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20182023
most citedMulti-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving

4 citations · 6 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.RO2023

A Remote Sim2real Aerial Competition: Fostering Reproducibility and Solutions' Diversity in Robotics Challenges

Spencer Teetaert, Wenda Zhao, Niu Xinyuan +21

Shared benchmark problems have historically been a fundamental driver of progress for scientific communities. In the context of academic conferences, competitions offer the opportu…

cs.RO20222 cited

Characterising the Robustness of Reinforcement Learning for Continuous Control using Disturbance Injection

Catherine R. Glossop, Jacopo Panerati, Amrit Krishnan +2

In this study, we leverage the deliberate and systematic fault-injection capabilities of an open-source benchmark suite to perform a series of experiments on state-of-the-art deep…

cs.RO2021

Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots

Wenda Zhao, Jacopo Panerati, Angela P. Schoellig

Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) time difference of…

cs.RO2021

Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Control

Jacopo Panerati, Hehui Zheng, SiQi Zhou +3

Robotic simulators are crucial for academic research and education as well as the development of safety-critical applications. Reinforcement learning environments -- simple simulat…

cs.RO2020

Learning-based Bias Correction for Ultra-wideband Localization of Resource-constrained Mobile Robots

Wenda Zhao, Abhishek Goudar, Jacopo Panerati +1

Accurate indoor localization is a crucial enabling technology for many robotics applications, from warehouse management to monitoring tasks. Ultra-wideband (UWB) ranging is a promi…

cs.RO20194 cited

Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving

Rupert Mitchell, Jenny Fletcher, Jacopo Panerati +1

Autonomous driving promises to transform road transport. Multi-vehicle and multi-lane scenarios, however, present unique challenges due to constrained navigation and unpredictable…