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18 February 2025 |
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Article overview
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Task-sequencing Simulator: Integrated Machine Learning to Execution Simulation for Robot Manipulation | | Kazuhiro Sasabuchi
; Daichi Saito
; Atsushi Kanehira
; Naoki Wake
; Jun Takamatsu
; Katsushi Ikeuchi
; | | Date: |
3 Jan 2023 | | 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. | | Source: | arXiv, 2301.01382 | | Services: | Forum | Review | PDF | Favorites | |
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