An overview of soft robotics
Soft robots' flexibility and compliance give them the potential to outperform traditional rigid-
bodied robots while performing multiple tasks in unexpectedly changing environments and …
bodied robots while performing multiple tasks in unexpectedly changing environments and …
Design of soft robots: a review of methods and future opportunities for research
Soft robots present resilient and adaptable systems characterized by deformable bodies
inspired by biological systems. In this paper, we comprehensively review existing design …
inspired by biological systems. In this paper, we comprehensively review existing design …
PreCo: Enhancing Generalization in Co-Design of Modular Soft Robots via Brain-Body Pre-Training
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 …
morphologies, has emerged as a promising approach to enhance a robot's adaptability to its …
Sim-to-real transfer of soft robotic navigation strategies that learns from the virtual eye-in-hand vision
To steer a soft robot precisely in an unconstructed environment with minimal collision
remains an open challenge for soft robots. When the environments are unknown, prior …
remains an open challenge for soft robots. When the environments are unknown, prior …
Sim-to-real of soft robots with learned residual physics
Accurately modeling soft robots in simulation is computationally expensive and commonly
falls short of representing the real world. This well-known discrepancy, known as the sim-to …
falls short of representing the real world. This well-known discrepancy, known as the sim-to …
Reinforcement learning enables real-time planning and control of agile maneuvers for soft robot arms
Control policies for soft robot arms typically assume quasi-static motion or require a hand-
designed motion plan. To achieve real-time planning and control for tasks requiring highly …
designed motion plan. To achieve real-time planning and control for tasks requiring highly …
A deep learning framework for soft robots with synthetic data
Data-driven methods with deep neural networks demonstrate promising results for accurate
modeling in soft robots. However, deep neural network models rely on voluminous data in …
modeling in soft robots. However, deep neural network models rely on voluminous data in …
Fast aquatic swimmer optimization with differentiable projective dynamics and neural network hydrodynamic models
Aquatic locomotion is a classic fluid-structure interaction (FSI) problem of interest to
biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier …
biologists and engineers. Solving the fully coupled FSI equations for incompressible Navier …
Sim-to-real transfer of co-optimized soft robot crawlers
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. Soft robots have “mechanical …
transfer of the design and control of soft legged robots. Soft robots have “mechanical …
Aquarium: A fully differentiable fluid-structure interaction solver for robotics applications
We present Aquarium, a differentiable fluid-structure interaction solver for robotics that offers
stable simulation, accurately coupled fluid-robot physics in two dimensions, and full …
stable simulation, accurately coupled fluid-robot physics in two dimensions, and full …