Task and Domain Adaptive Reinforcement Learning for Robot Control

• URL: http://arxiv.org/abs/2404.18713v3
• Date: Thu, 19 Sep 2024 02:36:53 GMT
• Title: Task and Domain Adaptive Reinforcement Learning for Robot Control
• Authors: Yu Tang Liu, Nilaksh Singh, Aamir Ahmad,
• Abstract summary: We present a novel adaptive agent to dynamically adapt policy in response to different tasks and environmental conditions. 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.
• Score: 0.34137115855910755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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