In the past decade, we have witnessed the rise of deep learning to dominate the field of
artificial intelligence. Advances in artificial neural networks alongside corresponding …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Voxel-based soft robots (VSRs) are a type of modular robots composed by interconnected
soft and deformable blocks, ie, voxels. Thanks to the softness of their bodies, VSRs may …

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

Nature evolves creatures with a high complexity of morphological and behavioral
intelligence, meanwhile computational methods lag in approaching that diversity and …

Modularity in robotics holds great potential. In principle, modular robots can be
disassembled and reassembled in different robots, and possibly perform new tasks …

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 …

This work provides a complete framework for the simulation, co-optimization, and sim-to-real
transfer of the design and control of soft legged robots. The compliance of soft robots …

Topology optimization is a powerful tool utilized in various fields for structural design.
However, its application has primarily been restricted to static or passively moving objects …

The automatic design of soft robots characterizes as jointly optimizing structure and control.
As reinforcement learning is gradually used to optimize control, the time-consuming …