6 citations · 11 across the 4 of their papers we have counts for
4 papers · 1 filter
Flow-based Domain Randomization for Learning and Sequencing Robotic Skills
Aidan Curtis, Eric Li, Michael Noseworthy +5
Domain randomization in reinforcement learning is an established technique for increasing the robustness of control policies trained in simulation. By randomizing environment prope…
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu +4
Visual servoing enables robotic systems to perform accurate closed-loop control, which is required in many applications. However, existing methods either require precise calibratio…
From proprioception to long-horizon planning in novel environments: A hierarchical RL model
Nishad Gothoskar, Miguel Lázaro-Gredilla, Dileep George
For an intelligent agent to flexibly and efficiently operate in complex environments, they must be able to reason at multiple levels of temporal, spatial, and conceptual abstractio…
Learning a generative model for robot control using visual feedback
Nishad Gothoskar, Miguel Lázaro-Gredilla, Abhishek Agarwal +2
We introduce a novel formulation for incorporating visual feedback in controlling robots. We define a generative model from actions to image observations of features on the end-eff…