Robot learning from randomized simulations: A review
The rise of deep learning has caused a paradigm shift in robotics research, favoring
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
methods that require large amounts of data. Unfortunately, it is prohibitively expensive to …
A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning
This article is about deep learning (DL) and deep reinforcement learning (DRL) works
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
applied to robotics. Both tools have been shown to be successful in delivering data-driven …
Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control
Robotic simulators are crucial for academic research and education as well as the
development of safety-critical applications. Reinforcement learning environments—simple …
development of safety-critical applications. Reinforcement learning environments—simple …
A benchmark comparison of learned control policies for agile quadrotor flight
E Kaufmann, L Bauersfeld… - … Conference on Robotics …, 2022 - ieeexplore.ieee.org
Quadrotors are highly nonlinear dynamical systems that require carefully tuned controllers to
be pushed to their physical limits. Recently, learning-based control policies have been …
be pushed to their physical limits. Recently, learning-based control policies have been …
Regularizing action policies for smooth control with reinforcement learning
A critical problem with the practical utility of controllers trained with deep Reinforcement
Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies …
Learning (RL) is the notable lack of smoothness in the actions learned by the RL policies …
Low-level autonomous control and tracking of quadrotor using reinforcement learning
This paper proposes a low-level quadrotor control algorithm using neural networks with
model-free reinforcement learning, then explores the algorithm's capabilities on quadrotor …
model-free reinforcement learning, then explores the algorithm's capabilities on quadrotor …
[HTML][HTML] Robust quadruped jumping via deep reinforcement learning
In this paper, we consider a general task of jumping varying distances and heights for a
quadrupedal robot in noisy environments, such as off of uneven terrain and with variable …
quadrupedal robot in noisy environments, such as off of uneven terrain and with variable …
Sample factory: Egocentric 3d control from pixels at 100000 fps with asynchronous reinforcement learning
Increasing the scale of reinforcement learning experiments has allowed researchers to
achieve unprecedented results in both training sophisticated agents for video games, and in …
achieve unprecedented results in both training sophisticated agents for video games, and in …
Decentralized control of quadrotor swarms with end-to-end deep reinforcement learning
We demonstrate the possibility of learning drone swarm controllers that are zero-shot
transferable to real quadrotors via large-scale multi-agent end-to-end reinforcement …
transferable to real quadrotors via large-scale multi-agent end-to-end reinforcement …
Neural-swarm: Decentralized close-proximity multirotor control using learned interactions
In this paper, we present Neural-Swarm, a nonlinear decentralized stable controller for close-
proximity flight of multirotor swarms. Close-proximity control is challenging due to the …
proximity flight of multirotor swarms. Close-proximity control is challenging due to the …