Game-theoretic Objective Space Planning

Autonomous Racing awards agents that react to opponents' behaviors with agile maneuvers towards progressing along the track while penalizing both over-aggressive and over-conservative agents. Understanding the intent of other agents is crucial to deploying autonomous systems in adversarial multi-agent environments. Current approaches either oversimplify the discretization of the action space of agents or fail to recognize the long-term effect of actions and become myopic. Our work focuses on addressing these two challenges. First, we propose a novel dimension reduction method that encapsulates diverse agent behaviors while conserving the continuity of agent actions. Second, we formulate the two-agent racing game as a regret minimization problem and provide a solution for tractable counterfactual regret minimization with a regret prediction model. Finally, we validate our findings experimentally on scaled autonomous vehicles. We demonstrate that using the proposed game-theoretic planner using agent characterization with the objective space significantly improves the win rate against different opponents, and the improvement is transferable to unseen opponents in an unseen environment.Comment: Submitted to 2023 IEEE International Conference on Robotics and Automation (ICRA 2023

Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack

The 3rd Japan Automotive AI Challenge was an international online autonomous racing challenge where 164 teams competed in December 2021. This paper outlines the winning strategy to this competition, and the advantages and challenges of using the Autoware.Auto open source autonomous driving platform for multi-agent racing. Our winning approach includes a lane-switching opponent overtaking strategy, a global raceline optimization, and the integration of various tools from Autoware.Auto including a Model-Predictive Controller. We describe the use of perception, planning and control modules for high-speed racing applications and provide experience-based insights on working with Autoware.Auto. While our approach is a rule-based strategy that is suitable for non-interactive opponents, it provides a good reference and benchmark for learning-enabled approaches.Comment: Accepted at Autoware Workshop at IV 202

HOFA: Twitter Bot Detection with Homophily-Oriented Augmentation and Frequency Adaptive Attention

Twitter bot detection has become an increasingly important and challenging task to combat online misinformation, facilitate social content moderation, and safeguard the integrity of social platforms. Though existing graph-based Twitter bot detection methods achieved state-of-the-art performance, they are all based on the homophily assumption, which assumes users with the same label are more likely to be connected, making it easy for Twitter bots to disguise themselves by following a large number of genuine users. To address this issue, we proposed HOFA, a novel graph-based Twitter bot detection framework that combats the heterophilous disguise challenge with a homophily-oriented graph augmentation module (Homo-Aug) and a frequency adaptive attention module (FaAt). Specifically, the Homo-Aug extracts user representations and computes a k-NN graph using an MLP and improves Twitter's homophily by injecting the k-NN graph. For the FaAt, we propose an attention mechanism that adaptively serves as a low-pass filter along a homophilic edge and a high-pass filter along a heterophilic edge, preventing user features from being over-smoothed by their neighborhood. We also introduce a weight guidance loss to guide the frequency adaptive attention module. Our experiments demonstrate that HOFA achieves state-of-the-art performance on three widely-acknowledged Twitter bot detection benchmarks, which significantly outperforms vanilla graph-based bot detection techniques and strong heterophilic baselines. Furthermore, extensive studies confirm the effectiveness of our Homo-Aug and FaAt module, and HOFA's ability to demystify the heterophilous disguise challenge.Comment: 11 pages, 7 figure

Accelerating Online Reinforcement Learning via Supervisory Safety Systems

Deep reinforcement learning (DRL) is a promising method to learn control policies for robots only from demonstration and experience. To cover the whole dynamic behaviour of the robot, the DRL training is an active exploration process typically derived in simulation environments. Although this simulation training is cheap and fast, applying DRL algorithms to real-world settings is difficult. If agents are trained until they perform safely in simulation, transferring them to physical systems is difficult due to the sim-to-real gap caused by the difference between the simulation dynamics and the physical robot. In this paper, we present a method of online training a DRL agent to drive autonomously on a physical vehicle by using a model-based safety supervisor. Our solution uses a supervisory system to check if the action selected by the agent is safe or unsafe and ensure that a safe action is always implemented on the vehicle. With this, we can bypass the sim-to-real problem while training the DRL algorithm safely, quickly, and efficiently. We provide a variety of real-world experiments where we train online a small-scale, physical vehicle to drive autonomously with no prior simulation training. The evaluation results show that our method trains agents with improved sample efficiency while never crashing, and the trained agents demonstrate better driving performance than those trained in simulation.Comment: 7 Pages, 10 Figures, 1 Table. Submitted to 2023 IEEE International Conference on Robotics and Automation (ICRA 2023

Teaching Autonomous Systems Hands-On: Leveraging Modular Small-Scale Hardware in the Robotics Classroom

Although robotics courses are well established in higher education, the courses often focus on theory and sometimes lack the systematic coverage of the techniques involved in developing, deploying, and applying software to real hardware. Additionally, most hardware platforms for robotics teaching are low-level toys aimed at younger students at middle-school levels. To address this gap, an autonomous vehicle hardware platform, called F1TENTH, is developed for teaching autonomous systems hands-on. This article describes the teaching modules and software stack for teaching at various educational levels with the theme of "racing" and competitions that replace exams. The F1TENTH vehicles offer a modular hardware platform and its related software for teaching the fundamentals of autonomous driving algorithms. From basic reactive methods to advanced planning algorithms, the teaching modules enhance students' computational thinking through autonomous driving with the F1TENTH vehicle. The F1TENTH car fills the gap between research platforms and low-end toy cars and offers hands-on experience in learning the topics in autonomous systems. Four universities have adopted the teaching modules for their semester-long undergraduate and graduate courses for multiple years. Student feedback is used to analyze the effectiveness of the F1TENTH platform. More than 80% of the students strongly agree that the hardware platform and modules greatly motivate their learning, and more than 70% of the students strongly agree that the hardware-enhanced their understanding of the subjects. The survey results show that more than 80% of the students strongly agree that the competitions motivate them for the course.Comment: 15 pages, 12 figures, 3 table

A simple and efficient method for extraction of Taq DNA polymerase

Background: Thermostable DNA polymerase (Taq Pol \u399) from Thermus aquaticus has beenwidely used in PCR, which was usually extracted with Pluthero's method. Themethod used ammonium sulfate to precipitate the enzyme, and it saved effort and money but not time. Moreover, we found that 30\u201340% activity of Taq Pol I was lost at the ammonium sulfate precipitation step, and the product contained a small amount of DNA. Results: We provided a novel, simplified and low-costmethod to purify the Taq Pol \u399 after overproduction of the enzyme in Escherichia coli , which used ethanol instead of ammonium sulfate to precipitate the enzyme. The precipitate can be directly dissolved in the storage buffer without dialysis. In addition, DNA and RNA contamination was removed with DNase I and RNase A before precipitation, and the extraction procedure was optimized. Our improvements increase recovery rate and specific activity of the enzyme, and save labor, time, and cost. Conclusions: Our method uses ethanol, DNase I, and RNase A to purify the Taq Pol \u399, and simplifies the operation, and increases the enzyme recovery rate and quality

Distributional effects of vehicle tax in the framework of transportation externalities

Figure S2.The relationship between perivascular CD4 infiltration and 12 months follow-up DLCO (p = 0.134, r = −0.205). (PPT 43 kb

Viral infection of an estuarine Synechococcus influences its co-occurring heterotrophic bacterial community in the culture

Viruses are infectious and abundant in the marine environment. Viral lysis of host cells releases organic matter and nutrients that affect the surrounding microbial community. Synechococcus are important primary producers in the ocean and they are subject to frequent viral infection. In the laboratory, Synechococcus cultures are often associated with bacteria and such a co-existence relationship appears to be important to the growth and stability of Synechococcus. However, we know little about how viral lysis of Synechococcus affects the co-existing bacteria in the culture. This study investigated the influence of viral infection of Synechococcus on co-occurring bacterial community in the culture. We analyzed the community composition, diversity, predicted functions of the bacterial community, and its correlations with fluorescent dissolved organic matter (FDOM) components and nutrients after introducing a cyanophage to the Synechococcus culture. Cyanophage infection altered the bacterial community structure and increased the bacterial diversity and richness. Increased bacterial groups such as Bacteroidetes and Alphaproteobacteria and decreased bacterial groups such as Gammaproteobacteria were observed. Moreover, cyanophage infection reduced bacterial interactions but enhanced correlations between the dominant bacterial taxa and nutrients. Unique FDOM components were observed in the cyanophage-added culture. Fluorescence intensities of FDOM components varied across the cyanophage-infection process. Decreased nitrate and increased ammonium and phosphate in the cyanophage-added culture coupled with the viral progeny production and increased substance transport and metabolism potentials of the bacterial community. Furthermore, increased potentials in methane metabolism and aromatic compound degradation of the bacterial community were observed in the cyanophage-added culture, suggesting that cyanophage infections contribute to the production of methane-related compounds and refractory organic matter in a microcosm like environment. This study has the potential to deepen our understanding of the impact of viral lysis of cyanobacteria on microbial community in the surrounding water