Search Results for author:
Found 16 papers, 6 papers with code
Unraveling the Key of Machine Learning Solutions for Android Malware Detection
Android malware detection serves as the front line against malicious apps.
Optimize Individualized Energy Delivery for Septic Patients Using Predictive Deep Learning Models: A Real World Study
Excessive energy intake increased mortality rapidly in the early period of the acute phase.
i2LQR: Iterative LQR for Iterative Tasks in Dynamic Environments
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.
Distantly-Supervised Named Entity Recognition with Adaptive Teacher Learning and Fine-grained Student Ensemble
Distantly-Supervised Named Entity Recognition (DS-NER) effectively alleviates the data scarcity problem in NER by automatically generating training samples.
Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots
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.
Adapting Rapid Motor Adaptation for Bipedal Robots
In this work, we leverage recent advances in rapid adaptation for locomotion control, and extend them to work on bipedal robots.
Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models
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.
Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter Learning for Bipedal Locomotion Control
We leverage BO to learn the control parameters used in the HZD-based controller.
Autonomous Navigation of Underactuated Bipedal Robots in Height-Constrained Environments
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.
Duality-based Convex Optimization for Real-time Obstacle Avoidance between Polytopes with Control Barrier Functions
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.
Autonomous Navigation for Quadrupedal Robots with Optimized Jumping through Constrained Obstacles
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.
Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions
In the existing approaches, the feasibility of the optimization and the system safety cannot be enhanced at the same time theoretically.
Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change
This paper develops a new control design for guaranteeing a vehicle's safety during lane change maneuvers in a complex traffic environment.
The Gross-Llewellyn Smith sum rule up to ${\cal O}(α_s^4)$-order QCD corrections
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
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
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.
CLUE: A Chinese Language Understanding Evaluation Benchmark
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.
