Search Results for author:
Found 9 papers, 5 papers with code
Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
Our experiments, performed in simulation and the real world onboard a highly agile quadrotor platform, demonstrate the capabilities of the described system to run learned models with, previously infeasible, large modeling capacity using gradient-based online optimization MPC.
Learning High-Speed Flight in the Wild
Indeed, the subtasks are executed sequentially, leading to increased processing latency and a compounding of errors through the pipeline.
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning
Professional race-car drivers can execute extreme overtaking maneuvers.
Autonomous Drone Racing with Deep Reinforcement Learning
In many robotic tasks, such as autonomous drone racing, the goal is to travel through a set of waypoints as fast as possible.
Data-Driven MPC for Quadrotors
Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors extremely challenging.
Gaussian Processes
Robotics
Flightmare: A Flexible Quadrotor Simulator
State-of-the-art quadrotor simulators have a rigid and highly-specialized structure: either are they really fast, physically accurate, or photo-realistic.
Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning
Autonomous car racing is a major challenge in robotics.
Deep Drone Acrobatics
In this paper, we propose to learn a sensorimotor policy that enables an autonomous quadrotor to fly extreme acrobatic maneuvers with only onboard sensing and computation.
Robotics
AlphaPilot: Autonomous Drone Racing
This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning.
