Safe RL by restricting policies to forward-invariant stabilizing actions, demonstrated on quadcopter hover control.
Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high- speed trajectories
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Self-supervised residual learning from trajectory data forms a hybrid dynamics model that enables trajectory optimization to produce aggressive yet precisely trackable motions for quadrotors.
Fuzzy logic-based adaptive reward shaping improves RL convergence speed, reduces variability, and boosts success rates by up to 5% in drone racing simulations compared to standard rewards.
citing papers explorer
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Learning over Forward-Invariant Policy Classes: Reinforcement Learning without Safety Concerns
Safe RL by restricting policies to forward-invariant stabilizing actions, demonstrated on quadcopter hover control.
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Optimizing Control-Friendly Trajectories with Self-Supervised Residual Learning
Self-supervised residual learning from trajectory data forms a hybrid dynamics model that enables trajectory optimization to produce aggressive yet precisely trackable motions for quadrotors.
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Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS)
Fuzzy logic-based adaptive reward shaping improves RL convergence speed, reduces variability, and boosts success rates by up to 5% in drone racing simulations compared to standard rewards.