A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
Champion-level drone racing using deep reinforce- ment learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
aerial-autonomy-stack is a ROS2-based open-source framework that supports faster-than-real-time simulation of complete perception-to-action drone autonomy pipelines while remaining agnostic to PX4 and ArduPilot autopilots.
citing papers explorer
-
Sim-to-Real Transfer and Robustness Evaluation of Reinforcement Learning Control with Integrated Perception on an ASV for Floating Waste Capture
A DRL controller for ASV floating waste capture, trained in simulation with a perception abstraction module, achieves centimeter-level accuracy in real-world field experiments across 14 disturbance regimes.
-
aerial-autonomy-stack -- a Faster-than-real-time, Autopilot-agnostic, ROS2 Framework to Simulate and Deploy Perception-based Drones
aerial-autonomy-stack is a ROS2-based open-source framework that supports faster-than-real-time simulation of complete perception-to-action drone autonomy pipelines while remaining agnostic to PX4 and ArduPilot autopilots.