2 research outputs found
Fast Motion Planning for High-DOF Robot Systems Using Hierarchical System Identification
We present an efficient algorithm for motion planning and control of a robot
system with a high number of degrees-of-freedom. These include high-DOF soft
robots or an articulated robot interacting with a deformable environment. Our
approach takes into account dynamics constraints and present a novel technique
to accelerate the forward dynamic computation using a data-driven method. We
precompute the forward dynamic function of the robot system on a hierarchical
adaptive grid. Furthermore, we exploit the properties of underactuated robot
systems and perform these computations for a few DOFs. We provide error bounds
for our approximate forward dynamics computation and use our approach for
optimization-based motion planning and reinforcement-learning-based feedback
control. Our formulation is used for motion planning of two high DOF robot
systems: a high-DOF line-actuated elastic robot arm and an underwater swimming
robot operating in water. As compared to prior techniques based on exact
dynamic function computation, we observe one to two orders of magnitude
improvement in performance.Comment: 7 page
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding
We address the problem of accelerating thin-shell deformable object
simulations by dimension reduction. We present a new algorithm to embed a
high-dimensional configuration space of deformable objects in a low-dimensional
feature space, where the configurations of objects and feature points have
approximate one-to-one mapping. Our key technique is a graph-based
convolutional neural network (CNN) defined on meshes with arbitrary topologies
and a new mesh embedding approach based on physics-inspired loss term. We have
applied our approach to accelerate high-resolution thin shell simulations
corresponding to cloth-like materials, where the configuration space has tens
of thousands of degrees of freedom. We show that our physics-inspired embedding
approach leads to higher accuracy compared with prior mesh embedding methods.
Finally, we show that the temporal evolution of the mesh in the feature space
can also be learned using a recurrent neural network (RNN) leading to fully
learnable physics simulators. After training our learned simulator runs
faster and the accuracy is high enough for robot manipulation
tasks