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Found 12 papers, 3 papers with code
Learning Quadruped Locomotion Using Differentiable Simulation
This work provides several important insights into using differentiable simulations for legged locomotion in the real world.
Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learning
A central question in robotics is how to design a control system for an agile mobile robot.
Contrastive Initial State Buffer for Reinforcement Learning
In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples.
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight
The utility of a robot greatly depends on its ability to perform a task in the real world, outside of a well-controlled lab environment.
Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing
The resulting policy directly infers control commands with feature representations learned from raw images, forgoing the need for globally-consistent state estimation, trajectory planning, and handcrafted control design.
Learning Perception-Aware Agile Flight in Cluttered Environments
Recently, neural control policies have outperformed existing model-based planning-and-control methods for autonomously navigating quadrotors through cluttered environments in minimum time.
Policy Search for Model Predictive Control with Application to Agile Drone Flight
In this work, we provide an answer by using policy search for automatically choosing high-level decision variables for MPC, which leads to a novel policy-search-for-model-predictive-control framework.
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.
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.
Learning High-Level Policies for Model Predictive Control
In this work, we leverage probabilistic decision-making approaches and the generalization capability of artificial neural networks to the powerful online optimization by learning a deep high-level policy for the MPC (High-MPC).
