ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility

• URL: http://arxiv.org/abs/2309.16909v2
• Date: Thu, 29 Feb 2024 07:05:52 GMT
• Title: ASAP: Automated Sequence Planning for Complex Robotic Assembly with Physical Feasibility
• Authors: Yunsheng Tian, Karl D.D. Willis, Bassel Al Omari, Jieliang Luo, Pingchuan Ma, Yichen Li, Farhad Javid, Edward Gu, Joshua Jacob, Shinjiro Sueda, Hui Li, Sachin Chitta and Wojciech Matusik
• Abstract summary: We present ASAP, a physics-based planning approach for automatically generating a sequence for general-shaped assemblies. A search can be guided by either geometrics or graph neural networks trained on data with simulation labels. We show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies.
• Score: 27.424678100675163
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The automated assembly of complex products requires a system that can automatically plan a physically feasible sequence of actions for assembling many parts together. In this paper, we present ASAP, a physics-based planning approach for automatically generating such a sequence for general-shaped assemblies. ASAP accounts for gravity to design a sequence where each sub-assembly is physically stable with a limited number of parts being held and a support surface. We apply efficient tree search algorithms to reduce the combinatorial complexity of determining such an assembly sequence. The search can be guided by either geometric heuristics or graph neural networks trained on data with simulation labels. Finally, we show the superior performance of ASAP at generating physically realistic assembly sequence plans on a large dataset of hundreds of complex product assemblies. We further demonstrate the applicability of ASAP on both simulation and real-world robotic setups. Project website: asap.csail.mit.edu

Related papers

• COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
  COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems. A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
  arXiv  Detail & Related papers  (2024-09-23T15:53:41Z)
• Physics-Aware Combinatorial Assembly Sequence Planning using Data-free Action Masking [6.919208054874144]
  Combinatorial assembly uses standardized unit primitives to build objects that satisfy user specifications. This paper studies assembly sequence planning (ASP) for physical assembly. We employ deep reinforcement learning to learn a construction policy for placing unit primitives sequentially to build the desired object.
  arXiv  Detail & Related papers  (2024-08-19T17:16:35Z)
• TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick Assembly [51.29305265324916]
  We propose a class-agnostic tree-transformer framework to predict the sequential assembly actions from input multi-view images. A major challenge of the sequential brick assembly task is that the step-wise action labels are costly and tedious to obtain in practice. We mitigate this problem by leveraging synthetic-to-real transfer learning.
  arXiv  Detail & Related papers  (2024-07-22T14:05:27Z)
• Multi-level Reasoning for Robotic Assembly: From Sequence Inference to Contact Selection [74.40109927350856]
  We present the Part Assembly Sequence Transformer (PAST) to infer assembly sequences from a target blueprint. We then use a motion planner and optimization to generate part movements and contacts. Experimental results show that our approach generalizes better than prior methods.
  arXiv  Detail & Related papers  (2023-12-17T00:47:13Z)
• Embodied Task Planning with Large Language Models [86.63533340293361]
  We propose a TAsk Planing Agent (TaPA) in embodied tasks for grounded planning with physical scene constraint. During inference, we discover the objects in the scene by extending open-vocabulary object detectors to multi-view RGB images collected in different achievable locations. Experimental results show that the generated plan from our TaPA framework can achieve higher success rate than LLaVA and GPT-3.5 by a sizable margin.
  arXiv  Detail & Related papers  (2023-07-04T17:58:25Z)
• Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation Learning [22.447462847331312]
  We propose a holistic graphical approach including a graph representation called Assembly Graph for product assemblies. With GRACE, we are able to extract meaningful information from the graph input and predict assembly sequences in a step-by-step manner. In experiments, we show that our approach can predict feasible assembly sequences across product variants of aluminum profiles.
  arXiv  Detail & Related papers  (2023-03-17T17:23:14Z)
• The Basis of Design Tools for Quantum Computing: Arrays, Decision Diagrams, Tensor Networks, and ZX-Calculus [55.58528469973086]
  Quantum computers promise to efficiently solve important problems classical computers never will. A fully automated quantum software stack needs to be developed. This work provides a look "under the hood" of today's tools and showcases how these means are utilized in them, e.g., for simulation, compilation, and verification of quantum circuits.
  arXiv  Detail & Related papers  (2023-01-10T19:00:00Z)
• Automata Cascades: Expressivity and Sample Complexity [90.53326983143644]
  We show that cascades allow for describing the sample complexity of automata in terms of their components. Our results show that one can in principle learn automata with infinite input alphabets and a number of states that is exponential in the amount of data available.
  arXiv  Detail & Related papers  (2022-11-25T11:02:33Z)
• Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery [34.25379651790627]
  We tackle the problem of building arbitrary, predefined target structures entirely from scratch using a set of Tetris-like building blocks and a robotic manipulator. Our novel hierarchical approach aims at efficiently decomposing the overall task into three feasible levels that benefit mutually from each other.
  arXiv  Detail & Related papers  (2022-03-08T14:44:51Z)
• Cooperative Task and Motion Planning for Multi-Arm Assembly Systems [32.56644393804845]
  Multi-robot assembly systems are becoming increasingly appealing in manufacturing. Planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging. We present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures.
  arXiv  Detail & Related papers  (2022-03-04T18:12:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.