Trajectory Planning for Teleoperated Space Manipulators Using Deep Reinforcement Learning
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
Trajectory planning for teleoperated space manipulators involves challenges such as accurately modeling system dynamics, particularly in free-floating modes with non-holonomic constraints, and managing time delays that increase model uncertainty and affect control precision. Traditional teleoperation methods rely on precise dynamic models requiring complex parameter identification and calibration, while data-driven methods do not require prior knowledge but struggle with time delays. A novel framework utilizing deep reinforcement learning (DRL) is introduced to address these challenges. The framework incorporates three methods: Mapping, Prediction, and State Augmentation, to handle delays when delayed state information is received at the master end. The Soft Actor Critic (SAC) algorithm processes the state information to compute the next action, which is then sent to the remote manipulator for environmental interaction. Four environments are constructed using the MuJoCo simulation platform to account for variations in base and target fixation: fixed base and target, fixed base with rotated target, free-floating base with fixed target, and free-floating base with rotated target. Extensive experiments with both constant and random delays are conducted to evaluate the proposed methods. Results demonstrate that all three methods effectively address trajectory planning challenges, with State Augmentation showing superior efficiency and robustness.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 47 references that are in the map. First posted on arXiv 10 Aug 2024.
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13 of 47 references.
References · 13
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- 2026Deep Reinforcement Learning for Adaptive Gain Tuning in Control of Teleoperation Manipulators with Joint Flexibility and Time-Varying Delays
- 2025A nonlinear real time capable motion cueing algorithm based on deep reinforcement learning
- 2024Trajectory Planning of Robotic Manipulator in Dynamic Environment Exploiting DRL
- 2024Reinforcement Learning Controllers for Soft Robots using Learned Environments
- 2025Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators
- 2024Integrating DeepRL with Robust Low-Level Control in Robotic Manipulators for Non-Repetitive Reaching Tasks
- 2026Prior Policy Guided Dual-Agent Coordinated Manipulation Planning of Spacecraft-Manipulator System ↗
- 2024Towards Real-World Efficiency: Domain Randomization in Reinforcement Learning for Pre-Capture of Free-Floating Moving Targets by Autonomous Robots