Kernel-Based Human-Dynamics Inversion for Precision Robot Motion-Primitives

• Main Authors: Warrier, Rahul B., Devasia, Santosh
• Format: Conference Proceeding
• Language: English
• Subjects: Biological system modeling; Estimation; Gaussian processes; Kernel; Robots; Task analysis; Trajectory
• ISSN: 2153-0866
• DOI: 10.1109/IROS.2018.8594164

Learning motion primitives from demonstration requires the human demonstrator to effectively relay the task intent to the robot controller. When the task intent is not reflected sufficiently by the demonstration, multiple iterations are required to recover the underlying intent of the demonstrations. However, a large number of iterations can be expensive and might not be practical for each new task. A challenge is that human-in-the-loop demonstrations can be affected by the human motor dynamics (e.g., from visual observation to hand motion), which can lead to differences between the demonstration and intent. The main contribution of this article is to correct for the human motor dynamics and infer the intended action (motion primitive) from the human demonstrations. The proposed approach uses a kernel-based regression approach to learn the inverse human-dynamics response. These models are then used to correct for human-motor-dynamics and infer the intent of the human-in-the-loop demonstrator. Experimental validation is performed with an assisted teleoperation setup where the underlying intent is specified using an augmented reality display. Results indicate that the proposed approach leads to more precise intent estimation as compared to the actual human demonstrations.