VLACURRENT2026-10-14

Towards Agentic Robots: Model, Data, Environment, and Harness

Chonghan Qin, Dongming Wu, Kaituo Feng, Jiaxu Wang, Xinlei Yu, Peiwen Sun, Yijia Fan, Xilin He, Bingao Chen, Weitong Lian, Ryan Ji, Wenqi Ren, Xiaobin Hu, Dahua Lin, Yu-Gang Jiang, Xiangyu Yue

This survey maps out how to build end-to-end autonomous robots that reason about goals, collect their own Robot LearningTrainingThe process of fitting a model using data or experience. data, learn from interaction, and adapt strategies without human Safety & DeploymentInterventionA human or safety system stepping in during robot operation.—moving from static policies to systems that actually improve themselves. It organizes 17 categories of agentic Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. research across model architecture, data curation, simulated environments, and system orchestration so you know what pieces exist and how to combine them.

THE PROBLEM

This paper focuses on Modern Robot LearningVision-Language-Action model (VLA)A model that takes images and language as input and outputs robot actions.. A comprehensive survey of the emerging paradigm of agentic robots—systems that autonomously reason, interact, learn, and adapt. Covers Modern Robot LearningVision-Language-Action model (VLA)A model that takes images and language as input and outputs robot actions. models, world models, world-action models, and organizes research across model design, data collection strategies, Core ConceptsEnvironmentThe external world the robot operates in, including objects, obstacles, people, and surfaces. construction, and system harnesses for deploying agent capabilities in real robots. Read the paper by tracking the Core ConceptsTaskThe job the robot is supposed to complete, such as pick-and-place, navigation, or drawer opening. definition, the Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or data assumptions, and the evidence that supports the claimed improvement.

HOW IT WORKS

1

Task framing

The paper frames the work as Modern Robot LearningVision-Language-Action model (VLA)A model that takes images and language as input and outputs robot actions.. Start here because it defines what success means and which assumptions the rest of the method inherits.

2

Core method

This survey maps out how to build end-to-end autonomous robots that reason about goals, collect their own Robot LearningTrainingThe process of fitting a model using data or experience. data, learn from interaction, and adapt strategies without human Safety & DeploymentInterventionA human or safety system stepping in during robot operation.—moving from static policies to systems that actually improve themselves. It organizes 17 categories of agentic Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. research across model architecture, data curation, simulated environments, and system orchestration so you know what pieces exist and how to combine them. When reading the method section, identify the inputs, the learned or engineered representation, and the Core ConceptsActionA command the robot sends to its motors, controller, or low-level system. or prediction produced by the system.

3

Data and supervision

For robotics work, the data story is part of the method: check whether the system depends on Imitation & Reinforcement LearningTeleoperation (teleop)A human remotely controlling the robot, often to collect demonstrations., Simulation & Sim-to-RealSimulationA virtual environment where robots can be trained or tested., internet video, human labels, or Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. rollouts.

4

Evaluation evidence

The paper should be judged through its Simulation & Sim-to-RealEvaluationMeasuring how well a robot system performs. protocol: what data is used, what Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. or simulator is tested, and which Evaluation & ResearchBaselineA reference method used for comparison. comparisons support the claim. Look for the gap between the headline result and the Simulation & Sim-to-RealDeploymentPutting the trained system on a real robot. setting you would actually care about.

KEY RESULTS

Main contributionConceptual contribution

This survey maps out how to build end-to-end autonomous robots that reason about goals, collect their own Robot LearningTrainingThe process of fitting a model using data or experience. data, learn from interaction, and adapt strategies without human Safety & DeploymentInterventionA human or safety system stepping in during robot operation.—moving from static policies to systems that actually improve themselves. It organizes 17 categories of agentic Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. research across model architecture, data curation, simulated environments, and system orchestration so you know what pieces exist and how to combine them.

WHY DEVELOPERS SHOULD CARE

This survey maps out how to build end-to-end autonomous robots that reason about goals, collect their own Robot LearningTrainingThe process of fitting a model using data or experience. data, learn from interaction, and adapt strategies without human Safety & DeploymentInterventionA human or safety system stepping in during robot operation.—moving from static policies to systems that actually improve themselves. It organizes 17 categories of agentic Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. research across model architecture, data curation, simulated environments, and system orchestration so you know what pieces exist and how to combine them.

LIMITATIONS

The main limitation to check is whether the claimed behavior holds outside the paper's reported setup. That means testing across different Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. embodiments, scenes, objects, and data distributions.

WHAT COMES NEXT

The practical next step is independent reproduction with clear baselines, ablations, and stress tests. For a developer, the useful follow-up is to map the paper's Modern Robot LearningVision-Language-Action model (VLA)A model that takes images and language as input and outputs robot actions. assumptions onto a concrete Core ConceptsRobotA physical system with sensors and actuators that can observe the world and take actions. stack, then test the smallest version of the method that could run end to end.

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