713 citations · 2.1k across the 11 of their papers we have counts for
12 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…
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Michael Ahn, Anthony Brohan, Noah Brown +42
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extend…
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Julian Ibarz, Jie Tan, Chelsea Finn +3
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low level sensor observations. Although a large portion of de…
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
Cristian Bodnar, Adrian Li, Karol Hausman +2
The distributional perspective on reinforcement learning (RL) has given rise to a series of successful Q-learning algorithms, resulting in state-of-the-art performance in arcade ga…
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Mengyuan Yan, Adrian Li, Mrinal Kalakrishnan +1
Many previous works approach vision-based robotic grasping by training a value network that evaluates grasp proposals. These approaches require an optimization process at run-time…
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor +8
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach. We study this problem in the context of g…
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart +9
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative…
End-to-End Learning of Semantic Grasping
Eric Jang, Sudheendra Vijayanarasimhan, Peter Pastor +2
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images. Inspired by the two-stream hypothesis…
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Sergey Levine, Peter Pastor, Alex Krizhevsky +1
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convoluti…
Probabilistic Object Tracking using a Range Camera
Manuel Wüthrich, Peter Pastor, Mrinal Kalakrishnan +2
We address the problem of tracking the 6-DoF pose of an object while it is being manipulated by a human or a robot. We use a dynamic Bayesian network to perform inference and compu…
Probabilistic Depth Image Registration incorporating Nonvisual Information
Manuel Wüthrich, Peter Pastor, Ludovic Righetti +2
In this paper, we derive a probabilistic registration algorithm for object modeling and tracking. In many robotics applications, such as manipulation tasks, nonvisual information a…
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Alexander Herzog, Ludovic Righetti, Felix Grimminger +2
Recently several hierarchical inverse dynamics controllers based on cascades of quadratic programs have been proposed for application on torque controlled robots. They have importa…