Search Results for author: Mo Chen

Found 27 papers, 11 papers with code

Guaranteed Completion of Complex Tasks via Temporal Logic Trees and Hamilton-Jacobi Reachability

no code implementations12 Apr 2024 Frank J. Jiang, Kaj Munhoz Arfvidsson, Chong He, Mo Chen, Karl H. Johansson

By ensuring a temporal logic tree has no leaking corners, we know the temporal logic tree correctly verifies the existence of control policies that satisfy the specified task.

Sequential Modeling of Complex Marine Navigation: Case Study on a Passenger Vessel (Student Abstract)

1 code implementation20 Mar 2024 Yimeng Fan, Pedram Agand, Mo Chen, Edward J. Park, Allison Kennedy, Chanwoo Bae

The maritime industry's continuous commitment to sustainability has led to a dedicated exploration of methods to reduce vessel fuel consumption.

Decision Making Time Series +1

Fuel Consumption Prediction for a Passenger Ferry using Machine Learning and In-service Data: A Comparative Study

1 code implementation19 Oct 2023 Pedram Agand, Allison Kennedy, Trevor Harris, Chanwoo Bae, Mo Chen, Edward J Park

As the importance of eco-friendly transportation increases, providing an efficient approach for marine vessel operation is essential.

LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning

1 code implementation19 Oct 2023 Pedram Agand, Mohammad Mahdavian, Manolis Savva, Mo Chen

In end-to-end autonomous driving, the utilization of existing sensor fusion techniques and navigational control methods for imitation learning proves inadequate in challenging situations that involve numerous dynamic agents.

Autonomous Driving Imitation Learning +3

Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissions

1 code implementation19 Oct 2023 Pedram Agand, Alexey Iskrov, Mo Chen

Nowadays, transportation networks face the challenge of sub-optimal control policies that can have adverse effects on human health, the environment, and contribute to traffic congestion.

Q-Learning

Task-Oriented Koopman-Based Control with Contrastive Encoder

no code implementations28 Sep 2023 Xubo Lyu, Hanyang Hu, Seth Siriya, Ye Pu, Mo Chen

We present task-oriented Koopman-based control that utilizes end-to-end reinforcement learning and contrastive encoder to simultaneously learn the Koopman latent embedding, operator, and associated linear controller within an iterative loop.

reinforcement-learning

Multi-Agent Reach-Avoid Games: Two Attackers Versus One Defender and Mixed Integer Programming

1 code implementation22 Sep 2023 Hanyang Hu, Minh Bui, Mo Chen

Utilizing this result and previous results for the 1 vs. 1 game, we further propose solving the general multi-agent reach-avoid game by determining the defender assignments that can maximize the number of attackers captured via a Mixed Integer Program (MIP).

Motion Planning

Efficient Domain Coverage for Vehicles with Second-Order Dynamics via Multi-Agent Reinforcement Learning

no code implementations11 Nov 2022 Xinyu Zhao, Razvan C. Fetecau, Mo Chen

Our proposed network architecture includes the incorporation of LSTM and self-attention, which allows the trained policy to adapt to a variable number of agents.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

Online Probabilistic Model Identification using Adaptive Recursive MCMC

1 code implementation23 Oct 2022 Pedram Agand, Mo Chen, Hamid D. Taghirad

We suggest the Adaptive Recursive Markov Chain Monte Carlo (ARMCMC) method, which eliminates the shortcomings of conventional online techniques while computing the entire probability density function of model parameters.

DMODE: Differential Monocular Object Distance Estimation Module without Class Specific Information

no code implementations23 Oct 2022 Pedram Agand, Michael Chang, Mo Chen

However, these cues can be misleading for objects with wide-range variation or adversarial situations, which is a challenging aspect of object-agnostic distance estimation.

Object Position

STPOTR: Simultaneous Human Trajectory and Pose Prediction Using a Non-Autoregressive Transformer for Robot Following Ahead

1 code implementation15 Sep 2022 Mohammad Mahdavian, Payam Nikdel, Mahdi TaherAhmadi, Mo Chen

The proposed architecture divides human motion prediction into two parts: 1) the human trajectory, which is the hip joint 3D position over time and 2) the human pose which is the all other joints 3D positions over time with respect to a fixed hip joint.

Human motion prediction motion prediction +1

DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network

no code implementations13 Sep 2022 Payam Nikdel, Mohammad Mahdavian, Mo Chen

We show that our system outperforms the state-of-the-art in human motion prediction while it can predict diverse multi-motion future trajectories with hip movements

Human motion prediction motion prediction

Towards Inclusive HRI: Using Sim2Real to Address Underrepresentation in Emotion Expression Recognition

no code implementations15 Aug 2022 Saba Akhyani, Mehryar Abbasi Boroujeni, Mo Chen, Angelica Lim

Robots and artificial agents that interact with humans should be able to do so without bias and inequity, but facial perception systems have notoriously been found to work more poorly for certain groups of people than others.

OptimizedDP: An Efficient, User-friendly Library For Optimal Control and Dynamic Programming

1 code implementation12 Apr 2022 Minh Bui, George Giovanis, Mo Chen, Arrvindh Shriraman

This paper introduces OptimizedDP, a high-performance software library that solves time-dependent Hamilton-Jacobi partial differential equation (PDE), computes backward reachable sets with application in robotics, and contains value iterations algorithm implementation for continuous action-state space Markov Decision Process (MDP) while leveraging user-friendliness of Python for different problem specifications without sacrificing efficiency of the core computation.

Asynchronous, Option-Based Multi-Agent Policy Gradient: A Conditional Reasoning Approach

no code implementations29 Mar 2022 Xubo Lyu, Amin Banitalebi-Dehkordi, Mo Chen, Yong Zhang

In complex problems with large state and action spaces, it is advantageous to extend MAPG methods to use higher-level actions, also known as options, to improve the policy search efficiency.

Hierarchical Reinforcement Learning Multi-agent Reinforcement Learning +3

Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture Generation

1 code implementation29 Sep 2021 Payam Jome Yazdian, Mo Chen, Angelica Lim

We propose a vector-quantized variational autoencoder structure as well as training techniques to learn a rigorous representation of gesture sequences.

Clustering Gesture Generation +3

ARMCMC: Online Bayesian Density Estimation of Model Parameters

no code implementations29 Sep 2021 Pedram Agand, Mo Chen, Hamid Taghirad

Our method shows at-least 70\% improvement in parameter point estimation accuracy and approximately 55\% reduction in tracking error of the value of interest compared to recursive least squares and conventional MCMC.

Density Estimation

ARMCMC: ONLINE MODEL PARAMETERS DENSITY ESTIMATION IN BAYESIAN PARADIGM

no code implementations1 Jan 2021 Pedram Agand, Mo Chen, Hamid D. Taghirad

We demonstrate our approach on a challenging benchmark: estimation of parameters in the Hunt-Crossley dynamic model, which models both on/off contact forces applied to soft materials.

Density Estimation

LBGP: Learning Based Goal Planning for Autonomous Following in Front

no code implementations5 Nov 2020 Payam Nikdel, Richard Vaughan, Mo Chen

Our deep RL module implicitly estimates human trajectory and produces short-term navigational goals to guide the robot.

Navigate Reinforcement Learning (RL) +1

MBB: Model-Based Baseline for Global Guidance of Model-Free Reinforcement Learning via Lower-Dimensional Solutions

no code implementations4 Nov 2020 Xubo Lyu, Site Li, Seth Siriya, Ye Pu, Mo Chen

On the other end, "classical methods" such as optimal control generate solutions without collecting data, but assume that an accurate model of the system and environment is known and are mostly limited to problems with low-dimensional (lo-dim) state spaces.

SFU-Store-Nav: A Multimodal Dataset for Indoor Human Navigation

no code implementations28 Oct 2020 Zhitian Zhang, Jimin Rhim, Taher Ahmadi, Kefan Yang, Angelica Lim, Mo Chen

This article describes a dataset collected in a set of experiments that involves human participants and a robot.

Robot Navigation

BaRC: Backward Reachability Curriculum for Robotic Reinforcement Learning

1 code implementation16 Jun 2018 Boris Ivanovic, James Harrison, Apoorva Sharma, Mo Chen, Marco Pavone

Our Backward Reachability Curriculum (BaRC) begins policy training from states that require a small number of actions to accomplish the task, and expands the initial state distribution backwards in a dynamically-consistent manner once the policy optimization algorithm demonstrates sufficient performance.

Continuous Control reinforcement-learning +1

Hamilton-Jacobi Reachability: A Brief Overview and Recent Advances

1 code implementation21 Sep 2017 Somil Bansal, Mo Chen, Sylvia Herbert, Claire J. Tomlin

Hamilton-Jacobi (HJ) reachability analysis is an important formal verification method for guaranteeing performance and safety properties of dynamical systems; it has been applied to many small-scale systems in the past decade.

Systems and Control Dynamical Systems Optimization and Control

FaSTrack: a Modular Framework for Fast and Guaranteed Safe Motion Planning

no code implementations21 Mar 2017 Sylvia L. Herbert, Mo Chen, SooJean Han, Somil Bansal, Jaime F. Fisac, Claire J. Tomlin

We propose a new algorithm FaSTrack: Fast and Safe Tracking for High Dimensional systems.

Robotics

Using Neural Networks to Compute Approximate and Guaranteed Feasible Hamilton-Jacobi-Bellman PDE Solutions

no code implementations10 Nov 2016 Frank Jiang, Glen Chou, Mo Chen, Claire J. Tomlin

To sidestep the curse of dimensionality when computing solutions to Hamilton-Jacobi-Bellman partial differential equations (HJB PDE), we propose an algorithm that leverages a neural network to approximate the value function.

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