PROSO Toolbox: a unified protein-constrained genome-scale modelling framework for strain designing and optimization

The genome-scale metabolic model with protein constraint (PC-model) has been increasingly popular for microbial metabolic simulations. We present PROSO Toolbox, a unified and simple-to-use PC-model toolbox that takes any high-quality genome-scale metabolic reconstruction as the input. The toolbox can construct a PC-model automatically, apply various algorithms for computational strain design and simulation, and help unveil metabolism from gene expression data through a state-of-the-art OVERLAY workflow. It also has detailed tutorials and documentation for maximum accessibility to researchers from diverse backgrounds. PROSO Toolbox, tutorials, and documentation are freely available online: https://github.com/QCSB/PROSO-Toolbox.Comment: 4 pages, 1 figur

Characterization of a RS-LiDAR for 3D Perception

High precision 3D LiDARs are still expensive and hard to acquire. This paper presents the characteristics of RS-LiDAR, a model of low-cost LiDAR with sufficient supplies, in comparison with VLP-16. The paper also provides a set of evaluations to analyze the characterizations and performances of LiDARs sensors. This work analyzes multiple properties, such as drift effects, distance effects, color effects and sensor orientation effects, in the context of 3D perception. By comparing with Velodyne LiDAR, we found RS-LiDAR as a cheaper and acquirable substitute of VLP-16 with similar efficiency.Comment: For ICRA201

Reinforcement learning-based approximate optimal control for attitude reorientation under state constraints

This paper addresses the attitude reorientation problems of rigid bodies under multiple state constraints. A novel reinforcement learning (RL)-based approximate optimal control method is proposed to make the trade-off between control cost and performance. The novelty lies in that it guarantees constraint handling abilities on attitude forbidden zones and angular-velocity limits. To achieve this, barrier functions are employed to encode the constraint information into the cost function. Then an RL-based learning strategy is developed to approximate the optimal cost function and control policy. A simplified critic-only neural network (NN) is employed to replace the conventional actor-critic structure once adequate data is collected online. This design guarantees the uniform boundedness of reorientation errors and NN weight estimation errors subject to the satisfaction of a finite excitation condition, which is a relaxation compared with the persistent excitation condition that is typically required for this class of problems. More importantly, all underlying state constraints are strictly obeyed during the online learning process. The effectiveness and advantages of the proposed controller are verified by both numerical simulations and experimental tests based on a comprehensive hardware-in-loop testbed

A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data

The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses the customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, we find that such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendation into language matching problem by taking product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.Comment: 6 pages, 3 figures, 5 tables, accepted in 21st IEEE International Conference on Machine Learning and Application

Potato: A Data-Oriented Programming 3D Simulator for Large-Scale Heterogeneous Swarm Robotics

Large-scale simulation with realistic nonlinear dynamic models is crucial for algorithms development for swarm robotics. However, existing platforms are mainly developed based on Object-Oriented Programming (OOP) and either use simple kinematic models to pursue a large number of simulating nodes or implement realistic dynamic models with limited simulating nodes. In this paper, we develop a simulator based on Data-Oriented Programming (DOP) that utilizes GPU parallel computing to achieve large-scale swarm robotic simulations. Specifically, we use a multi-process approach to simulate heterogeneous agents and leverage PyTorch with GPU to simulate homogeneous agents with a large number. We test our approach using a nonlinear quadrotor model and demonstrate that this DOP approach can maintain almost the same computational speed when quadrotors are less than 5,000. We also provide two examples to present the functionality of the platform.Comment: 4 pages, 5 figures, accepted by ICRA 2023 Workshop on "The Role of Robotics Simulators for Unmanned Aerial Vehicles

Learning-based 6-DOF control for autonomous proximity operations under motion constraints

This paper proposes a reinforcement learning (RL)-based six-degree-of-freedom (6-DOF) control scheme for the final phase proximity operations of spacecraft. The main novelty of the proposed method are from two aspects: 1) the closed-loop performance can be improved in real-time through the RL technique, achieving an online approximate optimal control subject to the full 6-DOF nonlinear dynamics of spacecraft; 2) Nontrivial motion constraints of proximity operations are considered and strictly obeyed during the whole control process. As a stepping stone, the dual-quaternion formalism is employed to characterize the 6-DOF dynamics model and motion constraints. Then, an RL-based control scheme is developed under the dual-quaternion algebraic framework to approximate the optimal control solution subject to a cost function and a Hamilton-Jacobi-Bellman equation. In addition, a specially designed barrier function is embedded in the reward function to avoid motion constraint violations. The Lyapunov-based stability analysis guarantees the ultimate boundedness of state errors and the weight of NN estimation errors. Besides, we also show that a PD-like controller under dual-quaternion formulation can be employed as the initial control policy to trigger the online learning process. The boundedness of it is proved by a special Lyapunov strictification method. Simulation results of prototypical spacecraft missions with proximity operations are provided to illustrate the effectiveness of the proposed method

ADP-based spacecraft attitude control under actuator misalignment and pointing constraints

This paper is devoted to real-time optimal attitude reorientation control of rigid spacecraft control. Particularly, two typical practical problems - actuator misalignment and forbidden pointing constraints are considered. Within the framework of adaptive dynamic programming (ADP), a novel constrained optimal attitude control scheme is proposed. In this design, a special reward function is developed to characterize the environment feedback and deal with the pointing constraints. Notably, a novel argument term is introduced to the reward function for overcoming the inevitable difficulty in actuator misalignment. By virtue of the Lyapunov stability theory, the ultimate boundedness of state error and the optimality of the proposed method can be guaranteed. Finally, the effectiveness and performance of the developed ADP-based controller are evaluated by not only numerical simulations but also experimental tests with a hardware-in-loop platform