Search Results for author: Changliu Liu

Found 21 papers, 13 papers with code

An Optical Controlling Environment and Reinforcement Learning Benchmarks

1 code implementation23 Mar 2022 Abulikemu Abuduweili, Changliu Liu

Deep reinforcement learning has the potential to address various scientific problems.

reinforcement-learning

Learning from Physical Human Feedback: An Object-Centric One-Shot Adaptation Method

1 code implementation9 Mar 2022 Alvin Shek, Rui Chen, Changliu Liu

For robots to be effectively deployed in novel environments and tasks, they must be able to understand the feedback expressed by humans during intervention.

Transferable and Adaptable Driving Behavior Prediction

no code implementations10 Feb 2022 Letian Wang, Yeping Hu, Liting Sun, Wei Zhan, Masayoshi Tomizuka, Changliu Liu

By mimicking humans' cognition model and semantic understanding during driving, we propose HATN, a hierarchical framework to generate high-quality, transferable, and adaptable predictions for driving behaviors in multi-agent dense-traffic environments.

Autonomous Vehicles Trajectory Prediction

Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction

no code implementations9 Dec 2021 Letian Wang, Yeping Hu, Changliu Liu

With the feedback of the observed trajectory, the algorithm is applied to neural-network-based models to improve the performance of driving behavior predictions across different human subjects and scenarios.

Autonomous Vehicles

Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning

no code implementations25 Nov 2021 Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Wenchao Sun, Jianyu Chen

Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger.

reinforcement-learning

Joint Synthesis of Safety Certificate and Safe Control Policy using Constrained Reinforcement Learning

no code implementations15 Nov 2021 Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Jianyu Chen

This paper proposes a novel approach that simultaneously synthesizes the energy-function-based safety certificate and learns the safe control policy with CRL.

reinforcement-learning

Safe Control with Neural Network Dynamic Models

1 code implementation3 Oct 2021 Tianhao Wei, Changliu Liu

It has been extensively studied regarding how to derive a safe control law with a control-affine analytical dynamic model.

The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results

2 code implementations31 Aug 2021 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 Autonomous Systems that was collocated with the 33rd International Conference on Computer-Aided Verification (CAV).

Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance

no code implementations16 Aug 2021 Ruixuan Liu, Changliu Liu

The proposed framework is applied to an artificial 2D dataset, the MNIST dataset, and a human motion dataset.

Data Augmentation

Online Verification of Deep Neural Networks under Domain or Weight Shift

no code implementations24 Jun 2021 Tianhao Wei, Changliu Liu

These methods are not ready to be applied to real-world problems with complex and/or dynamically changing specifications and networks.

Provably Safe Tolerance Estimation for Robot Arms via Sum-of-Squares Programming

1 code implementation18 Apr 2021 WeiYe Zhao, Suqin He, Changliu Liu

Tolerance estimation problems are prevailing in engineering applications.

Flexible MPC-based Conflict Resolution Using Online Adaptive ADMM

no code implementations25 Mar 2021 Jerry An, Giulia Giordano, Changliu Liu

We propose a decentralized conflict resolution method for autonomous vehicles based on a novel extension to the Alternating Directions Method of Multipliers (ADMM), called Online Adaptive ADMM (OA-ADMM), and on Model Predictive Control (MPC).

Autonomous Vehicles Motion Planning

Augmenting GAIL with BC for sample efficient imitation learning

1 code implementation21 Jan 2020 Rohit Jena, Changliu Liu, Katia Sycara

Behavior cloning and GAIL are two widely used methods for performing imitation learning.

Imitation Learning

Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy

1 code implementation L4DC 2020 Abulikemu Abuduweili, Changliu Liu

The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance.

Adaptable Human Intention and Trajectory Prediction for Human-Robot Collaboration

1 code implementation11 Sep 2019 Abulikemu Abuduweili, Siyan Li, Changliu Liu

The effectiveness and flexibility of the proposed method has been validated in experiments.

Robotics

Safe Control Algorithms Using Energy Functions: A Unified Framework, Benchmark, and New Directions

1 code implementation5 Aug 2019 Tianhao Wei, Changliu Liu

In different methods, the energy function is called a potential function, a safety index, or a barrier function.

Algorithms for Verifying Deep Neural Networks

2 code implementations15 Mar 2019 Changliu Liu, Tomer Arnon, Christopher Lazarus, Clark Barrett, Mykel J. Kochenderfer

Deep neural networks are widely used for nonlinear function approximation with applications ranging from computer vision to control.

Robot Safe Interaction System for Intelligent Industrial Co-Robots

1 code implementation12 Aug 2018 Changliu Liu, Masayoshi Tomizuka

Human-robot interactions have been recognized to be a key element of future industrial collaborative robots (co-robots).

Robotics Systems and Control

The Convex Feasible Set Algorithm for Real Time Optimization in Motion Planning

1 code implementation2 Sep 2017 Changliu Liu, Chung-Yen Lin, Masayoshi Tomizuka

The idea is to find a convex feasible set for the original problem and iteratively solve a sequence of subproblems using the convex constraints.

Optimization and Control Robotics

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