Energy-based regularization on residual dynamics learning improves neural MPC for aerial robots, cutting positional error 23% versus analytical models and boosting stability over unregularized neural MPC in real flights.
Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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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
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Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots
Energy-based regularization on residual dynamics learning improves neural MPC for aerial robots, cutting positional error 23% versus analytical models and boosting stability over unregularized neural MPC in real flights.
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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.