1 code implementation • 23 Mar 2022 • Abulikemu Abuduweili, Changliu Liu
Deep reinforcement learning has the potential to address various scientific problems.
1 code implementation • 9 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.
no code implementations • 10 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.
no code implementations • 9 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.
no code implementations • 25 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.
no code implementations • 15 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.
1 code implementation • 3 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.
2 code implementations • 31 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).
no code implementations • 16 Aug 2021 • Ruixuan Liu, Changliu Liu
The proposed framework is applied to an artificial 2D dataset, the MNIST dataset, and a human motion dataset.
no code implementations • 14 Aug 2021 • Zhenggang Tang, Kai Yan, Liting Sun, Wei Zhan, Changliu Liu
To efficiently simulate with massive amounts of agents in MPS, we propose Scalable Million-Agent DQN (SMADQN).
no code implementations • 24 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.
1 code implementation • 18 Apr 2021 • WeiYe Zhao, Suqin He, Changliu Liu
Tolerance estimation problems are prevailing in engineering applications.
no code implementations • 25 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).
1 code implementation • 21 Jan 2020 • Rohit Jena, Changliu Liu, Katia Sycara
Behavior cloning and GAIL are two widely used methods for performing imitation learning.
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.
1 code implementation • 11 Sep 2019 • Abulikemu Abuduweili, Siyan Li, Changliu Liu
The effectiveness and flexibility of the proposed method has been validated in experiments.
Robotics
1 code implementation • 5 Aug 2019 • Tianhao Wei, Changliu Liu
In different methods, the energy function is called a potential function, a safety index, or a barrier function.
2 code implementations • 15 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.
1 code implementation • 14 Mar 2019 • Raunak P. Bhattacharyya, Derek J. Phillips, Changliu Liu, Jayesh K. Gupta, Katherine Driggs-Campbell, Mykel J. Kochenderfer
Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers.
1 code implementation • 12 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
1 code implementation • 2 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