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
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
Task framing
Core method
Data and supervision
Evaluation evidence
KEY RESULTS
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.