title: Model-Based Control with Sparse Neural Dynamics
summary: 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.
paper_authors._id: 6583c913a4af83584c8e17c1
paper_authors.name: Ziang Liu
paper_authors._id: 6583c913a4af83584c8e17c2
paper_authors.name: Genggeng Zhou
paper_authors._id: 6583c913a4af83584c8e17c3
paper_authors.name: Jeff He
paper_authors._id: 6583c913a4af83584c8e17c4
paper_authors.name: Tobia Marcucci
paper_authors._id: 6583c913a4af83584c8e17c5
paper_authors.name: Li Fei-Fei
paper_authors._id: 6583c913a4af83584c8e17c6
paper_authors.name: Jiajun Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-21T09:31:55.837Z
paper_authors._id: 647b7b8345616c3de3627863
paper_authors.avatarUrl: /avatars/8196364bffed71086ba9a0a4a62027d3.svg
paper_authors.fullname: Jiajun Wu
paper_authors.type: user
paper_authors.user: jiajunwu
paper_authors._id: 6583c913a4af83584c8e17c7
paper_authors.name: Yunzhu Li
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-12-29T19:43:46.423Z
paper_authors._id: 64a46562c641afb468a3dc04
paper_authors.avatarUrl: /avatars/bc54e5c13f657251b05bf2725086e56d.svg
paper_authors.fullname: Yunzhu Li
paper_authors.type: user
paper_authors.user: yunzhuli
paper_title: Model-Based Control with Sparse Neural Dynamics
paper_summary: 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.
paper_ai_summary: A new framework sparsifies ReLU neural models for predictive control, enabling efficient optimization and handling complex tasks with improved performance.
paper_ai_keywords: 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