Pinxin Long
pinxinlong at gmail dot com

I am working on localization, decision making and motion planning for robotics and autonomous driving. Before digging into self-driving cars, I was a research scientist at Dorabot Inc., where I worked on multi-robot systems. Before that, I spent half a year at the City University of Hong Kong (CityU) , supervised by Prof. Jia Pan on multi-agent collision avoidance. And ever since then, I've been collaborating with Prof. Jia Pan on machine learning for robotic perception, planning and control.

Prior to CityU, I worked as a research assistant in Visual Computing Center (VCC) at Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), where I involved in several research projects, including two exciting robotic auto-scanning projects with the PR2 robot.

Google Scholar


My current interests lie in the intersection of autonomous driving, robotics, reinforcement learning and deep learning. In particular, I'm interested in designing machine learning algorithms to learn a driving policy that can enable robotic cars driving safely, reliably and efficiently in complex environments.


Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning
Pinxin Long*, Tingxiang Fan*, Xinyi Liao, Wenxi Liu, Hao Zhang, Jia Pan
International Conference on Robotics and Automation (ICRA), 2018.
project / video (youtube), video (bilibili) / arXiv

As a first step toward reducing the performance gap between decentralized and centralized multi-robot collsion avoidance, we present a multi-scenario multi-stage training framework to find an optimal policy which is trained over a large number of robots on rich, complex environments simultaneously using a policy gradient based reinforcement algorithm.

Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation
Pinxin Long, Wenxi Liu, Jia Pan
IEEE Robotics and Automation Letters (RAL), 2017
project (video) / arXiv

This paper is our first step toward learning a reactive collision avoidance policy for multi-agent collision avoidance. By carefully designing the data collection process and leveraging an end-to-end learning framework, our method can learn a deep neural network based collision avoidance policy which demonstrates an advantage over the state-of-theart ORCA policy in terms of ease of use, success rate, and navigation performance.

DoraPicker: An Autonomous Picking System for General Objects
Hao Zhang, Pinxin Long, Dandan Zhou, Zhongfeng Qian, Zheng Wang, Weiwei Wan, Dinesh Manocha, Chonhyon Park, Tommy Hu, Chao Cao, Yibo Chen, Marco Chow, Jia Pan
International Conference on Automation Science and Engineering (CASE), 2016
video / arXiv

We present our pick-and-place system in detail while highlighting our design principles for the warehouse settings, including the perception method that leverages knowledge about its workspace, three grippers designed to handle a large variety of different objects in terms of shape, weight and material, and grasp planning in cluttered scenarios.

Data-Driven Contextual Modeling for 3D Scene Understanding
Yifei Shi, Pinxin Long, Kai Xu, Hui Huang, Yueshan Xiong
Computer & Graphics (C&G), 2016

We propose a data-driven approach to modeling contextual information covering both intra-object part relations and inter-object object layouts. Our method combines the detection of individual objects and object groups within the same framework, enabling contextual analysis without knowing the objects in the scene a priori.

Full 3D Plant Reconstruction via Intrusive Acquisition
Kangxue Yin, Hui Huang, Pinxin Long, Alexei Gaissinski, Minglun Gong, Andrei Sharf Computer Graphics Forum (CGF), 2016
project / data

We present an intrusive acquisition approach for acquiring and modeling of plants and foliage, which disassembles the plant into disjoint parts that can be accurately scanned and reconstructed offline.

Autoscanning for Coupled Scene Reconstruction and Proactive Object Analysis
Kai Xu, Hui Huang, Yifei Shi, Hao Li, Pinxin Long, Jianong Caichen, Wei Sun, Baoquan Chen
ACM Transactions on Graphics (SIGGRAPH Asia 2015), 2015
project / slides / video (youtube), video (youku) / code

We propose autonomous scene scanning by a robot to relieve humans from such a tedious task. The presented algorithm interleaves between scene analysis for extracting objects and robot conducted validation for improving the segmentation and object-aware reconstruction.

Quality-driven Poisson-guided Autoscanning
Shihao Wu, Wei Sun, Pinxin Long, Hui Huang, Daniel Cohen-Or, Minglun Gong, Oliver Deussen, Baoquan Chen
ACM Transactions on Graphics (SIGGRAPH Asia 2014), 2014
project / slides / video (youtube), video (youku) / live show / code

We propose a quality-driven, Poisson-guided autonomous scanning method to ensure the high quality scanning of the model. This goal is achieved by placing the scanner at strategically selected Next-Best-Views (NBVs) to ensure progressively capturing the geometric details of the object, until both completeness and high fidelity are reached.

This nice webpage is "stolen" from here.