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arXiv · 2301.01382

Task-sequencing Simulator: Integrated Machine Learning to Execution Simulation for Robot Manipulation

Kazuhiro Sasabuchi·Daichi Saito·Atsushi Kanehira·Naoki Wake·Jun Takamatsu·Katsushi Ikeuchi
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Abstract

A task-sequencing simulator in robotics manipulation to integrate simulation-for-learning and simulation-for-execution is introduced. Unlike existing machine-learning simulation where a non-decomposed simulation is used to simulate a training scenario, the task-sequencing simulator runs a composed simulation using building blocks. This way, the simulation-for-learning is structured similarly to a multi-step simulation-for-execution. To compose both learning and execution scenarios, a unified trainable-and-composable description of blocks called a concept model is proposed and used. Using the simulator design and concept models, a reusable simulator for learning different tasks, a common-ground system for learning-to-execution, simulation-to-real is achieved and shown.

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Kazuhiro Sasabuchi, Daichi Saito, Atsushi Kanehira, Naoki Wake, Jun Takamatsu, Katsushi Ikeuchi. 2023-01-03. Task-sequencing Simulator: Integrated Machine Learning to Execution Simulation for Robot Manipulation. https://arxiv.org/abs/2301.01382

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