Model-Based Control with Sparse Neural Dynamics
Learning predictive models from observations using deep neural networks
(DNNs) is a promising new approach to many real-world planning and control
problems. However, common DNNs are too unstructured for effective planning, and
current control methods typically rely on extensive sampling or local gradient
descent. In this paper, we propose a new framework for integrated model
learning and predictive control that is amenable to efficient optimization
algorithms. Specifically, we start with a ReLU neural model of the system
dynamics and, with minimal losses in prediction accuracy, we gradually sparsify
it by removing redundant neurons. This discrete sparsification process is
approximated as a continuous problem, enabling an end-to-end optimization of
both the model architecture and the weight parameters. The sparsified model is
subsequently used by a mixed-integer predictive controller, which represents
the neuron activations as binary variables and employs efficient
branch-and-bound algorithms. Our framework is applicable to a wide variety of
DNNs, from simple multilayer perceptrons to complex graph neural dynamics. It
can efficiently handle tasks involving complicated contact dynamics, such as
object pushing, compositional object sorting, and manipulation of deformable
objects. Numerical and hardware experiments show that, despite the aggressive
sparsification, our framework can deliver better closed-loop performance than
existing state-of-the-art methods.
6583c913a4af83584c8e17c1
Ziang Liu
6583c913a4af83584c8e17c2
Genggeng Zhou
6583c913a4af83584c8e17c3
Jeff He
6583c913a4af83584c8e17c4
Tobia Marcucci
6583c913a4af83584c8e17c5
Li Fei-Fei
6583c913a4af83584c8e17c6
Jiajun Wu
claimed_verified
2023-12-21T09:31:55.837Z
647b7b8345616c3de3627863
/avatars/8196364bffed71086ba9a0a4a62027d3.svg
Jiajun Wu
user
jiajunwu
6583c913a4af83584c8e17c7
Yunzhu Li
extracted_confirmed
2023-12-29T19:43:46.423Z
64a46562c641afb468a3dc04
/avatars/bc54e5c13f657251b05bf2725086e56d.svg
Yunzhu Li
user
yunzhuli
Model-Based Control with Sparse Neural Dynamics
Learning predictive models from observations using deep neural networks
(DNNs) is a promising new approach to many real-world planning and control
problems. However, common DNNs are too unstructured for effective planning, and
current control methods typically rely on extensive sampling or local gradient
descent. In this paper, we propose a new framework for integrated model
learning and predictive control that is amenable to efficient optimization
algorithms. Specifically, we start with a ReLU neural model of the system
dynamics and, with minimal losses in prediction accuracy, we gradually sparsify
it by removing redundant neurons. This discrete sparsification process is
approximated as a continuous problem, enabling an end-to-end optimization of
both the model architecture and the weight parameters. The sparsified model is
subsequently used by a mixed-integer predictive controller, which represents
the neuron activations as binary variables and employs efficient
branch-and-bound algorithms. Our framework is applicable to a wide variety of
DNNs, from simple multilayer perceptrons to complex graph neural dynamics. It
can efficiently handle tasks involving complicated contact dynamics, such as
object pushing, compositional object sorting, and manipulation of deformable
objects. Numerical and hardware experiments show that, despite the aggressive
sparsification, our framework can deliver better closed-loop performance than
existing state-of-the-art methods.
A new framework sparsifies ReLU neural models for predictive control, enabling efficient optimization and handling complex tasks with improved performance.
deep neural networks ReLU neural model system dynamics discrete sparsification continuous problem end-to-end optimization model architecture weight parameters mixed-integer predictive controller neuron activations binary variables branch-and-bound algorithms graph neural dynamics contact dynamics object pushing compositional object sorting deformable objects closed-loop performance