首页|Recent Findings from University of the Chinese Academy of Sciences Has Provided New Information about Robotics (Sketch Rl: Interactive Sketch Generation for Long-horizon Tasks Via Vision-based Skill Predictor)
Recent Findings from University of the Chinese Academy of Sciences Has Provided New Information about Robotics (Sketch Rl: Interactive Sketch Generation for Long-horizon Tasks Via Vision-based Skill Predictor)
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NETL
NSTL
IEEE
Investigators discuss new findings in Robotics. According to news originating from Beijing, People's Republic of China, by NewsRx correspondents, research stated, “For autonomous robots, it is desirable to learn coordination of primitive skills that can effectively solve long-horizon tasks and perform novel ones. Recent advances in hierarchical policy learning have shown that decomposing complex tasks into sequences of primitive skills which are called sketches can enable robots to perform directed exploration in challenging manipulation tasks.” Financial support for this research came from National Key Research and Development Plan of China. Our news journalists obtained a quote from the research from the University of the Chinese Academy of Sciences, “However, they usually fall short in sequencing skills in a new task without retraining as the task sketches are almost hard-coded or learned by deep reinforcement learning. To improve exploration efficiency for long-horizon tasks, we propose Sketch RL, a hierarchical framework that combines supervised learning with reinforcement learning interactively generates the task sketch, and utilizes it as the curriculum to guide low-level skill learning. Furthermore, to allow for multitask decomposition and generalizing few- shot to new tasks, our method exploits a Vision-based Skill Predictor (VSP) to capture shared subtask structure.
BeijingPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningNano-robotReinforcement LearningRoboticsUniversity of the Chinese Academy of Sciences
NETL
NSTL
IEEE
