2,366 research outputs found
Efficient Jacobian-Based Inverse Kinematics With Sim-to-Real Transfer of Soft Robots by Learning
This paper presents an efficient learning-based method to solve the inverse
kinematic (IK) problem on soft robots with highly non-linear deformation. The
major challenge of efficiently computing IK for such robots is due to the lack
of analytical formulation for either forward or inverse kinematics. To address
this challenge, we employ neural networks to learn both the mapping function of
forward kinematics and also the Jacobian of this function. As a result,
Jacobian-based iteration can be applied to solve the IK problem. A sim-to-real
training transfer strategy is conducted to make this approach more practical.
We first generate a large number of samples in a simulation environment for
learning both the kinematic and the Jacobian networks of a soft robot design.
Thereafter, a sim-to-real layer of differentiable neurons is employed to map
the results of simulation to the physical hardware, where this sim-to-real
layer can be learned from a very limited number of training samples generated
on the hardware. The effectiveness of our approach has been verified on
pneumatic-driven soft robots for path following and interactive positioning
Development of a Modular and Submersible Soft Robotic Arm and Corresponding Learned Kinematics Models
Most soft-body organisms found in nature exist in underwater environments. It
is helpful to study the motion and control of soft robots underwater as well.
However, a readily available underwater soft robotic system is not available
for researchers to use because they are difficult to design, fabricate, and
waterproof. Furthermore, submersible robots usually do not have configurable
components because of the need for sealed electronics packages. This work
presents the development of a submersible soft robotic arm driven by hydraulic
actuators which consists of mostly 3D printable parts which can be assembled in
a short amount of time. Also, its modular design enables multiple shape
configurations and easy swapping of soft actuators. As a first step to
exploring machine learning control algorithms on this system, two deep neural
network models were developed, trained, and evaluated to estimate the robot's
forward and inverse kinematics. The techniques developed for controlling this
underwater soft robotic arm can help advance understanding on how to control
soft robotic systems in general.Comment: 12 pages, 10 figure
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