Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

Brain-body co-design, which involves the collaborative design of control strategies and
morphologies, has emerged as a promising approach to enhance a robot's adaptability to its …

Contrastive learning, which pulls positive pairs closer and pushes away negative pairs, has
remarkably propelled the development of self-supervised representation learning. Previous …

Robot co-design, where the morphology of a robot is optimized jointly with a learned policy
to solve a specific task, is an emerging area of research. It holds particular promise for soft …

Inspired by the necessity of morphological adaptation in animals, a growing body of work
has attempted to expand robot training to encompass physical aspects of a robot's design …

Multi-cellular robot design aims to create robots comprised of numerous cells that can be
efficiently controlled to perform diverse tasks. Previous research has demonstrated the …

Neuro-evolutionary methods have proven effective in addressing a wide range of tasks.
However, the study of the robustness and generalizability of evolved artificial neural …

In this study, we introduce an innovative approach to enhance interpretability in the design
optimization of voxel-based soft robots (VSRs). VSRs present a unique challenge in …

Robot simulators are indispensable tools across many fields, and recent research has
significantly improved their functionality by incorporating additional gradient information …

Анотація The article analyzes the design limitations of a compact robot designed to
investigate damage to panel houses. The requirements for size, maneuverability, energy …