RL framework for agile drone racing combines task-aware switching and physically informed procedural track generation to achieve 7.4x better zero-shot generalization to unseen tracks while maintaining competitive speeds.
Time-optimal flight with safety constraints and datadriven dynamics
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.RO 4roles
method 1polarities
use method 1representative citing papers
NavRL++ improves sim-to-real transfer for RL navigation via empirical analysis of perturbations, perturbation-aware fine-tuning, and a Transformer temporal policy, with real-world validation showing outperformance over learning baselines and parity with optimization planners in static cases.
TAG-K combines greedy randomized Kaczmarz row selection with tail averaging to deliver faster convergence and noise robustness for online inertial parameter estimation in robotics.
citing papers explorer
-
Bridging Performance and Generalization in Reinforcement Learning for Agile Flight
RL framework for agile drone racing combines task-aware switching and physically informed procedural track generation to achieve 7.4x better zero-shot generalization to unseen tracks while maintaining competitive speeds.
-
NavRL++: A System-Level Framework for Improving Sim-to-Real Transfer in Reinforcement Learning-Based Robot Navigation
NavRL++ improves sim-to-real transfer for RL navigation via empirical analysis of perturbations, perturbation-aware fine-tuning, and a Transformer temporal policy, with real-world validation showing outperformance over learning baselines and parity with optimization planners in static cases.
-
TAG-K: Tail-Averaged Greedy Kaczmarz for Computationally Efficient and Performant Online Inertial Parameter Estimation
TAG-K combines greedy randomized Kaczmarz row selection with tail averaging to deliver faster convergence and noise robustness for online inertial parameter estimation in robotics.
- Accurate Trajectory Tracking with Model Predictive Contouring Control for Bird-Scale Flapping-Wing MAVs