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- W4384158649 abstract "This paper aims to solve the trajectory tracking task of the pneumatic musculoskeletal robot within a model-based reinforcement learning framework. Considering the limited sensors and short lifespan of self-made pneumatic artificial muscles, multi-task Gaussian process regression is employed for micro-data model learning and the learned model is combined with cross entropy method (CEM)-based model predictive control to plan for the optimal action online. To further compensate for the model imperfection and improve the control performance, a proportional derivative controller-like strategy is proposed to guide the search space of the original CEM solver. The effectiveness of our approach is verified on a real musculoskeletal system with one degree of freedom and the results show that only 50 s of interacting with the environment is enough for the robot to learn trajectory tracking skills from scratch." @default.
- W4384158649 created "2023-07-14" @default.
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- W4384158649 date "2023-05-22" @default.
- W4384158649 modified "2023-09-24" @default.
- W4384158649 title "Model-Based Reinforcement Learning for Trajectory Tracking of Musculoskeletal Robots" @default.
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- W4384158649 doi "https://doi.org/10.1109/i2mtc53148.2023.10175993" @default.
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