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Contingency Games for Multi-Agent Interaction

Peters, L. (author), Bajcsy, Andrea (author), Chiu, Chih Yuan (author), Fridovich-Keil, David (author), Laine, Forrest (author), Ferranti, L. (author), Alonso-Mora, J. (author)

Contingency planning, wherein an agent generates a set of possible plans conditioned on the outcome of an uncertain event, is an increasingly popular way for robots to act under uncertainty. In this work we take a game-theoretic perspective on contingency planning, tailored to multi-agent scenarios in which a robot's actions impact the...

journal article 2024

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Adaptive Prescribed Performance Asymptotic Tracking for High-Order Odd-Rational-Power Nonlinear Systems

Lv, Maolong (author), De Schutter, B.H.K. (author), Cao, Jinde (author), Baldi, S. (author)

Practical tracking results have been reported in the literature for high-order odd-rational-power nonlinear dynamics (a chain of integrators whose power is the ratio of odd integers). Asymptotic tracking remains an open problem for such dynamics. This note gives a positive answer to this problem in the framework of prescribed performance...

journal article 2023

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Abstracting the Traffic of Nonlinear Event-Triggered Control Systems

Delimpaltadakis, Giannis (author), Mazo, M. (author)

Scheduling communication traffic in networks of event-triggered control (ETC) systems is challenging, as their sampling times are unknown, hindering application of ETC in networks. In previous work, finite-state abstractions were created, capturing the sampling behavior of linear time-invariant (LTI) ETC systems with quadratic triggering...

journal article 2023

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Promises of Deep Kernel Learning for Control Synthesis

Reed, Robert (author), Laurenti, L. (author), Lahijanian, Morteza (author)

Deep Kernel Learning (DKL) combines the representational power of neural networks with the uncertainty quantification of Gaussian Processes. Hence, it is potentially a promising tool to learn and control complex dynamical systems. In this letter, we develop a scalable abstraction-based framework that enables the use of DKL for control...

journal article 2023

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Data-driven Abstractions for Verification of Linear Systems

Coppola, R. (author), Peruffo, A. (author), Mazo, M. (author)

We introduce a novel approach for the construction of symbolic abstractions - simpler, finite-state models - which mimic the behaviour of a system of interest, and are commonly utilized to verify complex logic specifications. Such abstractions require an exhaustive knowledge of the concrete model, which can be difficult to obtain in real...

journal article 2023

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Learning to Play Trajectory Games Against Opponents with Unknown Objectives

Liu, Xinjie (author), Peters, L. (author), Alonso-Mora, J. (author)

Many autonomous agents, such as intelligent vehicles, are inherently required to interact with one another. Game theory provides a natural mathematical tool for robot motion planning in such interactive settings. However, tractable algorithms for such problems usually rely on a strong assumption, namely that the objectives of all players in...

journal article 2023

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Distributed Dynamic Coordination Control for Offshore Platform Transportation Under Ocean Environmental Disturbances

Du, Zhe (author), Negenborn, R.R. (author), Reppa, V. (author)

Transportation of a large offshore platform from inland waters to the open sea is a hazardous and challenging mission. With the development of the autonomous surface vessel (ASV), the problem of large floating object transportation has a chance to be solved by applying multiple physical-connected autonomous tugboats. This article proposes a...

journal article 2023

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Stable Motion Primitives via Imitation and Contrastive Learning

Pérez-Dattari, Rodrigo (author), Kober, J. (author)

Learning from humans allows nonexperts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In this work, we focus on...

journal article 2023

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A Provably Stable Iterative Learning Controller for Continuum Soft Robots

Pierallini, M. (author), Stella, F. (author), Angelini, Franco (author), Deutschmann, Bastian (author), Hughes, Josie (author), Bicchi, Antonio (author), Garabini, Manolo (author), Della Santina, C. (author)

Fully exploiting soft robots' capabilities requires devising strategies that can accurately control their movements with the limited amount of control sources available. This task is challenging for reasons including the hard-to-model dynamics, the system's underactuation, and the need of using a prominent feedforward control action to...

journal article 2023

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Robotic Skill Mutation in Robot-to-Robot Propagation During a Physically Collaborative Sawing Task

Maessen, Rosa E.S. (author), Prendergast, J.M. (author), Peternel, L. (author)

Skill propagation among robots without human involvement can be crucial in quickly spreading new physical skills to many robots. In this respect, it is a good alternative to pure reinforcement learning, which can be time-consuming, or learning from human demonstration, which requires human involvement. In the latter case, there may not be...

journal article 2023

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Chaos and Order in Event-Triggered Control

de Albuquerque Gleizer, G. (author), Mazo, M. (author)

Event-triggered control (ETC) is claimed to provide significant reductions in sampling frequency when compared to periodic sampling, but little is formally known about its generated traffic. This work shows that ETC can exhibit very complex, even chaotic traffic, especially when the triggering condition is aggressive in reducing...

journal article 2023

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Representer Theorem for Learning Koopman Operators

Khosravi, M. (author)

In this work, we consider the problem of learning the Koopman operator for discrete-time autonomous systems. The learning problem is formulated as a generic constrained regularized empirical loss minimization in the infinite-dimensional space of linear operators. We show that a representer theorem holds for the introduced learning problem...

journal article 2023

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A Literature Review on Train Motion Model Calibration

Cunillera, A. (author), Bešinović, Nikola (author), Lentink, Ramon M. (author), van Oort, N. (author), Goverde, R.M.P. (author)

The dynamics of a moving train are usually described by means of a motion model based on Newton's second law. This model uses as input track geometry data and train characteristics like mass, the parameters that model the running resistance, the maximum tractive effort and power, and the brake rates to be applied. It can reproduce and predict...

journal article 2023

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Safe and Adaptive 3-D Locomotion via Constrained Task-Space Imitation Learning

Ding, J. (author), Lam, Tin Lun (author), Ge, Ligang (author), Pang, Jianxin (author), Huang, Yanlong (author)

Bipedal locomotion has been widely studied in recent years, where passive safety (i.e., a biped rapidly brakes without falling) is deemed to be a pivotal problem. To realize safe 3-D walking, existing works resort to nonlinear optimization techniques based on simplified dynamics models, requiring hand-tuned reference trajectories. In this...

journal article 2023

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Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios

Schumann, J.F. (author), Kober, J. (author), Zgonnikov, A. (author)

Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction models enabling more efficient trajectory planning could make autonomous vehicles more assertive in such interactions. However, the evaluation of such models is...

journal article 2023

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Mean Field Behavior of Collaborative Multiagent Foragers

Jarne Ornia, D. (author), Zufiria, Pedro J. (author), Mazo, M. (author)

Collaborative multiagent robotic systems, where agents coordinate by modifying a shared environment often result in undesired dynamical couplings that complicate the analysis and experiments when solving a specific problem or task. Simultaneously, biologically inspired robotics rely on simplifying agents and increasing their number to obtain...

journal article 2022

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Hierarchical Optimal Maneuver Planning and Trajectory Control at On-Ramps With Multiple Mainstream Lanes

Chen, N. (author), van Arem, B. (author), Wang, M. (author)

Connected Automated Vehicles (CAVs) have the potential to improve traffic operations when they cooperatively maneuver in merging sections. State-of-the-art approaches in cooperative merging either build on heuristics solutions or prohibit mainline CAVs to change lane on multilane highways. This paper proposes a hierarchical cooperative...

journal article 2022

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Neural-adaptive constrained flight control for air ground recovery under terrain obstacles

Su, Zikang (author), Wang, X. (author), Wang, Honglun (author)

This article contrives a neural-adaptive constrained controller of the cable towed air-ground recovery system subject to terrain obstacles, unmeasurable cable tensions, trailing vortex, wind gust, and actuator saturation. In air-ground recovery system modeling, the towed vehicle's nominal 6 DOF affine nonlinear dynamics and the cable system's...

journal article 2022

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Distributed Time-Varying Optimization of Second-Order Multiagent Systems Under Limited Interaction Ranges

Hong, Huifen (author), Baldi, S. (author), Yu, Wenwu (author), Yu, Xinghuo (author)

This article investigates the distributed time-varying optimization problem for second-order multiagent systems (MASs) under limited interaction ranges. The goal is to seek the minimum of the sum of local time-varying cost functions (CFs), where each CF is only available to the corresponding agent. Limited communication range refers to the...

journal article 2022

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Probabilistic Risk Metric for Highway Driving Leveraging Multi-Modal Trajectory Predictions

Wang, X. (author), Alonso-Mora, J. (author), Wang, M. (author)

Road traffic safety has attracted increasing research attention, in particular in the current transition from human-driven vehicles to autonomous vehicles. Surrogate measures of safety are widely used to assess traffic safety but they typically ignore motion uncertainties and are inflexible in dealing with two-dimensional motion. Meanwhile,...

journal article 2022