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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...
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04/24/2023
USA-Net: Unified Semantic and Affordance Representations for Robot Memory
In order for robots to follow open-ended instructions like "go open the ...
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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...
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05/13/2020
Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Data
This paper introduces Action Image, a new grasp proposal representation ...
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10/01/2019
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping
The distributional perspective on reinforcement learning (RL) has given ...
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06/07/2019
Watch, Try, Learn: Meta-Learning from Demonstrations and Reward
Imitation learning allows agents to learn complex behaviors from demonst...
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04/15/2019
Learning Probabilistic Multi-Modal Actor Models for Vision-Based Robotic Grasping
Many previous works approach vision-based robotic grasping by training a...
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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...
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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...
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10/17/2017
Multi-task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Learning-based approaches to robotic manipulation are limited by the sca...
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09/22/2017
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Instrumenting and collecting annotated visual grasping datasets to train...
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10/03/2016
