Soft robotics has emerged as a promising field with the potential to
revolutionize industries such as healthcare and manufacturing. However,
designing effective soft robots presents challenges, particularly in managing
the complex interplay of material properties, structural design, and control
strategies. Traditional design methods are often time-consuming and may not
yield optimal designs. In this paper, we explore the use of generative AI to
create 3D models of soft actuators. We create a dataset of over 70 text-shape
pairings of soft pneumatic robot actuator designs, and adapt a latent diffusion
model (SDFusion) to learn the data distribution and generate novel designs from
it. By employing transfer learning and data augmentation techniques, we
significantly improve the performance of the diffusion model. These findings
highlight the potential of generative AI in designing complex soft robotic
systems, paving the way for future advancements in the field.

利用生成性人工智能设计复杂软体机器人系统的潜力和未来进展。

利用生成人工智能创造新型软体机器人设计

Creation of Novel Soft Robot Designs using Generative AI

Gait generation for soft robots is challenging due to the nonlinear dynamics
and high dimensional input spaces of soft actuators. Limitations in soft
robotic control and perception force researchers to hand-craft open loop
controllers for gait sequences, which is a non-trivial process. Moreover, short
soft actuator lifespans and natural variations in actuator behavior limit
machine learning techniques to settings that can be learned on the same time
scales as robot deployment. Lastly, simulation is not always possible, due to
heterogeneity and nonlinearity in soft robotic materials and their dynamics
change due to wear. We present a sample-efficient, simulation free, method for
self-generating soft robot gaits, using very minimal computation. This
technique is demonstrated on a motorized soft robotic quadruped that walks
using four legs constructed from 16 ``handed shearing auxetic" (HSA) actuators.
To manage the dimension of the search space, gaits are composed of two
sequential sets of leg motions selected from 7 possible primitives. Pairs of
primitives are executed on one leg at a time; we then select the
best-performing pair to execute while moving on to subsequent legs. This method
-- which uses no simulation, sophisticated computation, or user input --
consistently generates good translation and rotation gaits in as low as 4
minutes of hardware experimentation, outperforming hand-crafted gaits. This is
the first demonstration of completely autonomous gait generation in a soft
robot.

使用非常少的计算，无需模拟和用户输入的方法，实现了软体机器人的自动生成步态，性能优于手工制作的步态。

步態生成自動化方法用於行走的軟性機器四足

Automated Gait Generation For Walking, Soft Robotic Quadrupeds

Soft actuators offer a safe, adaptable approach to tasks like gentle grasping
and dexterous manipulation. Creating accurate models to control such systems
however is challenging due to the complex physics of deformable materials.
Accurate Finite Element Method (FEM) models incur prohibitive computational
complexity for closed-loop use. Using a differentiable simulator is an
attractive alternative, but their applicability to soft actuators and
deformable materials remains underexplored. This paper presents a framework
that combines the advantages of both. We learn a differentiable model
consisting of a material properties neural network and an analytical dynamics
model of the remainder of the manipulation task. This physics-informed model is
trained using data generated from FEM, and can be used for closed-loop control
and inference. We evaluate our framework on a dielectric elastomer actuator
(DEA) coin-pulling task. We simulate the task of using DEA to pull a coin along
a surface with frictional contact, using FEM, and evaluate the physics-informed
model for simulation, control, and inference. Our model attains < 5% simulation
error compared to FEM, and we use it as the basis for an MPC controller that
requires fewer iterations to converge than model-free actor-critic, PD, and
heuristic policies.

本研究提出了一种基于不同 iable simulator 的软致动器物理可行模型的训练方法，其通过与有限元方法结合来实现闭环控制，基于该模型构建 MPC 控制器，实现对硬币的拉动并获得了低于 5％的仿真误差。