeatedly invoke costly semantic reasoning even when the next command remains unchanged.Compatibility and future-proof testing for multi-agent systems
Independently developed autonomous agents, such as autonomous vehicles, must guarantee joint compatibility, such as safety and task-completion requirements, when they operate in a shared environment.Bridging Exploration and Loss of Plasticity in On-Policy Reinforcement Learning
Deep reinforcement learning (RL) algorithms have been observed to lose the ability to adaptively learn from new data, a phenomenon termed Loss of Plasticity (LoP).