Task and Domain Adaptive Reinforcement Learning for Robot Control
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914952369930240 |
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| author | Liu, Yu Tang Singh, Nilaksh Ahmad, Aamir |
| author_facet | Liu, Yu Tang Singh, Nilaksh Ahmad, Aamir |
| contents | Deep reinforcement learning (DRL) has shown remarkable success in simulation domains, yet its application in designing robot controllers remains limited, due to its single-task orientation and insufficient adaptability to environmental changes. To overcome these limitations, we present a novel adaptive agent that leverages transfer learning techniques to dynamically adapt policy in response to different tasks and environmental conditions. The approach is validated through the blimp control challenge, where multitasking capabilities and environmental adaptability are essential. The agent is trained using a custom, highly parallelized simulator built on IsaacGym. We perform zero-shot transfer to fly the blimp in the real world to solve various tasks. We share our code at https://github.com/robot-perception-group/adaptive_agent. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18713 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Task and Domain Adaptive Reinforcement Learning for Robot Control Liu, Yu Tang Singh, Nilaksh Ahmad, Aamir Robotics Artificial Intelligence Systems and Control Deep reinforcement learning (DRL) has shown remarkable success in simulation domains, yet its application in designing robot controllers remains limited, due to its single-task orientation and insufficient adaptability to environmental changes. To overcome these limitations, we present a novel adaptive agent that leverages transfer learning techniques to dynamically adapt policy in response to different tasks and environmental conditions. The approach is validated through the blimp control challenge, where multitasking capabilities and environmental adaptability are essential. The agent is trained using a custom, highly parallelized simulator built on IsaacGym. We perform zero-shot transfer to fly the blimp in the real world to solve various tasks. We share our code at https://github.com/robot-perception-group/adaptive_agent. |
| title | Task and Domain Adaptive Reinforcement Learning for Robot Control |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2404.18713 |