Reinforcement learning (RL) has roots in dynamic programming and it is called
adaptive/approximate dynamic programming (ADP) within the control community. This paper …

Lower-limb prostheses aim to restore ambulatory function for individuals with lower-limb
amputations. While the design of lower-limb prostheses is important, this paper focuses on …

Exoskeletons have enormous potential to improve human locomotive performance,–.
However, their development and broad dissemination are limited by the requirement for …

We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using
reinforcement learning. In this approach, we train a single policy model that finds near …

Objective. Advanced robotic lower limb prostheses are mainly controlled autonomously.
Although the existing control can assist cyclic movements during locomotion of amputee …

Dexterous manipulation of the robot is an important part of realizing intelligence, but
manipulators can only perform simple tasks such as sorting and packing in a structured …

In this article, we describe a method for optimizing control policies, with guaranteed
monotonic improvement. By making several approximations to the theoretically-justified …

From industrial exoskeletons to implantable medical devices, robots that interact closely with
people are poised to improve every aspect of our lives. Yet designing these systems is very …

This article presents a systematic review on wearable robotic devices that use human-in-the-
loop optimization (HILO) strategies to improve human–robot interaction. A total of 46 HILO …

Even though the unprecedented success of AlphaGo Zero demonstrated reinforcement
learning as a feasible complex problem solver, the research on reinforcement learning …