Adaptive PD Control using Deep Reinforcement Learning for Local-Remote Teleoperation with Stochastic Time Delays
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Abstract (arXiv)
Local-remote systems allow robots to execute complex tasks in hazardous environments such as space and nuclear power stations. However, establishing accurate positional mapping between local and remote devices can be difficult due to time delays that can compromise system performance and stability. Enhancing the synchronicity and stability of local-remote systems is vital for enabling robots to interact with environments at greater distances and under highly challenging network conditions, including time delays. We introduce an adaptive control method employing reinforcement learning to tackle the time-delayed control problem. By adjusting controller parameters in real-time, this adaptive controller compensates for stochastic delays and improves synchronicity between local and remote robotic manipulators. To improve the adaptive PD controller's performance, we devise a model-based reinforcement learning approach that effectively incorporates multi-step delays into the learning framework. Utilizing this proposed technique, the local-remote system's performance is stabilized for stochastic communication time-delays of up to 290ms. Our results demonstrate that the suggested model-based reinforcement learning method surpasses the Soft-Actor Critic and augmented state Soft-Actor Critic techniques. Access the code at: https://github.com/CAV-Research-Lab/Predictive-Model-Delay-Correction
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 26 May 2023.
Builds on
9 of 30 references.
References · 9
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
- 2018Soft Actor-Critic Algorithms and Applicationscited by 1,179
- 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 748
- 2018Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Researchcited by 256
- 2020Reinforcement Learning with Random Delayscited by 56
- 2020Delay-Aware Model-Based Reinforcement Learning for Continuous Controlcited by 33
- 2021Acting in Delayed Environments with Non-Stationary Markov Policiescited by 28
- 2018At Human Speed: Deep Reinforcement Learning with Action Delaycited by 24
- 2021Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delayscited by 20
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Citing papers per year: 2026: 1.
1 paper.