ArticleSensors (Basel, Switzerland)2021
Cooperative Object Transportation Using Curriculum-Based Deep Reinforcement Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A Review of Multi-Robot Systems and Soft Robotics: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2025Review
- Deep-Reinforcement-Learning-Based Object Transportation Using Task Space Decomposition.Sensors (Basel, Switzerland) · 2023Article
- Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey.Sensors (Basel, Switzerland) · 2023Review
- Estimation and Control of Cooperative Aerial Manipulators for a Payload with an Arbitrary Center-of-Mass.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
This paper presents a cooperative object transportation technique using deep reinforcement learning (DRL) based on curricula. Previous studies on object transportation highly depended on complex and intractable controls, such as grasping, pushing, and caging. Recently, DRL-based object transportation techniques have been proposed, which showed improved performance without precise controller design. However, DRL-based techniques not only take a long time to learn their policies but also sometimes fail to learn. It is difficult to learn the policy of DRL by random actions only. Therefore, we propose two curricula for the efficient learning of object transportation:
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