31 citations · 57 across the 3 of their papers we have counts for
4 papers
The Ingredients of Real-World Robotic Reinforcement Learning
Henry Zhu, Justin Yu, Abhishek Gupta +5
The success of reinforcement learning for real world robotics has been, in many cases limited to instrumented laboratory scenarios, often requiring arduous human effort and oversig…
Scalable Multi-Task Imitation Learning with Autonomous Improvement
Avi Singh, Eric Jang, Alexander Irpan +5
While robot learning has demonstrated promising results for enabling robots to automatically acquire new skills, a critical challenge in deploying learning-based systems is scale:…
End-to-End Robotic Reinforcement Learning without Reward Engineering
Avi Singh, Larry Yang, Kristian Hartikainen +2
The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory i…
GPLAC: Generalizing Vision-Based Robotic Skills using Weakly Labeled Images
Avi Singh, Larry Yang, Sergey Levine
We tackle the problem of learning robotic sensorimotor control policies that can generalize to visually diverse and unseen environments. Achieving broad generalization typically re…