no code implementations • 15 Feb 2024 • Wenliang Liu, Danyang Li, Erfan Aasi, Roberto Tron, Calin Belta

Imitation learning methods have demonstrated considerable success in teaching autonomous systems complex tasks through expert demonstrations.

no code implementations • 12 Oct 2023 • Mehdi Kermanshah, Calin Belta, Roberto Tron

Using the strong duality of linear programs (LPs) and robust optimization, we convert the optimization problem to a linear program that can be efficiently solved offline.

no code implementations • 12 Apr 2023 • Wenliang Liu, Wei Xiao, Calin Belta

In this paper, we consider the problem of learning a neural network controller for a system required to satisfy a Signal Temporal Logic (STL) specification.

no code implementations • 4 Apr 2023 • Max H. Cohen, Makai Mann, Kevin Leahy, Calin Belta

In this paper, we present a framework for online parameter estimation and uncertainty quantification in the context of adaptive safety-critical control.

no code implementations • 7 Mar 2023 • Max Cohen, Calin Belta

We demonstrate that the unification of ISS and ISSf in an adaptive control setting allows for maintaining a single set of parameter estimates for both the CLF and CBF that can be generated by a class of update laws satisfying a few general properties.

no code implementations • 30 Nov 2022 • Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta

In this paper, we propose a learning-based framework to simultaneously learn the communication and distributed control policies for a heterogeneous multi-agent system (MAS) under complex mission requirements from Capability Temporal Logic plus (CaTL+) specifications.

no code implementations • 4 Oct 2022 • Wenliang Liu, Kevin Leahy, Zachary Serlin, Calin Belta

We consider the problem of controlling a heterogeneous multi-agent system required to satisfy temporal logic requirements.

no code implementations • 2 Aug 2022 • Kasra Ghasemi, Sadra Sadraddini, Calin Belta

We take a divide and conquer approach to design controllers for reachability problems given large-scale linear systems with polyhedral constraints on states, controls, and disturbances.

no code implementations • 30 Jun 2022 • Kasra Ghasemi, Sadra Sadraddini, Calin Belta

We develop a novel decentralized control method for a network of perturbed linear systems with dynamical couplings subject to Signal Temporal Logic (STL) specifications.

no code implementations • 8 Mar 2022 • Ningyuan Zhang, Wenliang Liu, Calin Belta

We present a computational framework for synthesis of distributed control strategies for a heterogeneous team of robots in a partially observable environment.

no code implementations • 3 Mar 2022 • Max H. Cohen, Calin Belta

We first introduce the notion of a High Order Robust Adaptive Control Barrier Function (HO-RaCBF) as a means to compute control policies guaranteeing satisfaction of high relative degree safety constraints in the face of parametric model uncertainty.

no code implementations • 28 Jan 2022 • Mingyu Cai, Erfan Aasi, Calin Belta, Cristian-Ioan Vasile

This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) formula.

no code implementations • 28 Dec 2021 • Erfan Aasi, Mingyu Cai, Cristian Ioan Vasile, Calin Belta

In this paper, we introduce a time-incremental learning framework: given a dataset of labeled signal traces with a common time horizon, we propose a method to predict the label of a signal that is received incrementally over time, referred to as prefix signal.

no code implementations • 20 Dec 2021 • Suhail Alsalehi, Erfan Aasi, Ron Weiss, Calin Belta

In addition, given system requirements in the form of SVM-STL specifications, we provide an approach for parameter synthesis to find parameters that maximize the satisfaction of such specifications.

1 code implementation • 1 Oct 2021 • Erfan Aasi, Cristian Ioan Vasile, Mahroo Bahreinian, Calin Belta

Our algorithm leverages an ensemble of Concise Decision Trees (CDTs) to improve the classification performance, where each CDT is a decision tree that is empowered by a set of techniques to generate simpler formulae and improve interpretability.

1 code implementation • 28 Jul 2021 • Bassam Helou, Aditya Dusi, Anne Collin, Noushin Mehdipour, Zhiliang Chen, Cristhian Lizarazo, Calin Belta, Tichakorn Wongpiromsarn, Radboud Duintjer Tebbens, Oscar Beijbom

First, we found that these rules were enough for these models to achieve a high classification accuracy on the dataset.

no code implementations • 24 May 2021 • Erfan Aasi, Cristian Ioan Vasile, Mahroo Bahreinian, Calin Belta

Many autonomous systems, such as robots and self-driving cars, involve real-time decision making in complex environments, and require prediction of future outcomes from limited data.

no code implementations • 16 Apr 2021 • Max H. Cohen, Calin Belta

This paper develops a model-based reinforcement learning (MBRL) framework for learning online the value function of an infinite-horizon optimal control problem while obeying safety constraints expressed as control barrier functions (CBFs).

Model-based Reinforcement Learning
reinforcement-learning
**+2**

no code implementations • 6 Apr 2021 • Suhail Alsalehi, Noushin Mehdipour, Ezio Bartocci, Calin Belta

We propose a framework for solving control synthesis problems for multi-agent networked systems required to satisfy spatio-temporal specifications.

no code implementations • 29 Mar 2021 • Wenliang Liu, Mirai Nishioka, Calin Belta

To capture the history dependency of STL specifications, we use a recurrent neural network (RNN) to implement the control policy.

no code implementations • 29 Mar 2021 • Wei Xiao, Calin Belta, Christos G. Cassandras

We define a HOCBF for a safety requirement on the unmodelled system based on the adaptive dynamics and error states, and reformulate the safety-critical control problem as the above mentioned QP.

no code implementations • 25 Mar 2021 • Erkan Kayacan, Wouter Saeys, Herman Ramon, Calin Belta, Joshua M. Peschel

The experimental results for a time-based trajectory show that the NMHE-NMPC framework with the proposed real-time iteration scheme gives better trajectory tracking performance than the ISL-LMPC framework and the required computation time is feasible for real-time applications.

no code implementations • 18 Jan 2021 • Max Cohen, Calin Belta

In this paper we study the problem of synthesizing optimal control policies for uncertain continuous-time nonlinear systems from syntactically co-safe linear temporal logic (scLTL) formulas.

Model-based Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 14 Jan 2021 • Wei Xiao, Noushin Mehdipour, Anne Collin, Amitai Bin-Nun, Emilio Frazzoli, Radboud Duintjer Tebbens, Calin Belta

We develop optimal control strategies for Autonomous Vehicles (AVs) that are required to meet complex specifications imposed by traffic laws and cultural expectations of reasonable driving behavior.

Autonomous Driving Robotics Systems and Control Systems and Control

no code implementations • 24 Sep 2020 • Wenliang Liu, Noushin Mehdipour, Calin Belta

We propose a framework based on Recurrent Neural Networks (RNNs) to determine an optimal control strategy for a discrete-time system that is required to satisfy specifications given as Signal Temporal Logic (STL) formulae.

no code implementations • 15 Jul 2020 • Junmin Wang, Calin Belta, Samuel A. Isaacson

We compare the behaviors of IFFLs to negative autoregulatory loops, another sign-sensitive response-accelerating network motif, and find that increasing retroactivity in a negative autoregulated circuit can only slow the response.

no code implementations • 29 Oct 2019 • Zachary Serlin, Guang Yang, Brandon Sookraj, Calin Belta, Roberto Tron

The centralized QuickMatch algorithm is compared to other standard matching algorithms, while the Distributed QuickMatch algorithm is compared to the centralized algorithm in terms of preservation of match consistency.

no code implementations • ICLR 2019 • Xiao Li, Yao Ma, Calin Belta

Skills learned through (deep) reinforcement learning often generalizes poorly across tasks and re-training is necessary when presented with a new task.

1 code implementation • 25 Apr 2019 • Iman Haghighi, Noushin Mehdipour, Ezio Bartocci, Calin Belta

We present a framework to synthesize control policies for nonlinear dynamical systems from complex temporal constraints specified in a rich temporal logic called Signal Temporal Logic (STL).

Systems and Control

no code implementations • 23 Mar 2019 • Xiao Li, Calin Belta

We combine temporal logic with control Lyapunov functions to improve exploration.

no code implementations • 17 Sep 2018 • Xiao Li, Yao Ma, Calin Belta

Tasks with complex temporal structures and long horizons pose a challenge for reinforcement learning agents due to the difficulty in specifying the tasks in terms of reward functions as well as large variances in the learning signals.

no code implementations • ICLR 2018 • Xiao Li, Yao Ma, Calin Belta

An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill.

Hierarchical Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 31 Oct 2017 • Xiao Li, Yao Ma, Calin Belta

Skills learned through (deep) reinforcement learning often generalizes poorly across domains and re-training is necessary when presented with a new task.

Hierarchical Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 27 Sep 2017 • Xiao Li, Yao Ma, Calin Belta

In this paper, we explore the use of temporal logic (TL) to specify tasks in reinforcement learning.

no code implementations • 11 Dec 2016 • Xiao Li, Cristian-Ioan Vasile, Calin Belta

We propose Truncated Linear Temporal Logic (TLTL) as specifications language, that is arguably well suited for the robotics applications, together with quantitative semantics, i. e., robustness degree.

no code implementations • 23 Sep 2016 • Derya Aksaray, Austin Jones, Zhaodan Kong, Mac Schwager, Calin Belta

This paper addresses the problem of learning optimal policies for satisfying signal temporal logic (STL) specifications by agents with unknown stochastic dynamics.

Systems and Control

no code implementations • 20 Jun 2016 • Xiao Li, Calin Belta

Reinforcement learning has been applied to many interesting problems such as the famous TD-gammon and the inverted helicopter flight.

Hierarchical Reinforcement Learning
reinforcement-learning
**+1**

2 code implementations • 13 Feb 2016 • Cristian-Ioan Vasile, Derya Aksaray, Calin Belta

This paper introduces time window temporal logic (TWTL), a rich expressivity language for describing various time bounded specifications.

Formal Languages and Automata Theory Logic in Computer Science

no code implementations • 12 Sep 2014 • Ebru Aydin Gol, Ezio Bartocci, Calin Belta

We propose a technique to detect and generate patterns in a network of locally interacting dynamical systems.

no code implementations • 9 Sep 2013 • Austin Jones, Mac Schwager, Calin Belta

We present a new temporal logic called Distribution Temporal Logic (DTL) defined over predicates of belief states and hidden states of partially observable systems.

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