Abstract: Preference-based reinforcement learning (PBRL) enables policy learning through simple queries comparing trajectories from a single policy. While human responses to these queries make it possible to learn policies aligned with human preferences, PBRL suffers from low query efficiency, as policy bias limits trajectory diversity and reduces the number of … [Read more...] about DAPPER: Discriminability-Aware Policy-to-Policy Preference-Based Reinforcement Learning for Query-Efficient Robot Skill Acquisition
Data models
An Interactive Augmented Reality Interface for Personalized Proxemics Modeling: Comfort and Human–Robot Interactions
Understanding and respecting personal space preferences is essential for socially assistive robots designed for older adult users. This work introduces and evaluates a novel personalized context-aware method for modeling users’ proxemics preferences during human-robot interactions. Using an interactive augmented reality interface, we collected a set of user-preferred distances … [Read more...] about An Interactive Augmented Reality Interface for Personalized Proxemics Modeling: Comfort and Human–Robot Interactions
A Digital Twin of the Research Vessel Gunnerus for Lifecycle Services: Outlining Key Technologies
Digitalization has become a key aspect of making maritime industries more innovative, efficient, and fit for future operations. One of the most attractive aspects is the concept of digital twins , which refers to digital replicas of physical assets, processes, and systems that can be used as advanced tools for design, operation, and maintenance. This article introduces the … [Read more...] about A Digital Twin of the Research Vessel Gunnerus for Lifecycle Services: Outlining Key Technologies
Mastering the Complex Assembly Task With a Dual-Arm Robot: A Novel Reinforcement Learning Method
Deep reinforcement learning (DRL) has achieved great success across multiple fields; however, in the field of robot control, the acquisition of large amounts of motion data from real robots is challenging. In this work, an algorithm is proposed to train a neural network model with a large amount of data in a simulated environment and then transfer the model to the real … [Read more...] about Mastering the Complex Assembly Task With a Dual-Arm Robot: A Novel Reinforcement Learning Method
Toward Lifelong Learning for Industrial Defect Classification: A Proposed Framework
Automatic defect inspection is an important application for the development of smart factories in the era of Industry 4.0. It gathers data from production lines to train a model to automatically recognize certain types of defects. However, the defect types may vary in the production process, and it is difficult for the old model to adapt to new types of defects directly. … [Read more...] about Toward Lifelong Learning for Industrial Defect Classification: A Proposed Framework
