activity
20182022
most citedCtrl-Z: Recovering from Instability in Reinforcement Learning

13 citations · 20 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV20225 cited

Improving Road Segmentation in Challenging Domains Using Similar Place Priors

Connor Malone, Sourav Garg, Ming Xu +2

Road segmentation in challenging domains, such as night, snow or rain, is a difficult task. Most current approaches boost performance using fine-tuning, domain adaptation, style tr…

cs.RO2021

Refractive Light-Field Features for Curved Transparent Objects in Structure from Motion

Dorian Tsai, Peter Corke, Thierry Peynot +1

Curved refractive objects are common in the human environment, and have a complex visual appearance that can cause robotic vision algorithms to fail. Light-field cameras allow us t…

cs.LG201913 cited

Ctrl-Z: Recovering from Instability in Reinforcement Learning

Vibhavari Dasagi, Jake Bruce, Thierry Peynot +1

When learning behavior, training data is often generated by the learner itself; this can result in unstable training dynamics, and this problem has particularly important applicati…

cs.RO20192 cited

LookUP: Vision-Only Real-Time Precise Underground Localisation for Autonomous Mining Vehicles

Fan Zeng, Adam Jacobson, David Smith +3

A key capability for autonomous underground mining vehicles is real-time accurate localisation. While significant progress has been made, currently deployed systems have several li…

cs.CV2018

Distinguishing Refracted Features using Light Field Cameras with Application to Structure from Motion

Dorian Tsai, Donald G Dansereau, Thierry Peynot +1

Robots must reliably interact with refractive objects in many applications; however, refractive objects can cause many robotic vision algorithms to become unreliable or even fail,…