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Jingwei Xu 0005

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2 papers

1 author row

ICRA Conference 2021 Conference Paper

PyTouch: A Machine Learning Library for Touch Processing

Mike Lambeta
Huazhe Xu
Jingwei Xu 0005
Po-Wei Chou
Shaoxiong Wang
Trevor Darrell
Roberto Calandra

With the increased availability of rich tactile sensors, there is an an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making. In this paper, we present PyTouch – the first machine learning library dedicated to the processing of touch sensing signals. PyTouch, is designed to be modular, easy-to-use and provides state-of-the-art touch processing capabilities as a service with the goal of unifying the tactile sensing community by providing a library for building scalable, proven, and performance-validated modules over which applications and research can be built upon. We evaluate PyTouch on real-world data from several tactile sensors on touch processing tasks such as touch detection, slip and object pose estimations. PyTouch is open-sourced at https://github.com/facebookresearch/pytouch.

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ICML Conference 2020 Conference Paper

Video Prediction via Example Guidance

Jingwei Xu 0005
Huazhe Xu
Bingbing Ni
Xiaokang Yang 0001
Trevor Darrell

In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics. In this work, we propose a simple yet effective framework that can efficiently predict plausible future states, where the key insight is that the potential distribution of a sequence could be approximated with analogous ones in a repertoire of training pool, namely, expert examples. By further incorporating a novel optimization scheme into the training procedure, plausible predictions can be sampled efficiently from distribution constructed from the retrieved examples. Meanwhile, our method could be seamlessly integrated with existing stochastic predictive models; significant enhancement is observed with comprehensive experiments in both quantitative and qualitative aspects. We also demonstrate the generalization ability to predict the motion of unseen class, i. e. , without access to corresponding data during training phase. Project Page: \hyperlink{https: //sites. google. com/view/vpeg-supp/home. }{https: //sites. google. com/view/vpeg-supp/home. }

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