Media Summary: Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously ... Quadrotors are highly nonlinear dynamical systems that require carefully tuned controllers to be pushed to their physical limits. A team of engineers from Caltech has developed Neural-

Learning Perception Aware Agile Flight - Detailed Analysis & Overview

Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously ... Quadrotors are highly nonlinear dynamical systems that require carefully tuned controllers to be pushed to their physical limits. A team of engineers from Caltech has developed Neural- Speaker: Dr. Davide Scaramuzza Affiliation: University of Zurich This talk was presented at the 8th IFAC Conference on Nonlinear ... IFAC 2020 Talk Most vision-based navigation systems rely on Model Predictive Control (MPC)-based approach to

Talk by RPG PhD Student Antonio Loquercio on Code: Paper: Abstract: This paper presents PANTHER, ... Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection ... Quadrotor navigation in unknown environments is critical for practical missions such as search-and-rescue. Solving this problem ...

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Learning Perception-Aware Agile Flight in Cluttered Environment (ICRA 2023)
Learning from the best: Inverse reinforcement learning for agile drone flight.
A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight (ICRA 2022)
Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds
NMPC 2024 - Learning agile, vision-based drone flight: from simulation to reality
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight (CoRL 2024)
A Perception-Aware Flatness-based MPC for Fast Vision-based Flight
Learning High-Speed Flight in the Wild (Science Robotics, 2021)
Learning Vision-based Agile Drone Flight: from Simulation to Reality
Optimal and Reactive Control for Agile Drone Flight in Cluttered Environments
PAL 2020 Invited Talk: Learning vision-based, agile drone flight: from perception to action
Learning Vision-based Agile Drone Flight
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Learning Perception-Aware Agile Flight in Cluttered Environment (ICRA 2023)

Learning Perception-Aware Agile Flight in Cluttered Environment (ICRA 2023)

Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously ...

Learning from the best: Inverse reinforcement learning for agile drone flight.

Learning from the best: Inverse reinforcement learning for agile drone flight.

This research introduces a

Sponsored
A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight (ICRA 2022)

A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight (ICRA 2022)

Quadrotors are highly nonlinear dynamical systems that require carefully tuned controllers to be pushed to their physical limits.

Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds

Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds

A team of engineers from Caltech has developed Neural-

NMPC 2024 - Learning agile, vision-based drone flight: from simulation to reality

NMPC 2024 - Learning agile, vision-based drone flight: from simulation to reality

Speaker: Dr. Davide Scaramuzza Affiliation: University of Zurich This talk was presented at the 8th IFAC Conference on Nonlinear ...

Sponsored
Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight (CoRL 2024)

Bootstrapping Reinforcement Learning with Imitation for Vision-Based Agile Flight (CoRL 2024)

Learning

A Perception-Aware Flatness-based MPC for Fast Vision-based Flight

A Perception-Aware Flatness-based MPC for Fast Vision-based Flight

IFAC 2020 Talk Most vision-based navigation systems rely on

Learning High-Speed Flight in the Wild (Science Robotics, 2021)

Learning High-Speed Flight in the Wild (Science Robotics, 2021)

Quadrotors are

Learning Vision-based Agile Drone Flight: from Simulation to Reality

Learning Vision-based Agile Drone Flight: from Simulation to Reality

In this talk, we present our work on

Optimal and Reactive Control for Agile Drone Flight in Cluttered Environments

Optimal and Reactive Control for Agile Drone Flight in Cluttered Environments

Model Predictive Control (MPC)-based approach to

PAL 2020 Invited Talk: Learning vision-based, agile drone flight: from perception to action

PAL 2020 Invited Talk: Learning vision-based, agile drone flight: from perception to action

ICRA 2020 workshop on "

Learning Vision-based Agile Drone Flight

Learning Vision-based Agile Drone Flight

Antonio Loquercio.

UZH Deep Learning Symposium: Learning Agile Filght

UZH Deep Learning Symposium: Learning Agile Filght

Talk by RPG PhD Student Antonio Loquercio on

PANTHER: Perception-Aware Trajectory Planner in Dynamic Environments

PANTHER: Perception-Aware Trajectory Planner in Dynamic Environments

Code: https://github.com/mit-acl/panther Paper: http://arxiv.org/abs/2103.06372 Abstract: This paper presents PANTHER, ...

Perception-Aware Perching on Powerlines with Multirotors (RAL 2022)

Perception-Aware Perching on Powerlines with Multirotors (RAL 2022)

Multirotor aerial robots are becoming widely used for the inspection of powerlines. To enable continuous, robust inspection ...

Time-Optimal Online Replanning for Agile Quadrotor Flight (RAL 2022)

Time-Optimal Online Replanning for Agile Quadrotor Flight (RAL 2022)

In this paper, we tackle the problem of

PA-MPPI: Perception-Aware MPPI Control for Quadrotor Navigation in Unknown Environments (RA-L'26)

PA-MPPI: Perception-Aware MPPI Control for Quadrotor Navigation in Unknown Environments (RA-L'26)

Quadrotor navigation in unknown environments is critical for practical missions such as search-and-rescue. Solving this problem ...

Antonio Loquercio on Agile Autonomy: Learning High-Speed Flight | Toronto AIR Seminar

Antonio Loquercio on Agile Autonomy: Learning High-Speed Flight | Toronto AIR Seminar

Abstract: Quadrotors are among the most

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