Abstract-In this paper, we propose an approach for learning task specifications automatically, by observing human demonstrations. Using this approach allows a robot to combine representations of individual actions to achieve a high-level goal. We hypothesize that task specifications consist of variables that present a pattern of change that is invariant across demonstrations. We identify these specifications at different stages of task completion. Changes in task constraints allow us to identify transitions in the task description and to segment them into subtasks. We extract the following task-space constraints: 1) the reference frame in which to express the task variables; 2) the variable of interest at each time step, position, or force at the end effector; and 3) a factor that can modulate the contribution of force and position in a hybrid impedance controller. The approach was validated on a sevendegree-of-freedom Kuka arm, performing two different tasks: grating vegetables and extracting a battery from a charging stand. Index Terms-Constraint extraction, learning and adaptive systems, motion control, programming by demonstration (PbD).
Recently, REF presented an approach to learn the task of grating vegetables based on task-space constraints (e.g. force and position of the end-effector), which are defined by a significant variance of the observed variables across demonstrations.
Task Parameterization Using Continuous Constraints Extracted From Human Demonstrations
IEEE Transactions on Robotics
IEEE Transactions on Robotics
Computer Science