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Found 9 papers, 7 papers with code
What is the Impact of Releasing Code with Publications? Statistics from the Machine Learning, Robotics, and Control Communities
Open-sourcing research publications is a key enabler for the reproducibility of studies and the collective scientific progress of a research community.
safe-control-gym: a Unified Benchmark Suite for Safe Learning-based Control and Reinforcement Learning in Robotics
In recent years, both reinforcement learning and learning-based control -- as well as the study of their safety, which is crucial for deployment in real-world robots -- have gained significant traction.
Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning
The last half-decade has seen a steep rise in the number of contributions on safe learning methods for real-world robotic deployments from both the control and reinforcement learning communities.
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.
Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks.
Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots
Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks.
Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving
In this article, we present a safe and efficient framework that enables the learning of driving policies for autonomous vehicles operating in a shared workspace, where the absence of collisions cannot be guaranteed.
Decentralized Connectivity Control in Quadcopters: a Field Study of Communication Performance
Redundancy and parallelism make decentralized multi-robot systems appealing solutions for the exploration of extreme environments.
Robotics Multiagent Systems C.2.4; C.4; I.2.9
An Adversarial Approach to Private Flocking in Mobile Robot Teams
We present a method that generates private flocking controllers that hide the identity of the leader robot.
