research
∙
09/18/2023
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
In this work, we present a scalable reinforcement learning method for tr...
share
research
∙
05/05/2023
Deep RL at Scale: Sorting Waste in Office Buildings with a Fleet of Mobile Manipulators
We describe a system for deep reinforcement learning of robotic manipula...
research
∙
12/13/2022
RT-1: Robotics Transformer for Real-World Control at Scale
By transferring knowledge from large, diverse, task-agnostic datasets, m...
research
∙
11/16/2022
Token Turing Machines
We propose Token Turing Machines (TTM), a sequential, autoregressive Tra...
research
∙
04/04/2022
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
Large language models can encode a wealth of semantic knowledge about th...
research
∙
12/07/2021
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
Safe exploration is critical for using reinforcement learning (RL) in ri...
research
∙
03/26/2021
Visionary: Vision architecture discovery for robot learning
We propose a vision-based architecture search algorithm for robot manipu...
research
∙
02/04/2021
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Deep reinforcement learning (RL) has emerged as a promising approach for...
research
∙
10/29/2020
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
Safety remains a central obstacle preventing widespread use of RL in the...
research
∙
06/16/2020
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real
Deep neural network based reinforcement learning (RL) can learn appropri...
research
∙
04/13/2020
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
We study reinforcement learning in settings where sampling an action fro...
research
∙
06/04/2019
Off-Policy Evaluation via Off-Policy Classification
In this work, we consider the problem of model selection for deep reinfo...
research
∙
12/18/2018
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks
Real world data, especially in the domain of robotics, is notoriously co...
research
∙
06/27/2018
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
In this paper, we study the problem of learning vision-based dynamic man...
research
∙
04/23/2018
Guided Attention for Large Scale Scene Text Verification
Many tasks are related to determining if a particular text string exists...
research
∙
02/28/2018
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
In this paper, we explore deep reinforcement learning algorithms for vis...
research
∙
02/16/2018
Diversity is All You Need: Learning Skills without a Reward Function
Intelligent creatures can explore their environments and learn useful sk...
research
∙
11/18/2017
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
Deep reinforcement learning algorithms can learn complex behavioral skil...
research
∙
09/22/2017
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Instrumenting and collecting annotated visual grasping datasets to train...
research
∙
07/06/2017
End-to-End Learning of Semantic Grasping
We consider the task of semantic robotic grasping, in which a robot pick...
research
∙
05/14/2017
Discrete Sequential Prediction of Continuous Actions for Deep RL
It has long been assumed that high dimensional continuous control proble...
research
∙
04/16/2017
In-Datacenter Performance Analysis of a Tensor Processing Unit
Many architects believe that major improvements in cost-energy-performan...
research
∙
12/17/2015
Large Scale Business Discovery from Street Level Imagery
Search with local intent is becoming increasingly useful due to the popu...
research
∙
12/20/2013