... Abstract This study explores a learning-based tri-finger robotic arm manipulating task, which requires complex movements and coordination among the fingers. By employing reinforcement learning, we train an agent to acquire the necessary skills for proficient manipulation. To enhance...

... to this end include the use of physical talent metrics and modification of graph reinforcement learning architectures to allow joint learning of the swarm tactical policy and the talent metrics (search speed, flight range, and cruising speed) that constrain mobility and object/victim search capabilities...

... Abstract This paper presents an approach that integrates systems engineering principles with reinforcement learning (RL) to solve complex design problems under resource constraints. Product design processes often involve decomposing a large system into interconnected subsystems, with resources...

... pattern using a redesigned leg assembly and create a weight-compensation control system for a traditional leg assembly. Reinforcement learning (RL) was used in Simulink to develop the gait pattern control system, and a physical prototype was created to test the zero-gravity control system using Arduino...

... learning approach to solve control co-design problems for such smart systems. This approach uses a discrete two timescale reinforcement learning that addresses the control system design in an inner loop with a fast time scale and the physical system design in an outer loop with a slower time scale. Both...

... characterization. Previous work has shown various approaches to analyze balance in the COM state space through analytical, computational, and experimental methods. Here, we investigate balance recovery by developing a balance controller for a musculoskeletal model through reinforcement learning (RL). The RL...

... is used for case studies due to its inherent complexity and diversity. Two transfer reinforcement learning methods, feature extraction, and finetuning, are implemented and evaluated against the baseline. Instead of introducing large-scaled pre-trained models as the backbone, a light CNN model pre-trained...

.... The results show that the proposed approach is effective in deriving a rich set of heuristics for the golf problem, and can be extended in the future to more complex systems design problems. design heuristics decision making reinforcement learning multi-armed bandit systems engineering systems...

... Commission, focusing on how improving the 5G network by Artificial Intelligence (AI) and edge computing. The traffic flow into a reference roundabout is simulated by SUMO, a Reinforcement Learning (RL) algorithm is derived and drives the automated vehicles into the roundabout. By means of a dynamic driving...

...Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2022 August 14-17, 2022, St. Louis, Missouri DETC2022-87995 REINFORCEMENT LEARNING BASED SEQUENTIAL BATCH-SAMPLING FOR BAYESIAN OPTIMAL EXPERIMENTAL...

...Proceedings of the ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2022 August 14-17, 2022, St. Louis, Missouri DETC2022-89953 DESIGN OF FOURBAR LINKAGES USING A REINFORCEMENT LEARNING OPTIMIZATION METHOD Juan...

... Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2021 August 17-19, 2021, Virtual, Online DETC2021-70425 EVALUATING HEURISTICS IN ENGINEERING DESIGN: A REINFORCEMENT LEARNING APPROACH Karim...

...1 Assistant Professor, zequnw@mtu.edu, Corresponding Author 2 Graduate Student, narendra@mtu.edu RELIABILITY-BASED REINFORCEMENT LEARNING UNDER UNCERTAINTY Zequn Wang1 and Narendra Patwardhan2 Department of Mechanical Engineering-Engineering Mechanics, Michigan Technological University, Houghton...

... Abstract Model-free reinforcement learning based methods such as Proximal Policy Optimization, or Q-learning typically require thousands of interactions with the environment to approximate the optimal controller which may not always be feasible in robotics due to safety and time consumption...

... Abstract Particle swarm optimization (PSO) method is a well-known optimization algorithm, which shows good performance in solving different optimization problems. However, PSO usually suffers from slow convergence. In this paper, a reinforcement learning method is used to enhance PSO...

...REINFORCEMENT LEARNING CONTENT GENERATION FOR VIRTUAL REALITY APPLICATIONS Christian E. Lopez1 1Department of Industrial and Manufacturing Engineering The Pennsylvania State University, University Park, PA 16802 Email: cql5441@psu.edu Omar Ashour Department of Industrial Engineering...

..., this paper aims to maximize the arbitrage value of electricity through the optimal design of control strategies for DERs. Formulated as an arbitrage maximization problem using design optimization, and solved using reinforcement learning, the proposed approach is applied towards shared DERs within multi...

...) learning algorithms sequential decision-making under uncertainty simulation-based optimization reinforcement learning internal combustion engine calibration fuel economy con dom styl basi to l time eng eve spec app igni resp Key algo sim com Proceedings of the ASME 2007 International Design...

... Interface Design Behavior Design Petri-Net Reinforcement Learning Heuristic Search The design of interactions of product components, i.e., an interface structure, is a very difficult design problem, because the whole product behavior is determined by the interface structure. This paper...