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Interactive Machine Learning is concerned with creating systems that operatein environments alongside humans to achieve a task. A typical use is to extendor amplify the capabilities of a human in cognitive or physical ways, requiringthe machine to adapt to the users' intentions and preferences. Often, thistakes the form of a human operator providing some type of feedback to the user,which can be explicit feedback, implicit feedback, or a combination of both.Explicit feedback, such as through a mouse click, carries a high cognitiveload. The focus of this study is to extend the current state of the art ininteractive machine learning by demonstrating that agents can learn a humanuser's behavior and adapt to preferences with a reduced amount of explicithuman feedback in a mixed feedback setting. The learning agent perceives avalue of its own behavior from hand gestures given via a spatial interface.This feedback mechanism is termed Spatial Interface Valuing. This method isevaluated experimentally in a simulated environment for a grasping task using arobotic arm with variable grip settings. Preliminary results indicate thatlearning agents using spatial interface valuing can learn a value functionmapping spatial gestures to expected future rewards much more quickly ascompared to those same agents just receiving explicit feedback, demonstratingthat an agent perceiving feedback from a human user via a spatial interface canserve as an effective complement to existing approaches.

Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing