Robotic automation for pick and place task has vast applications. Deep Reinforcement Learning (DRL) is one of the leading robotic automation technique that has been able to achieve dexterous manipulation and locomotion robotics skills . However, a major drawback of using DRL is the Data hungry training regime of DRL that requires millions of trial and error attempts, impractical in real robotic hardware. We propose a multi-subtask reinforcement learning method where complex tasks can be decomposed into low-level subtasks  . These subtasks can be parametrised as expert networks and learnt via existing DRL methods. The trained subtasks can be choreographed by a high-level synthesizer . As a test bed , we use a pick and place robotic simulator , and transfer the learnt behaviour in a real robotic system. We show that our method outperforms imitation learning based method and reaches  high success rate compared to an end-to-end learning approach. Furthermore, we investigate the trained subtasks to demonstrate a adaptive behaviour by fine-tuning a subset of subtasks on a different task. Our approach deviates from the end-to-end learning strategy and provide an initial direction towards learning modular task representations that can generate robust behaviours .
 Deep Reinforcement Learning (DRL) is emerging as a promising approach to generate adaptive behaviors for robotic platforms . However, a major drawback of using DRL is the data-hungry training regime that requires millions of trial and error attempts, which is impractical when running experiments on robotic systems. To address this issue, we propose a multi-subtask reinforcement learning method where complex tasks are decomposed manually into low-level subtasks by leveraging human domain knowledge . These subtasks can be parametrized as expert networks and learned via existing DRL methods. Trained subtasks can then be composed with a high-level choreographer . As a testbed , we use a pick and place robotic simulator to demonstrate our methodology, and show that our method outperforms an imitation learning-based method and reaches a high success rate compared to an end-to-end learning approach. Moreover, we transfer the learned behavior in a different robotic environment that allows us to exploit sim-to-real transfer and demonstrate the trajectories in a real robotic system. Our training regime is carried out using a central processing unit (CPU)-based system, which demonstrates the data-efficient properties of our approach .
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Robotic automation for pick and place task has vast applications.
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one of the leading robotic automation technique that has been able to achieve dexterous manipulation and locomotion robotics skills
emerging as a promising approach to generate adaptive behaviors for robotic platforms
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Data hungry training regime of DRL
data-hungry training regime
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impractical in real robotic hardware. We
which is impractical when running experiments on robotic systems. To address this issue, we
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can be decomposed
are decomposed manually
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by leveraging human domain knowledge
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parametrised
parametrized
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learnt
learned
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The trained subtasks can be choreographed by
Trained subtasks can then be composed with
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synthesizer
choreographer
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test bed
testbed
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, and transfer the learnt behaviour in a real robotic system. We
to demonstrate our methodology, and
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imitation learning based
an imitation learning-based
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a
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Furthermore, we investigate the trained subtasks to demonstrate a adaptive behaviour by fine-tuning a subset of subtasks on a different task. Our approach deviates from the end-to-end learning strategy and provide an initial direction towards learning modular task representations that can generate robust behaviours
Moreover, we transfer the learned behavior in a different robotic environment that allows us to exploit sim-to-real transfer and demonstrate the trajectories in a real robotic system. Our training regime is carried out using a central processing unit (CPU)-based system, which demonstrates the data-efficient properties of our approach