Abstract
A robot can perform a given task through a policy that maps its sensed state to appropriate actions. We assume that a hand-coded controller can achieve such a mapping only for the basic cases of the task. Refining the controller becomes harder and gets more tedious and error prone as the complexity of the task increases. In this paper, we present a new learning from demonstration approach to improve the robot's performance through the use of corrective human feedback as a complement to an existing hand-coded algorithm. The human teacher observes the robot as it performs the task using the hand-coded algorithm and takes over the control to correct the behavior when the robot selects a wrong action to be executed. Corrections are captured as new state-action pairs and the default controller output is replaced by the demonstrated corrections during autonomous execution when the current state of the robot is decided to be similar to a previously corrected state in the correction database. The proposed approach is applied to a complex ball dribbling task performed against stationary defender robots in a robot soccer scenario, where physical Aldebaran Nao humanoid robots are used. The results of our experiments show an improvement in the robot's performance when the default hand-coded controller is augmented with corrective human demonstration.
Highlights
Transferring the knowledge of how to perform a certain task to a complex robotic platform remains a challenging problem in robotics research with an increasing importance as robots start emerging from research laboratories into everyday life and interacting with ordinary people who are not robotics experts
We evaluated the efficiency of the complementary corrective demonstration using three instances of the ball dribbling task with different opponent robot placements in each of them
Corrective demonstrations were supplied at two levels: action selection and dribble direction selection
Summary
Transferring the knowledge of how to perform a certain task to a complex robotic platform remains a challenging problem in robotics research with an increasing importance as robots start emerging from research laboratories into everyday life and interacting with ordinary people who are not robotics experts. A widely adopted method for transferring task knowledge to a robot is to develop a controller using a model for performing the task or skill, if such a model is available. Learning from Demonstration (LfD) paradigm is one such approach that utilizes supervised learning for transferring task or skill knowledge to an autonomous robot without explicitly programming it. Instead of hand-coding a controller for performing a task or skill, LfD methods make use of a teacher who demonstrates the robot how to perform the task or skill while the robot observes the demonstrations and synchronously records
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