arXiv research

No-collision maps improve manifold learning for image data.

New algorithm for multi-player bandits with collision-dependent rewards.

New strategy achieves optimal regret without communication or collisions in multi-player bandit.

A new algorithm reduces regret in multi-player bandits without collision info.

A new algorithm RESYNC for defenders against malicious attackers in multi-player bandits.

Rolling systems limit to billiard models with no-slip collisions.

New algorithms tackle adversarial multi-player bandits with forced-collision communication.

The paper addresses the Multiplayer Multi-Armed Bandit (MMAB) problem, where $M$ decision makers or players collaborate to maximize their cumulative reward. When several players select the same arm, a collision occurs and no reward is collected on this arm. Players involved in a collision are informed about this collis…

We study multiplayer stochastic multi-armed bandit problems in which the players cannot communicate and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward. We consider two feedback models: a model in which the players can observe whether a collision has occurred an…

No communication allows optimal instance-dependent regret guarantees in multi-player bandits.

We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore when two (or more) players select the same action this results in a maximal loss. We prove the first $\sqrt{T}$-type regret guarantee for th…

Residual neural networks improve collision prediction in planetary simulations.

Study nonholonomic systems with collisions using variational principles.

Paper analyzes dynamics of nonholonomic systems with collisions using variational techniques.

Model forecasts motor vehicle collision rates with high accuracy.

Machine learning competition predicts spacecraft collision risks.

Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage. We present a decentralized algorithm that achieves the same performance as a centra…

New algorithms estimate and test collision probability with near-optimal sample complexity.

One approach to designing decision making logic for an aircraft collision avoidance system frames the problem as a Markov decision process and optimizes the system using dynamic programming. The resulting collision avoidance strategy can be represented as a numeric table. This methodology has been used in the developme…

Reduces necessary conditions for collision avoidance on curved spaces.

A new metric for uncertainty quantification using class collisions.

We study an elementary problem of the topological robotics: collective motion of a set of $n$ distinct particles which one has to move from an initial configuration to a final configuration, with the requirement that no collisions occur in the process of motion. The ultimate goal is to construct an algorithm which will…

Algorithm reduces regret in multi-player bandits with unknown collision rewards.

An important application of intelligent vehicles is advance detection of dangerous events such as collisions. This problem is framed as a problem of optimal alarm choice given predictive models for vehicle location and motion. Techniques for real-time collision detection are surveyed and grouped into three classes: ran…

Bayesian deep learning predicts satellite collisions.

Study shows how transformers classify symbols without naming them, proving a margin-versus-collision criterion.

The configuration manifold $M$ of a mechanical system consisting of two unconstrained rigid bodies in $\mathbb{R}^n$, $n\geq 1$, is a manifold with boundary (typically with singularities.) A complete description of the system requires boundary conditions that specify how orbits should be continued after collisions. A b…

The Kepler-Heisenberg problem is that of determining the motion of a planet around a sun in the Heisenberg group, thought of as a three-dimensional sub-Riemannian manifold. The sub-Riemannian Hamiltonian provides the kinetic energy, and the gravitational potential is given by the fundamental solution to the sub-Laplaci…

Study motion planning for points avoiding obstacles in a plane.

Centrality, as a geometrical property of the collision, is crucial for the physical interpretation of nucleus-nucleus and proton-nucleus experimental data. However, it cannot be directly accessed in event-by-event data analysis. Common methods for centrality estimation in A-A and p-A collisions usually rely on a single…

Recovering manifold geometry from geodesic intersections.

New algorithm for multi-player bandits without needing lower bounds or scaling inversely.

The space of shapes of a polyhedron with given total angles less than 2πat each of its n vertices has a Kaehler metric, locally isometric to complex hyperbolic space CH^{n-3}. The metric is not complete: collisions between vertices take place a finite distance from a nonsingular point. The metric completion is a comple…

Two non-communicating players minimize regret in a multi-armed bandit game.

Unified approach detects traffic conflicts across various interactions.

Collision avoidance is a critical task in many applications, such as ADAS (advanced driver-assistance systems), industrial automation and robotics. In an industrial automation setting, certain areas should be off limits to an automated vehicle for protection of people and high-valued assets. These areas can be quaranti…

Motion planning for robots of high degrees-of-freedom (DOFs) is an important problem in robotics with sampling-based methods in configuration space C as one popular solution. Recently, machine learning methods have been introduced into sampling-based motion planning methods, which train a classifier to distinguish coll…

This work examines the role of reinforcement learning in reducing the severity of on-road collisions by controlling velocity and steering in situations in which contact is imminent. We construct a model, given camera images as input, that is capable of learning and predicting the dynamics of obstacles, cars and pedestr…

Up to symmetries, the orbits of three equal masses under an inverse cube force with zero angular momentum and constant moment of inertia can be reparametrized as the geodesics of a complete, negatively curved metric on a pair of pants. The ends of the pants represent binary collisions. Here we will examine the visibili…

Stochastic approach improves neural network training for kinetic simulations.

Multipeakons are special solutions to the Camassa-Holm equation described by an integrable geodesic flow on a Riemannian manifold. We present a bi-Hamiltonian formulation of the system explicitly and write down formulae for the associated first integrals. Then we exploit the first integrals and present a novel approach…

Generalizes Landau-Ginzburg mirrors for Frobenius manifolds in Dynkin type A.

This work improves motion planning for quadcopters by learning and reasoning about controller performance.

This work optimizes signal estimation for sparse MRA with collision-free signals.

CriticSMC improves planning efficiency in constrained environments.

CARML uses meta-learning to avoid obstacles in 2D vehicle navigation.

A fast method learns plasma collision kernels from simulations, improving kinetic models.

Develops scalable differentiable physics for complex object interactions.