Search Results for author: Fangchen Liu

Found 10 papers, 3 papers with code

HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator

no code implementations15 Sep 2022 Younggyo Seo, Kimin Lee, Fangchen Liu, Stephen James, Pieter Abbeel

Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics.

Data Augmentation Video Prediction

Masked World Models for Visual Control

no code implementations28 Jun 2022 Younggyo Seo, Danijar Hafner, Hao liu, Fangchen Liu, Stephen James, Kimin Lee, Pieter Abbeel

Yet the current approaches typically train a single model end-to-end for learning both visual representations and dynamics, making it difficult to accurately model the interaction between robots and small objects.

Model-based Reinforcement Learning Representation Learning

Towards More Generalizable One-shot Visual Imitation Learning

no code implementations26 Oct 2021 Zhao Mandi, Fangchen Liu, Kimin Lee, Pieter Abbeel

We then study the multi-task setting, where multi-task training is followed by (i) one-shot imitation on variations within the training tasks, (ii) one-shot imitation on new tasks, and (iii) fine-tuning on new tasks.

Contrastive Learning Imitation Learning +1

Autoregressive Latent Video Prediction with High-Fidelity Image Generator

no code implementations29 Sep 2021 Younggyo Seo, Kimin Lee, Fangchen Liu, Stephen James, Pieter Abbeel

Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics.

Data Augmentation Video Prediction

SAPIEN: A SimulAted Part-based Interactive ENvironment

1 code implementation CVPR 2020 Fanbo Xiang, Yuzhe Qin, Kaichun Mo, Yikuan Xia, Hao Zhu, Fangchen Liu, Minghua Liu, Hanxiao Jiang, Yifu Yuan, He Wang, Li Yi, Angel X. Chang, Leonidas J. Guibas, Hao Su

To achieve this task, a simulated environment with physically realistic simulation, sufficient articulated objects, and transferability to the real robot is indispensable.

State Alignment-based Imitation Learning

no code implementations ICLR 2020 Fangchen Liu, Zhan Ling, Tongzhou Mu, Hao Su

Consider an imitation learning problem that the imitator and the expert have different dynamics models.

Imitation Learning reinforcement-learning

Mapping State Space using Landmarks for Universal Goal Reaching

1 code implementation NeurIPS 2019 Zhiao Huang, Fangchen Liu, Hao Su

An agent that has well understood the environment should be able to apply its skills for any given goals, leading to the fundamental problem of learning the Universal Value Function Approximator (UVFA).

Adversarial Defense by Stratified Convolutional Sparse Coding

no code implementations CVPR 2019 Bo Sun, Nian-hsuan Tsai, Fangchen Liu, Ronald Yu, Hao Su

We propose an adversarial defense method that achieves state-of-the-art performance among attack-agnostic adversarial defense methods while also maintaining robustness to input resolution, scale of adversarial perturbation, and scale of dataset size.

Adversarial Defense

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

2 code implementations CVPR 2020 Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, Trevor Darrell

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving.

Autonomous Driving Domain Adaptation +7

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