159 citations · 492 across the 75 of their papers we have counts for
15 papers · 1 filter
Learn from Human Teams: a Probabilistic Solution to Real-Time Collaborative Robot Handling with Dynamic Gesture Commands
Rui Chen, Alvin Shek, Changliu Liu
We study real-time collaborative robot (cobot) handling, where the cobot maneuvers a workpiece under human commands. This is useful when it is risky for humans to directly handle t…
Hierarchical Adaptable and Transferable Networks (HATN) for Driving Behavior Prediction
Letian Wang, Yeping Hu, Liting Sun +3
When autonomous vehicles still struggle to solve challenging situations during on-road driving, humans have long mastered the essence of driving with efficient transferable and ada…
Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction
Letian Wang, Yeping Hu, Changliu Liu
High fidelity behavior prediction of human drivers is crucial for efficient and safe deployment of autonomous vehicles, which is challenging due to the stochasticity, heterogeneity…
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li +3
In the trial-and-error mechanism of reinforcement learning (RL), a notorious contradiction arises when we expect to learn a safe policy: how to learn a safe policy without enough d…
Safe Control of Arbitrary Nonlinear Systems using Dynamic Extension
Yihang Yao, Tianhao Wei, Changliu Liu
Safe control for control-affine systems has been extensively studied. However, due to the complexity of system dynamics, it is challenging and time-consuming to apply these methods…
Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li +2
Safety is the major consideration in controlling complex dynamical systems using reinforcement learning (RL), where the safety certificate can provide provable safety guarantee. A…
Safe Control with Neural Network Dynamic Models
Tianhao Wei, Changliu Liu
Safety is critical in autonomous robotic systems. A safe control law ensures forward invariance of a safe set (a subset in the state space). It has been extensively studied regardi…
The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results
Stanley Bak, Changliu Liu, Taylor Johnson
This report summarizes the second International Verification of Neural Networks Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for ML-Enabled Aut…
A Microscopic Pandemic Simulator for Pandemic Prediction Using Scalable Million-Agent Reinforcement Learning
Zhenggang Tang, Kai Yan, Liting Sun +2
Microscopic epidemic models are powerful tools for government policy makers to predict and simulate epidemic outbreaks, which can capture the impact of individual behaviors on the…
Tolerance-Guided Policy Learning for Adaptable and Transferrable Delicate Industrial Insertion
Boshen Niu, Chenxi Wang, Changliu Liu
Policy learning for delicate industrial insertion tasks (e.g., PC board assembly) is challenging. This paper considers two major problems: how to learn a diversified policy (instea…
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance
Ruixuan Liu, Changliu Liu
Predicting human intention is critical to facilitating safe and efficient human-robot collaboration (HRC). However, it is challenging to build data-driven models for human intentio…
Online Verification of Deep Neural Networks under Domain Shift or Network Updates
Tianhao Wei, Changliu Liu
Although neural networks are widely used, it remains challenging to formally verify the safety and robustness of neural networks in real-world applications. Existing methods are de…
Provably Safe Tolerance Estimation for Robot Arms via Sum-of-Squares Programming
Weiye Zhao, Suqin He, Changliu Liu
Tolerance estimation problems are prevailing in engineering applications. For example, in modern robotics, it remains challenging to efficiently estimate joint tolerance, \ie the m…
Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM
Jerry An, Giulia Giordano, Changliu Liu
Decentralized conflict resolution for autonomous vehicles is needed in many places where a centralized method is not feasible, e.g., parking lots, rural roads, merge lanes, etc. Ho…
Distributed Motion Coordination Using Convex Feasible Set Based Model Predictive Control
Hongyu Zhou, Changliu Liu
The implementation of optimization-based motion coordination approaches in real world multi-agent systems remains challenging due to their high computational complexity and potenti…