Bayesian Nonparametric Learning of Cloth Models for Real-time State Estimation

Robotic solutions to clothing assistance can significantly improve quality of life for the elderly and disabled. Real-time estimation of the human-cloth relationship is crucial for efficient learning of motor skills for robotic clothing assistance. The major challenge involved is cloth-state estimation due to inherent nonrigidity and occlusion. In this study, we present a novel framework for real-time estimation of the cloth state using a low-cost depth sensor, making it suitable for a feasible social implementation. The framework relies on the hypothesis that clothing articles are constrained to a low-dimensional latent manifold during clothing tasks. We propose the use of manifold relevance determination (MRD) to learn an offline cloth model that can be used to perform informed cloth-state estimation in real time. The cloth model is trained using observations from a motion capture system and depth sensor. MRD provides a principled probabilistic framework for inferring the accurate motion-capture state when only the noisy depth sensor feature state is available in real time. The experimental results demonstrate that our framework is capable of learning consistent task-specific latent features using few data samples and has the ability to generalize to unseen environmental settings. We further present several factors that affect the predictive performance of the learned cloth-state model

Data-efficient Learning of Robotic Clothing Assistance using Bayesian Gaussian Process Latent Variable Models

Motor-skill learning for complex robotic tasks is a challenging problem due to the high task variability. Robotic clothing assistance is one such challenging problem that can greatly improve the quality-of-life for the elderly and disabled. In this study, we propose a data-efficient representation to encode task-specific motor-skills of the robot using Bayesian nonparametric latent variable models. The effectivity of the proposed motor-skill representation is demonstrated in two ways: (1) through a real-time controller that can be used as a tool for learning from demonstration to impart novel skills to the robot and (2) by demonstrating that policy search reinforcement learning in such a task-specific latent space outperforms learning in the high-dimensional joint configuration space of the robot. We implement our proposed framework in a practical setting with a dual-arm robot performing clothing assistance tasks

Abstract Background

Continuous and simultaneous estimation of finger kinematics using inputs from an EMG-to-muscl

Bilateral (but not unilateral) interaction creates and cements norms at the covert psychophysical level: A behavioral and an fMRI study

Social norms, including values, beliefs and even perceptions about the world, are preserved and created through repeated interactions between individuals. However, whereas neuro-cognitive research on social norms has used the “unilateral influence” paradigm focusing on people’s reactions to extant standards, little is known about how our basic perceptions and judgments are shaped as new norms through bilateral interaction. Here, using a simple estimation task, we investigated the formation of perceptual norms using two experiments coupled with computational modeling. In the behavioral experiment, participants in dyads repeatedly estimated the number of dots on a screen and viewed each other’s answers. In the fMRI experiment, we manipulated the interaction process by pairing each participant with a computer agent which adjusted its estimations reciprocally to participants’ estimations (bilateral agent) or did not (unilateral). The results indicated that only the bilateral interaction yielded convergence of participants’ covert psychophysical functions (relations between subjective estimations and the actual number of dots) as well as overt behavioral responses within a pair. Bilateral interaction also increased the stability (reliability) of the covert function within each individual after interaction. Neural activity in the mentalizing network (right temporoparietal junction and dorsomedial prefrontal cortex) during interaction modulated the stabilization of the psychophysical function. These results imply that bilateral interaction helps people to cognitively anchor their views with each other. Such spontaneous perspective sharing can yield a shared covert “generative model” that enables endogenous agreement on totally new targets ― one of the key features of social norms