no code implementations • 23 Sep 2023 • Anirudh Chari, Rui Chen, Jaskaran Grover, Changliu Liu
Given these results, the main contribution of our framework is its versatility in a wide spectrum of influence objectives and mixed-autonomy configurations.
no code implementations • 21 Sep 2023 • Rui Chen, WeiYe Zhao, Changliu Liu
This paper introduces an approach for synthesizing feasible safety indices to derive safe control laws under state-dependent control spaces.
no code implementations • 5 Sep 2023 • Ruixuan Liu, Yifan Sun, Changliu Liu
LEGO is a well-known platform for prototyping pixelized objects.
no code implementations • 20 Aug 2023 • Xusheng Luo, Shaojun Xu, Ruixuan Liu, Changliu Liu
In order to maximize the potential of LTL specifications, we capitalized on the intrinsic structure of tasks and introduced a hierarchical structure to LTL specifications.
1 code implementation • 21 Jun 2023 • WeiYe Zhao, Rui Chen, Yifan Sun, Tianhao Wei, Changliu Liu
In particular, we introduce the framework of Maximum Markov Decision Process, and prove that the worst-case safety violation is bounded under SCPO.
no code implementations • 20 Jun 2023 • Ruixuan Liu, Rui Chen, Abulikemu Abuduweili, Changliu Liu
Second, it is difficult to ensure interactive safety due to uncertainty in human behaviors.
1 code implementation • 23 May 2023 • WeiYe Zhao, Rui Chen, Yifan Sun, Ruixuan Liu, Tianhao Wei, Changliu Liu
Due to the diversity of algorithms and tasks, it remains difficult to compare existing safe RL algorithms.
1 code implementation • 24 Mar 2023 • Hongyi Chen, Changliu Liu
This study proposes a safe and sample-efficient reinforcement learning (RL) framework to address two major challenges in developing applicable RL algorithms: satisfying safety constraints and efficiently learning with limited samples.
no code implementations • 6 Feb 2023 • WeiYe Zhao, Tairan He, Rui Chen, Tianhao Wei, Changliu Liu
Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges.
no code implementations • 24 Jan 2023 • Tairan He, WeiYe Zhao, Changliu Liu
Results show that the converged policies with intrinsic costs in all environments achieve zero constraint violation and comparable performance with baselines.
no code implementations • 14 Jan 2023 • Christopher Brix, Mark Niklas Müller, Stanley Bak, Taylor T. Johnson, Changliu Liu
This paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP) held in 2020, 2021, and 2022.
2 code implementations • 20 Dec 2022 • Mark Niklas Müller, Christopher Brix, Stanley Bak, Changliu Liu, Taylor T. Johnson
This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV).
1 code implementation • 20 Nov 2022 • Simin Liu, Changliu Liu, John Dolan
We propose new methods to synthesize control barrier function (CBF)-based safe controllers that avoid input saturation, which can cause safety violations.
1 code implementation • 9 Sep 2022 • Peng Yin, Shiqi Zhao, Ivan Cisneros, Abulikemu Abuduweili, Guoquan Huang, Micheal Milford, Changliu Liu, Howie Choset, Sebastian Scherer
A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github. com/MetaSLAM/GPRS.
Loop Closure Detection
Simultaneous Localization and Mapping
no code implementations • 30 Aug 2022 • Peng Yin, Abulikemu Abuduweili, Shiqi Zhao, Changliu Liu, Sebastian Scherer
We present BioSLAM, a lifelong SLAM framework for learning various new appearances incrementally and maintaining accurate place recognition for previously visited areas.
no code implementations • 5 Jun 2022 • Ankur Deka, Changliu Liu, Katia Sycara
In AIL, an artificial adversary's misclassification is used as a reward signal that is optimized by any standard Reinforcement Learning (RL) algorithm.
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, Bo Ying Su, 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.
1 code implementation • 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.
1 code implementation • 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.
3 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
Predicting human intention is critical to facilitating safe and efficient human-robot collaboration (HRC).
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
Although neural networks are widely used, it remains challenging to formally verify the safety and robustness of neural networks in real-world applications.
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).
2 code implementations • 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