Sensory information is fundamental for autonomous robots that face unknown environments. On-line sensing allows a robot arm to modify its motion in real time to cope better with the environment. Reactive systems (e.g., [1]) are appropriate to generate on-line motions from local sensory data. A reactive controller can be implemented automatically by using artificial neural networks and reinforcement learning (RL) [2,3,4]. RL allows a neural network to acquire reaction rules while the robot arm interacts with its environment.
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