REINFORCEMENT LEARNING FOR MULTI-LINKED MANIPULATOR CONTROL
Chen K. Tham & Richard W. Prager
We present an automatic trajectory planning and obstacle avoidance method for a multi-linked manipulator which uses position and velocity sensor information directly to produce the appropriate continuous-valued torques for each joint. The inputs are fed into a Cerebellar Model Arithmetic Computer (CMAC) (Albus, 1975) and in each state, the expected reward and torques for each joint are learnt through self-experimentation using a combination of the Temporal Difference (TD) technique (Sutton, 1987) and stochastic hillclimbing (Williams, 1988). Actions which cause the manipulator to reach the desired destination are rewarded whereas actions which lead to collisions with either joint limits or obstacles are punished by an amount proportional to the velocity before collision. After training, the manipulator is able to move along collision free paths from different start positions in the workspace to the destination.
Keywords: Reinforcement Learning; Machine Learning; Robotics; Connectionist Models
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