Kuan Fang | DeepAI

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∙ 08/24/2023

BridgeData V2: A Dataset for Robot Learning at Scale

We introduce BridgeData V2, a large and diverse dataset of robotic manip...

0 Homer Walke, et al. ∙

∙ 07/18/2023

Multi-Stage Cable Routing through Hierarchical Imitation Learning

We study the problem of learning to perform multi-stage robotic manipula...

0 Jianlan Luo, et al. ∙

∙ 06/30/2023

Goal Representations for Instruction Following: A Semi-Supervised Language Interface to Control

Our goal is for robots to follow natural language instructions like "put...

0 Vivek Myers, et al. ∙

∙ 06/06/2023

Stabilizing Contrastive RL: Techniques for Offline Goal Reaching

In the same way that the computer vision (CV) and natural language proce...

0 Chongyi Zheng, et al. ∙

∙ 11/11/2022

Active Task Randomization: Learning Visuomotor Skills for Sequential Manipulation by Proposing Feasible and Novel Tasks

Solving real-world sequential manipulation tasks requires robots to have...

0 Kuan Fang, et al. ∙

∙ 10/12/2022

Generalization with Lossy Affordances: Leveraging Broad Offline Data for Learning Visuomotor Tasks

The utilization of broad datasets has proven to be crucial for generaliz...

11 Kuan Fang, et al. ∙

∙ 05/17/2022

Planning to Practice: Efficient Online Fine-Tuning by Composing Goals in Latent Space

General-purpose robots require diverse repertoires of behaviors to compl...

8 Kuan Fang, et al. ∙

∙ 06/26/2021

Discovering Generalizable Skills via Automated Generation of Diverse Tasks

The learning efficiency and generalization ability of an intelligent age...

5 Kuan Fang, et al. ∙

∙ 08/10/2020

Beyond Lexical: A Semantic Retrieval Framework for Textual SearchEngine

Search engine has become a fundamental component in various web and mobi...

0 Kuan Fang, et al. ∙

∙ 07/01/2020

Adaptive Procedural Task Generation for Hard-Exploration Problems

We introduce Adaptive Procedural Task Generation (APT-Gen), an approach ...

18 Kuan Fang, et al. ∙

∙ 06/07/2020

SERank: Optimize Sequencewise Learning to Rank Using Squeeze-and-Excitation Network

Learning-to-rank (LTR) is a set of supervised machine learning algorithm...

0 RuiXing Wang, et al. ∙

∙ 10/29/2019

Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation

The fundamental challenge of planning for multi-step manipulation is to ...

10 Kuan Fang, et al. ∙

∙ 10/26/2019

KETO: Learning Keypoint Representations for Tool Manipulation

We aim to develop an algorithm for robots to manipulate novel objects as...

0 Zengyi Qin, et al. ∙

∙ 03/09/2019

Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks

Many robotic applications require the agent to perform long-horizon task...

4 Kuan Fang, et al. ∙

∙ 06/25/2018

Learning Task-Oriented Grasping for Tool Manipulation from Simulated Self-Supervision

Tool manipulation is vital for facilitating robots to complete challengi...

8 Kuan Fang, et al. ∙

∙ 11/07/2017

Recurrent Autoregressive Networks for Online Multi-Object Tracking

The main challenge of online multi-object tracking is to reliably associ...

0 Kuan Fang, et al. ∙

∙ 10/17/2017

Multi-task Domain Adaptation for Deep Learning of Instance Grasping from Simulation

Learning-based approaches to robotic manipulation are limited by the sca...

0 Kuan Fang, et al. ∙

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