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Method and system for automatic robot control policy generation via CAD-based deep inverse reinforcement learning
Method and system for automatic robot control policy generation via CAD-based deep inverse reinforcement learning
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机译:通过基于CAD的深度逆强化学习自动生成机器人控制策略的方法和系统
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摘要
Systems and methods for automatic generation of robot control policies include a CAD-based simulation engine for simulating CAD-based trajectories for the robot based on cost function parameters, a demonstration module configured to record demonstrative trajectories of the robot, an optimization engine for optimizing a ratio of CAD-based trajectories to demonstrative trajectories based on computation resource limits, a cost learning module for learning cost functions by adjusting the cost function parameters using a minimized divergence between probability distribution of CAD-based trajectories and demonstrative trajectories; and a deep inverse reinforcement learning engine for generating robot control policies based on the learned cost functions.
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