PriPG-RL trains RL policies for POMDPs by distilling knowledge from a privileged anytime-feasible MPC planner into a P2P-SAC policy, improving sample efficiency and performance in partially observable robotic navigation.
Partially observable markov decision processes in robotics: A survey,
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EfficientTrack integrates model-based learning and closed-loop dynamics to minimize tracking errors in excavator trajectory control with high efficiency and precision, outperforming prior learning-based methods in simulation and real-world tests.
citing papers explorer
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PriPG-RL: Privileged Planner-Guided Reinforcement Learning for Partially Observable Systems with Anytime-Feasible MPC
PriPG-RL trains RL policies for POMDPs by distilling knowledge from a privileged anytime-feasible MPC planner into a P2P-SAC policy, improving sample efficiency and performance in partially observable robotic navigation.
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High-Precision and High-Efficiency Trajectory Tracking for Excavators Based on Closed-Loop Dynamics
EfficientTrack integrates model-based learning and closed-loop dynamics to minimize tracking errors in excavator trajectory control with high efficiency and precision, outperforming prior learning-based methods in simulation and real-world tests.