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Cyber-Human Approach For Learning Human Intention And Shape Robotic Behavior Based On Task Demonstration

Recent developments in artificial intelligence enabled training of autonomous robots without human supervision. Even without human supervision during training, current models have yet to be human-engineered and have neither guarantees to match human expectation nor perform within safety bounds. This paper proposes CyberSteer to leverage human-robot interaction and align goals between humans and robotic intelligent agents. Based on human demonstration of the task, CyberSteer learns an intrinsic reward function used by the human demonstrator to pursue the goal of the task. The learned intrinsic human function shapes the robotic behavior during training through deep reinforcement learning algorithms, removing the need for environment-dependent or hand-engineered reward signal. Two different hypotheses were tested, both using non-expert human operators for initial demonstration of a given task or desired behavior: one training a deep neural network to classify human-like behavior and other training a behavior cloning deep neural network to suggest actions. In this experiment, CyberSteer was tested in a high-fidelity unmanned air system simulation environment, Microsoft AirSim. The simulated aerial robot performed collision avoidance through a clustered forest environment using forward-looking depth sensing. The performance of CyberSteer is compared to behavior cloning algorithms and reinforcement learning algorithms guided by handcrafted reward functions. Results show that the human-learned intrinsic reward function can shape the behavior of robotic systems and have better task performance guiding reinforcement learning algorithms compared to standard human-handcrafted reward functions.