90 citations · 150 across the 7 of their papers we have counts for
13 papers
Practical Imitation Learning in the Real World via Task Consistency Loss
Mohi Khansari, Daniel Ho, Yuqing Du +6
Recent work in visual end-to-end learning for robotics has shown the promise of imitation learning across a variety of tasks. Such approaches are expensive both because they requir…
Bayesian Imitation Learning for End-to-End Mobile Manipulation
Yuqing Du, Daniel Ho, Alexander A. Alemi +2
In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a…
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
Eric Jang, Alex Irpan, Mohi Khansari +5
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning. We approach th…
RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
Daniel Ho, Kanishka Rao, Zhuo Xu +3
The success of deep reinforcement learning (RL) and imitation learning (IL) in vision-based robotic manipulation typically hinges on the expense of large scale data collection. Wit…
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real
Kanishka Rao, Chris Harris, Alex Irpan +3
Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without the need for manuall…
Modeling Long-horizon Tasks as Sequential Interaction Landscapes
Sören Pirk, Karol Hausman, Alexander Toshev +1
Complex object manipulation tasks often span over long sequences of operations. Task planning over long-time horizons is a challenging and open problem in robotics, and its complex…