Incremental Learning Introspective Movement Primitives From Multimodal Unstructured Demonstrations

Abstract

Learning movement primitive from unstructured demonstrations has become a popular topic in recent years, which provides a natural way to endow human-inspired skills to robots. The main idea of movement primitives is that should suffice to reconstruct a large set of complex manipulation tasks. However, conventional learning methods mostly focus on the kinesthetic variables and ignore those critical introspective capacities in manipulation such as movement generalization and assessment of the sensory signals. In this paper, we investigate the association of generalization, fault detection, fault diagnoses, and task exploration during manipulation task, and call such movement primitives augmented with introspective capacities Introspective Movement Primitives (IMP). With our previous work, this paper mainly addresses how IMPs can be acquired by assessing the quality of multimodal sensory data of unstructured demonstrations and how they can incrementally create manipulation task by reverse execution and human interaction. Experimental evaluation on a human-robot collaborative packaging task with a Rethink Baxter robot, results indicate that our proposed method can effectively increase robustness towards external perturbations and adaptive exploration during robot manipulation task.

Learning Sequential Force Interaction Skills

“…As a result, their approach enables a robot to physically interact with a human co-worker. While human-robot collaboration (e.g., Maeda et al [27], Caccavale et al [28], Wu et al [29]) is an interesting research domain, our approach focuses on tasks where the robot is operating autonomously. A similar approach was proposed by Huang et al [30].…”