9 citations · 16 across the 3 of their papers we have counts for
7 papers · 1 filter
Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation
Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski +14
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e.g., in cluttered home environments or in human-occupied publi…
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar +5
General-purpose robotic systems must master a large repertoire of diverse skills to be useful in a range of daily tasks. While reinforcement learning provides a powerful framework…
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Yevgen Chebotar, Karol Hausman, Yao Lu +8
We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional online exploration, a se…
Visionary: Vision architecture discovery for robot learning
Iretiayo Akinola, Anelia Angelova, Yao Lu +5
We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and high dimensional visua…
Disentangled Planning and Control in Vision Based Robotics via Reward Machines
Alberto Camacho, Jacob Varley, Deepali Jain +2
In this work we augment a Deep Q-Learning agent with a Reward Machine (DQRM) to increase speed of learning vision-based policies for robot tasks, and overcome some of the limitatio…
Learning Precise 3D Manipulation from Multiple Uncalibrated Cameras
Iretiayo Akinola, Jacob Varley, Dmitry Kalashnikov
In this work, we present an effective multi-view approach to closed-loop end-to-end learning of precise manipulation tasks that are 3D in nature. Our method learns to accomplish th…