[HTML][HTML] Human–robot collaboration and machine learning: A systematic review of recent research
Technological progress increasingly envisions the use of robots interacting with people in
everyday life. Human–robot collaboration (HRC) is the approach that explores the …
everyday life. Human–robot collaboration (HRC) is the approach that explores the …
Reinforcement learning approaches in social robotics
This article surveys reinforcement learning approaches in social robotics. Reinforcement
learning is a framework for decision-making problems in which an agent interacts through …
learning is a framework for decision-making problems in which an agent interacts through …
Model-based reinforcement learning variable impedance control for human-robot collaboration
Abstract Industry 4.0 is taking human-robot collaboration at the center of the production
environment. Collaborative robots enhance productivity and flexibility while reducing …
environment. Collaborative robots enhance productivity and flexibility while reducing …
Embodied communication: How robots and people communicate through physical interaction
Early research on physical human–robot interaction (pHRI) has necessarily focused on
device design—the creation of compliant and sensorized hardware, such as exoskeletons …
device design—the creation of compliant and sensorized hardware, such as exoskeletons …
A dynamical system approach to task-adaptation in physical human–robot interaction
The goal of this work is to enable robots to intelligently and compliantly adapt their motions
to the intention of a human during physical Human–Robot Interaction in a multi-task setting …
to the intention of a human during physical Human–Robot Interaction in a multi-task setting …
Deep predictive policy training using reinforcement learning
Skilled robot task learning is best implemented by predictive action policies due to the
inherent latency of sensorimotor processes. However, training such predictive policies is …
inherent latency of sensorimotor processes. However, training such predictive policies is …
Online hybrid motion planning for dyadic collaborative manipulation via bilevel optimization
T Stouraitis, I Chatzinikolaidis… - IEEE Transactions …, 2020 - ieeexplore.ieee.org
Effective collaboration is based on online adaptation of one's own actions to the actions of
their partner. This article provides a principled formalism to address online adaptation in …
their partner. This article provides a principled formalism to address online adaptation in …
Learning and comfort in human–robot interaction: A review
Collaborative robots provide prospective and great solutions to human–robot cooperative
tasks. In this paper, we present a comprehensive review for two significant topics in human …
tasks. In this paper, we present a comprehensive review for two significant topics in human …
Human-centered collaborative robots with deep reinforcement learning
We present a reinforcement learning based framework for human-centered collaborative
systems. The framework is proactive and balances the benefits of timely actions with the risk …
systems. The framework is proactive and balances the benefits of timely actions with the risk …
Bayesian optimization meets Riemannian manifolds in robot learning
Bayesian optimization (BO) recently became popular in robotics to optimize control
parameters and parametric policies in direct reinforcement learning due to its data efficiency …
parameters and parametric policies in direct reinforcement learning due to its data efficiency …