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06/20/2023
RoboCat: A Self-Improving Foundation Agent for Robotic Manipulation
The ability to leverage heterogeneous robotic experience from different ...
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05/06/2022
How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation
Reinforcement learning (RL) has been shown to be effective at learning c...
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10/30/2021
Learning Coordinated Terrain-Adaptive Locomotion by Imitating a Centroidal Dynamics Planner
Dynamic quadruped locomotion over challenging terrains with precise foot...
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10/12/2021
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes
We study the problem of robotic stacking with objects of complex geometr...
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06/15/2021
On Multi-objective Policy Optimization as a Tool for Reinforcement Learning
Many advances that have improved the robustness and efficiency of deep r...
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03/16/2021
Manipulator-Independent Representations for Visual Imitation
Imitation learning is an effective tool for robotic learning tasks where...
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10/21/2019
Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation
Collecting and automatically obtaining reward signals from real robotic ...
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06/04/2019
Off-Policy Evaluation via Off-Policy Classification
In this work, we consider the problem of model selection for deep reinfo...
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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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10/26/2018
Unsupervised Multi-Target Domain Adaptation: An Information Theoretic Approach
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation...
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11/14/2017
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Style transfer usually refers to the task of applying color and texture ...
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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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12/16/2016
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Collecting well-annotated image datasets to train modern machine learnin...
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08/22/2016
Domain Separation Networks
The cost of large scale data collection and annotation often makes the a...
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09/10/2015