203 citations · 353 across the 13 of their papers we have counts for
13 papers
SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks
Xingyu Lin, John So, Sashwat Mahalingam +2
The existing internet-scale image and video datasets cover a wide range of everyday objects and tasks, bringing the potential of learning policies that generalize in diverse scenar…
Chain-of-Thought Predictive Control
Zhiwei Jia, Vineet Thumuluri, Fangchen Liu +3
We study generalizable policy learning from demonstrations for complex low-level control (e.g., contact-rich object manipulations). We propose a novel hierarchical imitation learni…
The Wisdom of Hindsight Makes Language Models Better Instruction Followers
Tianjun Zhang, Fangchen Liu, Justin Wong +2
Reinforcement learning has seen wide success in finetuning large language models to better align with instructions via human feedback. The so-called algorithm, Reinforcement Learni…
Masked Autoencoding for Scalable and Generalizable Decision Making
Fangchen Liu, Hao Liu, Aditya Grover +1
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 model…
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Younggyo Seo, Kimin Lee, Fangchen Liu +2
Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics. Recently, autoregressive latent vid…
Masked World Models for Visual Control
Younggyo Seo, Danijar Hafner, Hao Liu +4
Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient robot learning from visual observations. Yet the current approaches typically train a si…
Towards More Generalizable One-shot Visual Imitation Learning
Zhao Mandi, Fangchen Liu, Kimin Lee +1
A general-purpose robot should be able to master a wide range of tasks and quickly learn a novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches t…
SAPIEN: A SimulAted Part-based Interactive ENvironment
Fanbo Xiang, Yuzhe Qin, Kaichun Mo +11
Building home assistant robots has long been a pursuit for vision and robotics researchers. To achieve this task, a simulated environment with physically realistic simulation, suff…
State Alignment-based Imitation Learning
Fangchen Liu, Zhan Ling, Tongzhou Mu +1
Consider an imitation learning problem that the imitator and the expert have different dynamics models. Most of the current imitation learning methods fail because they focus on im…
Mapping State Space using Landmarks for Universal Goal Reaching
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 Functio…
Adversarial Defense by Stratified Convolutional Sparse Coding
Bo Sun, Nian-hsuan Tsai, Fangchen Liu +2
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 res…
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Fisher Yu, Haofeng Chen, Xin Wang +5
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. Re…
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning
Xiangyu Kong, Bo Xin, Fangchen Liu +1
Many tasks in artificial intelligence require the collaboration of multiple agents. We exam deep reinforcement learning for multi-agent domains. Recent research efforts often take…