title: Dynamic-Resolution Model Learning for Object Pile Manipulation
summary: Dynamics models learned from visual observations have shown to be effective
  in various robotic manipulation tasks. One of the key questions for learning
  such dynamics models is what scene representation to use. Prior works typically
  assume representation at a fixed dimension or resolution, which may be
  inefficient for simple tasks and ineffective for more complicated tasks. In
  this work, we investigate how to learn dynamic and adaptive representations at
  different levels of abstraction to achieve the optimal trade-off between
  efficiency and effectiveness. Specifically, we construct dynamic-resolution
  particle representations of the environment and learn a unified dynamics model
  using graph neural networks (GNNs) that allows continuous selection of the
  abstraction level. During test time, the agent can adaptively determine the
  optimal resolution at each model-predictive control (MPC) step. We evaluate our
  method in object pile manipulation, a task we commonly encounter in cooking,
  agriculture, manufacturing, and pharmaceutical applications. Through
  comprehensive evaluations both in the simulation and the real world, we show
  that our method achieves significantly better performance than state-of-the-art
  fixed-resolution baselines at the gathering, sorting, and redistribution of
  granular object piles made with various instances like coffee beans, almonds,
  corn, etc.
paper_authors._id: 649e53f81b00eaf1eeb03faf
paper_authors.name: Yixuan Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-03T07:27:19.571Z
paper_authors._id: 649f4eca850cf879852b6cbc
paper_authors.avatarUrl: /avatars/1253925dce9875eaa54332be59149a1b.svg
paper_authors.fullname: Yixuan Wang
paper_authors.type: user
paper_authors.user: yixuan1999
paper_authors._id: 649e53f81b00eaf1eeb03fb0
paper_authors.name: Yunzhu Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-04T19:17:25.073Z
paper_authors._id: 64a46562c641afb468a3dc04
paper_authors.avatarUrl: /avatars/bc54e5c13f657251b05bf2725086e56d.svg
paper_authors.fullname: Yunzhu Li
paper_authors.type: user
paper_authors.user: yunzhuli
paper_authors._id: 649e53f81b00eaf1eeb03fb1
paper_authors.name: Katherine Driggs-Campbell
paper_authors._id: 649e53f81b00eaf1eeb03fb2
paper_authors.name: Li Fei-Fei
paper_authors._id: 649e53f81b00eaf1eeb03fb3
paper_authors.name: Jiajun Wu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-30T20:55:55.157Z
paper_authors._id: 647b7b8345616c3de3627863
paper_authors.avatarUrl: /avatars/8196364bffed71086ba9a0a4a62027d3.svg
paper_authors.fullname: Jiajun Wu
paper_authors.type: user
paper_authors.user: jiajunwu
paper_title: Dynamic-Resolution Model Learning for Object Pile Manipulation
paper_summary: Dynamics models learned from visual observations have shown to be effective
  in various robotic manipulation tasks. One of the key questions for learning
  such dynamics models is what scene representation to use. Prior works typically
  assume representation at a fixed dimension or resolution, which may be
  inefficient for simple tasks and ineffective for more complicated tasks. In
  this work, we investigate how to learn dynamic and adaptive representations at
  different levels of abstraction to achieve the optimal trade-off between
  efficiency and effectiveness. Specifically, we construct dynamic-resolution
  particle representations of the environment and learn a unified dynamics model
  using graph neural networks (GNNs) that allows continuous selection of the
  abstraction level. During test time, the agent can adaptively determine the
  optimal resolution at each model-predictive control (MPC) step. We evaluate our
  method in object pile manipulation, a task we commonly encounter in cooking,
  agriculture, manufacturing, and pharmaceutical applications. Through
  comprehensive evaluations both in the simulation and the real world, we show
  that our method achieves significantly better performance than state-of-the-art
  fixed-resolution baselines at the gathering, sorting, and redistribution of
  granular object piles made with various instances like coffee beans, almonds,
  corn, etc.
paper_ai_summary: A dynamic-resolution particle representation using graph neural networks achieves better performance in object pile manipulation tasks compared to fixed-resolution models.
paper_ai_keywords: graph neural networks dynamic-resolution particle representations model-predictive control object pile manipulation