Search Results for author: Kuan Fang

Found 20 papers, 4 papers with code

Multi-Stage Cable Routing through Hierarchical Imitation Learning

no code implementations18 Jul 2023 Jianlan Luo, Charles Xu, Xinyang Geng, Gilbert Feng, Kuan Fang, Liam Tan, Stefan Schaal, Sergey Levine

In such settings, learning individual primitives for each stage that succeed with a high enough rate to perform a complete temporally extended task is impractical: if each stage must be completed successfully and has a non-negligible probability of failure, the likelihood of successful completion of the entire task becomes negligible.

Imitation Learning

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

no code implementations30 Jun 2023 Vivek Myers, Andre He, Kuan Fang, Homer Walke, Philippe Hansen-Estruch, Ching-An Cheng, Mihai Jalobeanu, Andrey Kolobov, Anca Dragan, Sergey Levine

Our method achieves robust performance in the real world by learning an embedding from the labeled data that aligns language not to the goal image, but rather to the desired change between the start and goal images that the instruction corresponds to.

Instruction Following

Stabilizing Contrastive RL: Techniques for Offline Goal Reaching

1 code implementation6 Jun 2023 Chongyi Zheng, Benjamin Eysenbach, Homer Walke, Patrick Yin, Kuan Fang, Ruslan Salakhutdinov, Sergey Levine

In the same way that the computer vision (CV) and natural language processing (NLP) communities have developed self-supervised methods, reinforcement learning (RL) can be cast as a self-supervised problem: learning to reach any goal, without requiring human-specified rewards or labels.

Data Augmentation Reinforcement Learning (RL)

Active Task Randomization: Learning Robust Skills via Unsupervised Generation of Diverse and Feasible Tasks

no code implementations11 Nov 2022 Kuan Fang, Toki Migimatsu, Ajay Mandlekar, Li Fei-Fei, Jeannette Bohg

ATR selects suitable tasks, which consist of an initial environment state and manipulation goal, for learning robust skills by balancing the diversity and feasibility of the tasks.

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

no code implementations12 Oct 2022 Kuan Fang, Patrick Yin, Ashvin Nair, Homer Walke, Gengchen Yan, Sergey Levine

The utilization of broad datasets has proven to be crucial for generalization for a wide range of fields.

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

no code implementations17 May 2022 Kuan Fang, Patrick Yin, Ashvin Nair, Sergey Levine

Our experimental results show that PTP can generate feasible sequences of subgoals that enable the policy to efficiently solve the target tasks.

reinforcement-learning Reinforcement Learning (RL)

Discovering Generalizable Skills via Automated Generation of Diverse Tasks

no code implementations26 Jun 2021 Kuan Fang, Yuke Zhu, Silvio Savarese, Li Fei-Fei

To encourage generalizable skills to emerge, our method trains each skill to specialize in the paired task and maximizes the diversity of the generated tasks.

Hierarchical Reinforcement Learning reinforcement-learning +1

Synergies Between Affordance and Geometry: 6-DoF Grasp Detection via Implicit Representations

1 code implementation4 Apr 2021 Zhenyu Jiang, Yifeng Zhu, Maxwell Svetlik, Kuan Fang, Yuke Zhu

The experimental results in simulation and on the real robot have demonstrated that the use of implicit neural representations and joint learning of grasp affordance and 3D reconstruction have led to state-of-the-art grasping results.

3D Reconstruction Multi-Task Learning

Adaptive Procedural Task Generation for Hard-Exploration Problems

no code implementations ICLR 2021 Kuan Fang, Yuke Zhu, Silvio Savarese, Li Fei-Fei

To enable curriculum learning in the absence of a direct indicator of learning progress, we propose to train the task generator by balancing the agent's performance in the generated tasks and the similarity to the target tasks.

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

1 code implementation7 Jun 2020 RuiXing Wang, Kuan Fang, RiKang Zhou, Zhan Shen, LiWen Fan

Recently, there are a few methods have been proposed which focused on mining information across ranking candidates list for further improvements, such as learning multivariant scoring function or learning contextual embedding.

Learning-To-Rank Question Answering

Dynamics Learning with Cascaded Variational Inference for Multi-Step Manipulation

no code implementations29 Oct 2019 Kuan Fang, Yuke Zhu, Animesh Garg, Silvio Savarese, Li Fei-Fei

The fundamental challenge of planning for multi-step manipulation is to find effective and plausible action sequences that lead to the task goal.

Variational Inference

KETO: Learning Keypoint Representations for Tool Manipulation

no code implementations26 Oct 2019 Zengyi Qin, Kuan Fang, Yuke Zhu, Li Fei-Fei, Silvio Savarese

For this purpose, we present KETO, a framework of learning keypoint representations of tool-based manipulation.


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

no code implementations25 Jun 2018 Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, Silvio Savarese

We perform both simulated and real-world experiments on two tool-based manipulation tasks: sweeping and hammering.

Recurrent Autoregressive Networks for Online Multi-Object Tracking

no code implementations7 Nov 2017 Kuan Fang, Yu Xiang, Xiaocheng Li, Silvio Savarese

The external memory explicitly stores previous inputs of each trajectory in a time window, while the internal memory learns to summarize long-term tracking history and associate detections by processing the external memory.

Multi-Object Tracking Online Multi-Object Tracking

DeLay: Robust Spatial Layout Estimation for Cluttered Indoor Scenes

no code implementations CVPR 2016 Saumitro Dasgupta, Kuan Fang, Kevin Chen, Silvio Savarese

We consider the problem of estimating the spatial layout of an indoor scene from a monocular RGB image, modeled as the projection of a 3D cuboid.

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