2019/02/02 by Miguel Alonso, Miguel Alonso Jr, Alonso, Miguel
Computer Science · Engineering · Mathematics · Neuroscience · #Artificial Intelligence (cs.AI) #EEG and Brain-Computer Interfaces #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural and Behavioral Psychology Studies #Robot Manipulation and Learning #cs.AI #cs.HC #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1902.00719
Submitted to HCI International 2019 Parallel Session on Spatial Interaction for Universal Access
arxiv created 2019/02/02 · openalex publication_date 2019/02/02 · arxiv updated 2019/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Interactive Machine Learning is concerned with creating systems that operate in environments alongside humans to achieve a task. A typical use is to extend or amplify the capabilities of a human in cognitive or physical ways, requiring the machine to adapt to the users' intentions and preferences. Often, this takes 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 cognitive load. The focus of this study is to extend the current state of the art in interactive machine learning by demonstrating that agents can learn a human user's behavior and adapt to preferences with a reduced amount of explicit human feedback in a mixed feedback setting. The learning agent perceives a value of its own behavior from hand gestures given via a spatial interface. This feedback mechanism is termed Spatial Interface Valuing. This method is evaluated experimentally in a simulated environment for a grasping task using a robotic arm with variable grip settings. Preliminary results indicate that learning agents using spatial interface valuing can learn a value function mapping spatial gestures to expected future rewards much more quickly as compared to those same agents just receiving explicit feedback, demonstrating that an agent perceiving feedback from a human user via a spatial interface can serve as an effective complement to existing approaches.