Search Results for author: Javier Alonso-Mora

Found 15 papers, 2 papers with code

Auto-Encoding Bayesian Inverse Games

no code implementations14 Feb 2024 Xinjie Liu, Lasse Peters, Javier Alonso-Mora, Ufuk Topcu, David Fridovich-Keil

When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling.

Motion Planning

Robust Multi-Modal Density Estimation

no code implementations19 Jan 2024 Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov, Jens Kober

We compared our approach to state-of-the-art methods for density estimation as well as ablations of ROME, showing that it not only outperforms established methods but is also more robust to a variety of distributions.

Density Estimation

Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization

1 code implementation30 Nov 2023 Daniel Jarne Ornia, Giannis Delimpaltadakis, Jens Kober, Javier Alonso-Mora

In Reinforcement Learning (RL), agents have no incentive to exhibit predictable behaviors, and are often pushed (through e. g. policy entropy regularization) to randomize their actions in favor of exploration.

Policy Gradient Methods reinforcement-learning +1

Simultaneous Synthesis and Verification of Neural Control Barrier Functions through Branch-and-Bound Verification-in-the-loop Training

no code implementations17 Nov 2023 Xinyu Wang, Luzia Knoedler, Frederik Baymler Mathiesen, Javier Alonso-Mora

In this work, we leverage bound propagation techniques and the Branch-and-Bound scheme to efficiently verify that a neural network satisfies the conditions to be a CBF over the continuous state space.

Large-scale Online Ridesharing: The Effect of Assignment Optimality on System Performance

no code implementations7 Apr 2023 David Fiedler, Michal Čertický, Javier Alonso-Mora, Michal Pěchouček, Michal Čáp

We found that the system that uses optimal ridesharing assignments subject to the maximum travel delay of 4 minutes reduces the vehicle distance driven by 57 % compared to an MoD system without ridesharing.

Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games

no code implementations4 Apr 2023 Jingqi Li, Chih-Yuan Chiu, Lasse Peters, Fernando Palafox, Mustafa Karabag, Javier Alonso-Mora, Somayeh Sojoudi, Claire Tomlin, David Fridovich-Keil

To accommodate this, we decompose the approximated game into a set of smaller games with few constraints for each sampled scenario, and propose a decentralized, consensus-based ADMM algorithm to efficiently compute a generalized Nash equilibrium (GNE) of the approximated game.

Decision Making

Active Classification of Moving Targets with Learned Control Policies

no code implementations6 Dec 2022 Álvaro Serra-Gómez, Eduardo Montijano, Wendelin Böhmer, Javier Alonso-Mora

In this paper, we consider the problem where a drone has to collect semantic information to classify multiple moving targets.

Classification Reinforcement Learning (RL)

Learning Interaction-aware Guidance Policies for Motion Planning in Dense Traffic Scenarios

1 code implementation9 Jul 2021 Bruno Brito, Achin Agarwal, Javier Alonso-Mora

Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors.

Autonomous Navigation Motion Planning +2

Anticipatory routing methods for an on-demand ridepooling mobility system

no code implementations25 Jun 2021 Andres Fielbaum, Maximilian Kronmuller, Javier Alonso-Mora

Most ridepooling models face this problem through rebalancing methods only, i. e., moving idle vehicles towards areas with high rejections rate, which is done independently from routing and vehicle-to-orders assignments, so that vehicles serving passengers (a large portion of the total fleet) remain unaffected.

Where to go next: Learning a Subgoal Recommendation Policy for Navigation Among Pedestrians

no code implementations25 Feb 2021 Bruno Brito, Michael Everett, Jonathan P. How, Javier Alonso-Mora

Robotic navigation in environments shared with other robots or humans remains challenging because the intentions of the surrounding agents are not directly observable and the environment conditions are continuously changing.

Collision Avoidance Model Predictive Control +1

Learning Interaction-Aware Trajectory Predictions for Decentralized Multi-Robot Motion Planning in Dynamic Environments

no code implementations10 Feb 2021 Hai Zhu, Francisco Martinez Claramunt, Bruno Brito, Javier Alonso-Mora

In this paper, we introduce a novel trajectory prediction model based on recurrent neural networks (RNN) that can learn multi-robot motion behaviors from demonstrated trajectories generated using a centralized sequential planner.

Collision Avoidance Model Predictive Control +2

With Whom to Communicate: Learning Efficient Communication for Multi-Robot Collision Avoidance

no code implementations25 Sep 2020 Álvaro Serra-Gómez, Bruno Brito, Hai Zhu, Jen Jen Chung, Javier Alonso-Mora

Decentralized multi-robot systems typically perform coordinated motion planning by constantly broadcasting their intentions as a means to cope with the lack of a central system coordinating the efforts of all robots.

Collision Avoidance Motion Planning

Sample Efficient Learning of Path Following and Obstacle Avoidance Behavior for Quadrotors

no code implementations28 Jun 2019 Stefan Stevsic, Tobias Naegeli, Javier Alonso-Mora, Otmar Hilliges

This enables an easy to implement learning algorithm that is robust to errors of the model used in the model predictive controller.

Collision Avoidance Imitation Learning

A message-passing algorithm for multi-agent trajectory planning

no code implementations NeurIPS 2013 Jose Bento, Nate Derbinsky, Javier Alonso-Mora, Jonathan Yedidia

We describe a novel approach for computing collision-free \emph{global} trajectories for $p$ agents with specified initial and final configurations, based on an improved version of the alternating direction method of multipliers (ADMM).

Motion Planning Trajectory Planning

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