DreamLander: A 6-DOF World Model based Reinforcement Learning Planetary Lander
Overview
This paper introduces DreamLander, a model-based reinforcement learning algorithm based on DreamerV3 designed for optimal guidance and control in 6-degree-of-freedom planetary entry descent landing. The approach uses state sensor observations to achieve high accuracy, with simulated landings within 2 meters of the target zone and velocity norms below 0.3 m/s, demonstrating sample efficiency and generalizability across scenarios.
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Reported quantity · landing accuracy: 2 other · Basis not reported
“based approach creates a sample efficient and highly generalizable model that can be applied to a variety of scenarios whilst maintaining a high degree of accuracy, landing within 2 meters of the designated landing zone, and with velocity norm less than 0.3 meters per second. DreamLander makes use of state sensor observations, making progress toward End-to-End solutions of the Entry Descent Landing problem.”
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Reported quantity · velocity norm: 0.3 other · Basis not reported
“based approach creates a sample efficient and highly generalizable model that can be applied to a variety of scenarios whilst maintaining a high degree of accuracy, landing within 2 meters of the designated landing zone, and with velocity norm less than 0.3 meters per second. DreamLander makes use of state sensor observations, making progress toward End-to-End solutions of the Entry Descent Landing problem.”
Exact source · revision 1Source owner not reported
Reported quantity · DOF: 6 other · Basis not reported · Differing source assertions
“DreamLander: A 6-DOF World Model based Reinforcement Learning Planetary Lander”
Exact source · revision 1Source owner not reported
Reported quantity · DOF: 6 other · Basis not reported · Differing source assertions
“d control. Model based reinforcement learning is proving to be an important tool in simplifying the highly complex process of generating optimal guidance trajectories and control. In this work, we present DreamLander, a state of the art model based reinforcement learning algorithm based on DreamerV3, to generate optimal guidance and control for a realistic 6 degree of freedom entry descent landing problem. The model”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalDreamLander: A 6-DOF World Model based Reinforcement Learning Planetary Lander
This paper introduces DreamLander, a model-based reinforcement learning algorithm based on DreamerV3 designed for optimal guidance and control in 6-degree-of-freedom planetary entry descent landing. The approach uses state sensor observations to achieve high accuracy, with simulated landings within 2 meters of the target zone and velocity norms below 0.3 m/s, demonstrating sample efficiency and generalizability across scenarios.
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