Karol Hausman

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A Geometric Perspective on Self-Supervised Policy Adaptation

One of the most challenging aspects of real-world reinforcement learning...
11/14/2020 ∙ by Cristian Bodnar, et al. ∙ 18 ∙ share

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Confidence-rich grid mapping

Representing the environment is a fundamental task in enabling robots to...
06/29/2020 ∙ by Ali-akbar Agha-mohammadi, et al. ∙ 5 ∙ share

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Modeling Long-horizon Tasks as Sequential Interaction Landscapes

Complex object manipulation tasks often span over long sequences of oper...
06/08/2020 ∙ by Sören Pirk, et al. ∙ 0 ∙ share

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Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

Reinforcement learning provides a general framework for learning robotic...
04/27/2020 ∙ by Archit Sharma, et al. ∙ 0 ∙ share

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Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation

One of the great promises of robot learning systems is that they will be...
04/21/2020 ∙ by Ryan Julian, et al. ∙ 29 ∙ share

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Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

We study reinforcement learning in settings where sampling an action fro...
04/13/2020 ∙ by Ted Xiao, et al. ∙ 17 ∙ share

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Gradient Surgery for Multi-Task Learning

While deep learning and deep reinforcement learning (RL) systems have de...
01/19/2020 ∙ by Tianhe Yu, et al. ∙ 19 ∙ share

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Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

We present relay policy learning, a method for imitation and reinforceme...
10/25/2019 ∙ by Abhishek Gupta, et al. ∙ 14 ∙ share

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Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Meta-reinforcement learning algorithms can enable robots to acquire new ...
10/24/2019 ∙ by Tianhe Yu, et al. ∙ 45 ∙ share

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Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping

The distributional perspective on reinforcement learning (RL) has given ...
10/01/2019 ∙ by Cristian Bodnar, et al. ∙ 1 ∙ share

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Dynamics-Aware Unsupervised Discovery of Skills

Conventionally, model-based reinforcement learning (MBRL) aims to learn ...
07/02/2019 ∙ by Archit Sharma, et al. ∙ 0 ∙ share

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Learning to Interactively Learn and Assist

When deploying autonomous agents in the real world, we need to think abo...
06/24/2019 ∙ by Mark Woodward, et al. ∙ 0 ∙ share

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Training an Interactive Helper

Developing agents that can quickly adapt their behavior to new tasks rem...
06/24/2019 ∙ by Mark Woodward, et al. ∙ 1 ∙ share

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Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations

Simulation-to-real transfer is an important strategy for making reinforc...
10/04/2018 ∙ by Zhanpeng He, et al. ∙ 8 ∙ share

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Scaling simulation-to-real transfer by learning composable robot skills

We present a novel solution to the problem of simulation-to-real transfe...
09/26/2018 ∙ by Ryan Julian, et al. ∙ 10 ∙ share

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Region Growing Curriculum Generation for Reinforcement Learning

Learning a policy capable of moving an agent between any two states in t...
07/04/2018 ∙ by Artem Molchanov, et al. ∙ 0 ∙ share

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Karol Hausman

Featured Co-authors

Sergey Levine

Chelsea Finn

Abhishek Gupta

Stan Birchfield

Gaurav S. Sukhatme

Alexander Toshev

Ali-akbar Agha-mohammadi

Shixiang Gu

Stefan Schaal

Vikash Kumar

Julian Ibarz

A Geometric Perspective on Self-Supervised Policy Adaptation

Confidence-rich grid mapping

Modeling Long-horizon Tasks as Sequential Interaction Landscapes

Emergent Real-World Robotic Skills via Unsupervised Off-Policy Reinforcement Learning

Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

Gradient Surgery for Multi-Task Learning

Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learning

Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping

Dynamics-Aware Unsupervised Discovery of Skills

Learning to Interactively Learn and Assist

Training an Interactive Helper

Zero-Shot Skill Composition and Simulation-to-Real Transfer by Learning Task Representations

Scaling simulation-to-real transfer by learning composable robot skills

Region Growing Curriculum Generation for Reinforcement Learning