Search Results for author: Fangchen Liu

Found 16 papers, 7 papers with code

MOKA: Open-Vocabulary Robotic Manipulation through Mark-Based Visual Prompting

no code implementations5 Mar 2024 Fangchen Liu, Kuan Fang, Pieter Abbeel, Sergey Levine

In this paper, we present MOKA (Marking Open-vocabulary Keypoint Affordances), an approach that employs VLMs to solve robotic manipulation tasks specified by free-form language descriptions.

In-Context Learning Question Answering +2

SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks

no code implementations7 Jul 2023 Xingyu Lin, John So, Sashwat Mahalingam, Fangchen Liu, Pieter Abbeel

In this work, we present a focused study of the generalization capabilities of the pre-trained visual representations at the categorical level.

Imitation Learning

Chain-of-Thought Predictive Control

1 code implementation3 Apr 2023 Zhiwei Jia, Fangchen Liu, Vineet Thumuluri, Linghao Chen, Zhiao Huang, Hao Su

We study generalizable policy learning from demonstrations for complex low-level control tasks (e. g., contact-rich object manipulations).

Imitation Learning

The Wisdom of Hindsight Makes Language Models Better Instruction Followers

1 code implementation10 Feb 2023 Tianjun Zhang, Fangchen Liu, Justin Wong, Pieter Abbeel, Joseph E. Gonzalez

In this paper, we consider an alternative approach: converting feedback to instruction by relabeling the original one and training the model for better alignment in a supervised manner.

Decision Making Language Modelling +2

Masked Autoencoding for Scalable and Generalizable Decision Making

1 code implementation23 Nov 2022 Fangchen Liu, Hao liu, Aditya Grover, Pieter Abbeel

We are interested in learning scalable agents for reinforcement learning that can learn from large-scale, diverse sequential data similar to current large vision and language models.

Decision Making Offline RL +2

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 +1

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 Reinforcement Learning (RL) +1

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 +2

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 +1

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.

Attribute

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

1 code implementation 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

3 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 +8

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