Teleoperation, Human Intent Prediction and Imitation Learning Methods for Collaborative Robots

Sanket Gaurav
Some limitations and challenges prevent robots from being accepted widely as human peers. This thesis studies prediction and learning methods to understand human intentions and address challenges posed to collaborative robots related to translating between human and robotic behavior. The first topic discusses the correspondence learning problem of estimating a mapping of human embodiment to robot-joint configuration for robotic teleoperation using virtual reality. The second topic seeks to
more » ... e robots to more accurately predict human intentions from partial trajectories so that the robot can plan complementary activities. Finally, the research extends to learn a daily human activity (mopping the floor) from human demonstration videos to a robotic arm. By projecting into a 3-D workspace, robotic teleoperation using virtual reality allows for a more intuitive method of control for the operator than using a 2-D view from the robot's visual sensors. This chapter investigates a setup that places the teleoperator in a virtual representation of the robot's environment and develops a deep learning based architecture modeling the correspondence between the operator's movements in the virtual space and joint angles for a humanoid robot using data collected from a series of demonstrations. We evaluate the correspondence model's performance in a pick-and-place teleoperation experiment. More accurately inferring human intentions/goals can help robots complete collaborative human-robot tasks more safely and efficiently. Bayesian reasoning has become a popular approach for predicting the intention or goal of a partial sequence of actions/controls using a trajectory likelihood model. However, the mismatch between the training objective for these models (maximizing trajectory likelihood) and the application objective (maximizing intention likelihood) can be detrimental. In this chapter, we seek to improve the goal prediction of maximum entropy inverse reinforcement learning (MaxEnt IRL) models by training to maximize goal likel [...]
doi:10.25417/uic.17026412.v1 fatcat:mvfmixoemzevln3wtm6ipr5gue