no code implementations • 17 Apr 2024 • Rachel, Chen, Wenjia Zheng, Sandeep Jalui, Pavan Suri, Jun Zeng
With the advancements in 3D printing technologies, it is extremely important that the quality of 3D printed objects, and dimensional accuracies should meet the customer's specifications.
1 code implementation • 17 Apr 2024 • Rachel, Chen, Juheon Lee, Chuang Gan, Zijiang Yang, Mohammad Amin Nabian, Jun Zeng
Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer.
no code implementations • 5 Feb 2024 • Jiahao Liu, Jun Zeng, Fabio Pierazzi, Lorenzo Cavallaro, Zhenkai Liang
Android malware detection serves as the front line against malicious apps.
no code implementations • 3 Feb 2024 • Lu Wang, Li Chang, Ruipeng Zhang, Kexun Li, Yu Wang, Wei Chen, Xuanlin Feng, Mingwei Sun, Qi Wang, Charles Damien Lu, Jun Zeng, Hua Jiang
Excessive energy intake increased mortality rapidly in the early period of the acute phase.
1 code implementation • 28 Feb 2023 • Yifan Zeng, Suiyi He, Han Hoang Nguyen, Yihan Li, Zhongyu Li, Koushil Sreenath, Jun Zeng
This work introduces a novel control strategy called Iterative Linear Quadratic Regulator for Iterative Tasks (i2LQR), which aims to improve closed-loop performance with local trajectory optimization for iterative tasks in a dynamic environment.
1 code implementation • 13 Dec 2022 • Xiaoye Qu, Jun Zeng, Daizong Liu, Zhefeng Wang, Baoxing Huai, Pan Zhou
Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples.
no code implementations • 29 Jun 2022 • Chenyu Yang, Guo Ning Sue, Zhongyu Li, Lizhi Yang, Haotian Shen, Yufeng Chi, Akshara Rai, Jun Zeng, Koushil Sreenath
We develop and demonstrate one of the first collaborative autonomy framework that is able to move a cable-towed load, which is too heavy to move by a single robot, through narrow spaces with real-time feedback and reactive planning in experiments.
no code implementations • 30 May 2022 • Ashish Kumar, Zhongyu Li, Jun Zeng, Deepak Pathak, Koushil Sreenath, Jitendra Malik
In this work, we leverage recent advances in rapid adaptation for locomotion control, and extend them to work on bipedal robots.
no code implementations • 11 May 2022 • Zhongyu Li, Jun Zeng, Akshay Thirugnanam, Koushil Sreenath
Furthermore, we illustrate that the found linear model is able to provide guarantees by safety-critical optimal control framework, e. g., Model Predictive Control with Control Barrier Functions, on an example of autonomous navigation using Cassie while taking advantage of the agility provided by the RL-based controller.
no code implementations • 4 Mar 2022 • Lizhi Yang, Zhongyu Li, Jun Zeng, Koushil Sreenath
We leverage BO to learn the control parameters used in the HZD-based controller.
no code implementations • 13 Sep 2021 • Zhongyu Li, Jun Zeng, Shuxiao Chen, Koushil Sreenath
This demonstrates reliable autonomy to drive the robot to safely avoid obstacles while walking to the goal location in various kinds of height-constrained cluttered environments.
no code implementations • 18 Jul 2021 • Akshay Thirugnanam, Jun Zeng, Koushil Sreenath
A dual optimization problem is introduced to represent the minimum distance between polytopes and the Lagrangian function for the dual form is applied to construct a control barrier function.
no code implementations • 1 Jul 2021 • Scott Gilroy, Derek Lau, Lizhi Yang, Ed Izaguirre, Kristen Biermayer, Anxing Xiao, Mengti Sun, Ayush Agrawal, Jun Zeng, Zhongyu Li, Koushil Sreenath
The resulted jumping mode is utilized in an autonomous navigation pipeline that leverages a search-based global planner and a local planner to enable the robot to reach the goal location by walking.
2 code implementations • 21 May 2021 • Jun Zeng, Zhongyu Li, Koushil Sreenath
In the existing approaches, the feasibility of the optimization and the system safety cannot be enhanced at the same time theoretically.
1 code implementation • 23 Mar 2021 • Suiyi He, Jun Zeng, Bike Zhang, Koushil Sreenath
This paper develops a new control design for guaranteeing a vehicle's safety during lane change maneuvers in a complex traffic environment.
no code implementations • 26 Jan 2021 • Xu-Dong Huang, Xing-Gang Wu, Qing Yu, Xu-Chang Zheng, Jun Zeng
In the paper, we analyze the properties of Gross-Llewellyn Smith (GLS) sum rule by using the $\mathcal{O}(\alpha_s^4)$-order QCD corrections with the help of principle of maximum conformality (PMC).
High Energy Physics - Phenomenology
2 code implementations • 22 Jul 2020 • Jun Zeng, Bike Zhang, Koushil Sreenath
In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control.
3 code implementations • COLING 2020 • Liang Xu, Hai Hu, Xuanwei Zhang, Lu Li, Chenjie Cao, Yudong Li, Yechen Xu, Kai Sun, Dian Yu, Cong Yu, Yin Tian, Qianqian Dong, Weitang Liu, Bo Shi, Yiming Cui, Junyi Li, Jun Zeng, Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou, Shaoweihua Liu, Zhe Zhao, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Kyle Richardson, Zhenzhong Lan
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.