Adaptive modality selection algorithm in robot-assisted cognitive training

© 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.Interaction of socially assistive robots with users is based on social cues coming from different interaction modalities, such as speech or gestures. However, using all modalities at all times may be inefficient as it can overload the user with redundant information and increase the task completion time. Additionally, users may favor certain modalities over the other as a result of their disability or personal preference. In this paper, we propose an Adaptive Modality Selection (AMS) algorithm that chooses modalities depending on the state of the user and the environment, as well as user preferences. The variables that describe the environment and the user state are defined as resources, and we posit that modalities are successful if certain resources possess specific values during their use. Besides the resources, the proposed algorithm takes into account user preferences which it learns while interacting with users. We tested our algorithm in simulations, and we implemented it on a robotic system that provides cognitive training, specifically Sequential memory exercises. Experimental results show that it is possible to use only a subset of available modalities without compromising the interaction. Moreover, we see a trend for users to perform better when interacting with a system with implemented AMS algorithm.Peer ReviewedPostprint (author's final draft

Multi-armed bandit models for 2D grasp planning with uncertainty

Abstract — For applications such as warehouse order fulfill-ment, robot grasps must be robust to uncertainty arising from sensing, mechanics, and control. One way to achieve robustness is to evaluate the performance of candidate grasps by sampling perturbations in shape, pose, and gripper approach and to com-pute the probability of force closure for each candidate to iden-tify a grasp with the highest expected quality. Since evaluating the quality of each grasp is computationally demanding, prior work has turned to cloud computing. To improve computational efficiency and to extend this work, we consider how Multi-Armed Bandit (MAB) models for optimizing decisions can be applied in this context. We formulate robust grasp planning as a MAB problem and evaluate convergence times towards an optimal grasp candidate using 100 object shapes from the Brown Vision 2D Lab Dataset with 1000 grasp candidates per object. We consider the case where shape uncertainty is represented as a Gaussian process implicit surface (GPIS) with Gaussian uncertainty in pose, gripper approach angle, and coefficient of friction. We find that Thompson Sampling and the Gittins index MAB methods converged to within 3 % of the optimal grasp up to 10x faster than uniform allocation and 5x faster than iterative pruning. I

Multi-Armed Recommendation Bandits for Selecting State Machine Policies for Robotic Systems

Abstract — We investigate the problem of selecting a statemachine from a library to control a robot. We are particularly interested in this problem when evaluating such state machines on a particular robotics task is expensive. As a motivating example, we consider a problem where a simulated vacuuming robot must select a driving state machine well-suited for a particular (unknown) room layout. By borrowing concepts from collaborative filtering (recommender systems such as Netflix and Amazon.com), we present a multi-armed bandit formulation that incorporates recommendation techniques to efficiently select state machines for individual room layouts. We show that this formulation outperforms the individual approaches (recommendation, multi-armed bandits) as well as the baseline of selecting the ‘average best ’ state machine across all rooms. I