Task Relevant Adversarial Imitation Learning

June 24, 2026
Media Summary: Task-Relevant Adversarial Imitation Learning October 2019 DOI: 10.1109/Humanoids43949.2019.9034991 Conference: 2019 IEEE-RAS 19th International Conference on ... This video presents our work Combating False Negatives in

Task Relevant Adversarial Imitation Learning - Detailed Analysis & Overview

Task-Relevant Adversarial Imitation Learning October 2019 DOI: 10.1109/Humanoids43949.2019.9034991 Conference: 2019 IEEE-RAS 19th International Conference on ... This video presents our work Combating False Negatives in Demo of the CAT and DAugGI networks presented in the paper " Lunar Lander optimal landing (average high reward greater than 250) In the first part of the talk, I will introduce Multi-agent Generative

This shows the experts' (few) trajectories. Conclusion: GAIL is successful in imitating the expert. Comparison between our AEAIL and other baselines.

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Task Relevant Adversarial Imitation Learning
Task-Relevant Adversarial Imitation Learning
CoRL 2020, Spotlight Talk 50: Task-Relevant Adversarial Imitation Learning
Generative Adversarial Imitation Learning with Deep P-Network for Robotic Cloth Manipulation
AugGAIL: Generative Adversarial Imitation Learning for Robotic Manipulation Tasks
Combating False Negatives in Adversarial Imitation Learning
Learning Food-arrangement Policies from Raw Images with Generative Adversarial Imitation Learning
Demo of networks from Adversarial Imitation Learning with Trajectorial Augmentation and Correction
Expert trajectories: Generative Adversarial Imitation Learning (GAIL)
Deep Generative Models for Imitation Learning and Fairness
Expert trajectories: Generative Adversarial Imitation Learning (GAIL)
Auto-Encoding Adversarial Imitation Learning

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Task-Relevant Adversarial Imitation Learning CoRL 2020, Spotlight Talk 50: Task-Relevant Adversarial Imitation Learning Generative Adversarial Imitation Learning with Deep P-Network for Robotic Cloth Manipulation AugGAIL: Generative Adversarial Imitation Learning for Robotic Manipulation Tasks Combating False Negatives in Adversarial Imitation Learning Learning Food-arrangement Policies from Raw Images with Generative Adversarial Imitation Learning Demo of networks from Adversarial Imitation Learning with Trajectorial Augmentation and Correction DRAJA777 - Jadwal FIFA World Cup Terbaru Lengkap AVTUB - Info Jadwal FIFA World Cup Terbaru LUNATOGEL - Jadwal FIFA World Cup Paling Update BUSTOGEL - Jadwal FIFA World Cup Hari Ini Lengkap LINITOTO - Jadwal FIFA World Cup Dan Klasemen PANDORA88 - Jadwal FIFA World Cup Resmi Terupdate ADS508 - Jadwal FIFA World Cup Terupdate Lengkap DEWETOTO - Jadwal FIFA World Cup Resmi Terupdate GACOR668 - Jadwal FIFA World Cup Resmi Terbaru
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Task Relevant Adversarial Imitation Learning

Task Relevant Adversarial Imitation Learning

See the paper for more details: https://arxiv.org/abs/1910.01077.

Task-Relevant Adversarial Imitation Learning

Task-Relevant Adversarial Imitation Learning

Task-Relevant Adversarial Imitation Learning

CoRL 2020, Spotlight Talk 50: Task-Relevant Adversarial Imitation Learning

CoRL 2020, Spotlight Talk 50: Task-Relevant Adversarial Imitation Learning

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Generative Adversarial Imitation Learning with Deep P-Network for Robotic Cloth Manipulation

Generative Adversarial Imitation Learning with Deep P-Network for Robotic Cloth Manipulation

October 2019 DOI: 10.1109/Humanoids43949.2019.9034991 Conference: 2019 IEEE-RAS 19th International Conference on ...

AugGAIL: Generative Adversarial Imitation Learning for Robotic Manipulation Tasks

AugGAIL: Generative Adversarial Imitation Learning for Robotic Manipulation Tasks

AugGAIL: Generative

Combating False Negatives in Adversarial Imitation Learning

Combating False Negatives in Adversarial Imitation Learning

This video presents our work Combating False Negatives in

Learning Food-arrangement Policies from Raw Images with Generative Adversarial Imitation Learning

Learning Food-arrangement Policies from Raw Images with Generative Adversarial Imitation Learning

Specifically, we utilize a Generative

Demo of networks from Adversarial Imitation Learning with Trajectorial Augmentation and Correction

Demo of networks from Adversarial Imitation Learning with Trajectorial Augmentation and Correction

Demo of the CAT and DAugGI networks presented in the paper "

Expert trajectories: Generative Adversarial Imitation Learning (GAIL)

Expert trajectories: Generative Adversarial Imitation Learning (GAIL)

Lunar Lander optimal landing (average high reward greater than 250)

Deep Generative Models for Imitation Learning and Fairness

Deep Generative Models for Imitation Learning and Fairness

In the first part of the talk, I will introduce Multi-agent Generative

Expert trajectories: Generative Adversarial Imitation Learning (GAIL)

Expert trajectories: Generative Adversarial Imitation Learning (GAIL)

This shows the experts' (few) trajectories. Conclusion: GAIL is successful in imitating the expert.

Auto-Encoding Adversarial Imitation Learning

Auto-Encoding Adversarial Imitation Learning

Comparison between our AEAIL and other baselines.

Imitation Learning for Dense Reward Manipulation Tasks

Imitation Learning for Dense Reward Manipulation Tasks

Abstract - In this project, I use