90 citations · 171 across the 11 of their papers we have counts for
16 papers
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators
Alexander Herzog, Kanishka Rao, Karol Hausman +37
We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Rea…
Open-vocabulary Queryable Scene Representations for Real World Planning
Boyuan Chen, Fei Xia, Brian Ichter +5
Large language models (LLMs) have unlocked new capabilities of task planning from human instructions. However, prior attempts to apply LLMs to real-world robotic tasks are limited…
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…
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
Mohi Khansari, Daniel Kappler, Jianlan Luo +2
This paper introduces Action Image, a new grasp proposal representation that allows learning an end-to-end deep-grasping policy. Our model achieves grasp success on re…
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:…
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Allan Zhou, Eric Jang, Daniel Kappler +7
Imitation learning allows agents to learn complex behaviors from demonstrations. However, learning a complex vision-based task may require an impractical number of demonstrations.…