Reward-weighted GMM and its application to action-selection in robotized shoe dressing

The final publication is available at link.springer.comIn the context of assistive robotics, robots need to make multiple decisions. We explore the problem where a robot has multiple choices to perform a task and must select the action that maximizes success probability among a repertoire of pre-trained actions. We investigate the case in which sensory data is only available before making the decision, but not while the action is being performed. In this paper we propose to use a Gaussian Mixture Model (GMM) as decision-making system. Our adaptation permits the initialization of the model using only one sample per component. We also propose an algorithm to use the result of each execution to update the model, thus adapting the robot behavior to the user and evaluating the effectiveness of each pre-trained action. The proposed algorithm is applied to a robotic shoe-dressing task. Simulated and real experiments show the validity of our approach.Peer ReviewedPostprint (author's final draft

Dimensionality reduction in learning Gaussian mixture models of movement primitives for contextualized action selection and adaptation

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Robotic manipulation often requires adaptation to changing environments. Such changes can be represented by a certain number of contextual variables that may be observed or sensed in different manners. When learning and representing robot motion –usually with movement primitives–, it is desirable to adapt the learned behaviors to the current context. Moreover, different actions or motions can be considered in the same framework, using contextualization to decide which action applies to which situation. Such frameworks, however, may easily become large-dimensional, thus requiring to reduce the dimensionality of the parameters space, as well as the amount of data needed to generate and improve the model over experience. In this paper, we propose an approach to obtain a generative model from a set of actions that share a common feature. Such feature, namely a contextual variable, is plugged into the model to generate motion. We encode the data with a Gaussian Mixture Model in the parameter space of Probabilistic Movement Primitives (ProMPs), after performing Dimensionality Reduction (DR) on such parameter space. We append the contextual variable to the parameter space and obtain the number of Gaussian components, i.e., different actions in a dataset, through Persistent Homology. Then, using multimodal Gaussian Mixture Regression (GMR), we can retrieve the most likely actions given a contextual situation and execute them. After actions are executed, we use Reward-Weighted Responsibility GMM (RWR-GMM) update the model after each execution. Experimentation in 3 scenarios shows that the method drastically reduces the dimensionality of the parameter space, thus implementing both action selection and adaptation to a changing situation in an efficient way.Peer ReviewedPostprint (author's final draft

Bimanual Interaction with Clothes. Topology, Geometry, and Policy Representations in Robots

Twardon L. Bimanual Interaction with Clothes. Topology, Geometry, and Policy Representations in Robots. Bielefeld: Universität Bielefeld; 2019.If anthropomorphic robots are to assist people with activities of daily living, they must be able to handle all kinds of everyday objects, including highly deformable ones such as garments. The present thesis begins with a detailed problem analysis of robotic interaction with and perception of clothes. We show that handling items of clothing is very challenging due to their complex dynamics and the vast number of degrees of freedom. As a result of our analysis, we obtain a topological, geometric, and functional description of garments that supports the development of reduced object and task representations. One of the key findings is that the boundary components, which typically correspond with the openings, characterize garments well, both in terms of their topology and their inherent purpose, namely dressing. We present a polygon-based and an interactive method for identifying boundary components using RGB-D vision with application to grasping. Moreover, we propose Active Boundary Component Models (ABCMs), a constraint-based framework for tracking garment openings with point clouds. It is often difficult to maintain an accurate representation of the objects involved in contact-rich interaction tasks such as dressing assistance. Therefore, our policy optimization approach to putting a knit cap on a styrofoam head avoids modeling the details of the garment and its deformations. The experimental results suggest that a heuristic performance measure that takes into account the amount of contact established between the two objects is suitable for the task

Reward-weighted GMM and its application to action-selection in robotized shoe dressing

The final publication is available at link.springer.comIn the context of assistive robotics, robots need to make multiple decisions. We explore the problem where a robot has multiple choices to perform a task and must select the action that maximizes success probability among a repertoire of pre-trained actions. We investigate the case in which sensory data is only available before making the decision, but not while the action is being performed. In this paper we propose to use a Gaussian Mixture Model (GMM) as decision-making system. Our adaptation permits the initialization of the model using only one sample per component. We also propose an algorithm to use the result of each execution to update the model, thus adapting the robot behavior to the user and evaluating the effectiveness of each pre-trained action. The proposed algorithm is applied to a robotic shoe-dressing task. Simulated and real experiments show the validity of our approach.Peer Reviewe