P.P. Su-Jin, Olivier Lebeltel, Christian Laugier · 2004
The kinematic constraints on a car limits the movements that it can follow, and this difficulty also makes planning a path a challenging problem. More precisely, the parallel parking problem has been widely addressed in the literature, but these approaches rely on more traditional methods using control laws, motion planners or artificial intelligence. There have been recently much interest in the robotics domain on using probabilistic approaches. In this paper, we propose an original formulation and resolution of the parking problem using Bayesian Robot Programming.
Cited by 7
Miriana Quiroga, Pablo Garay, Juan Manuel Alonso et al. · arXiv (Cornell University) · 2022
Bayesian additive regression trees (BART) is a non-parametric method to approximate functions. It is a black-box method based on the sum of many trees where priors are used to regularize inference, mainly by restricting trees' learning capacity so that no individual tree is able to explain the data, but rather the sum of trees. We discuss BART in the context of probabilistic programming languages (PPL), i.e., we present BART as a primitive that can be used as a component of a probabilistic model rather than as a standalone model. Specifically, we introduce the Python library PyMC-BART, which works by extending PyMC, a library for probabilistic programming. We showcase a few examples of models that can be built using PyMC-BART, discuss recommendations for the selection of hyperparameters, and finally, we close with limitations of our implementation and future directions for improvement.
Cited by 7Open access
Paul Brunzema, Sebastian Trimpe · arXiv (Cornell University) · 2026
We introduce BayeSQP, a novel algorithm for general black-box optimization that merges the structure of sequential quadratic programming with concepts from Bayesian optimization. BayeSQP employs second-order Gaussian process surrogates for both the objective and constraints to jointly model the function values, gradients, and Hessian from only zero-order information. At each iteration, a local subproblem is constructed using the GP posterior estimates and solved to obtain a search direction. Crucially, the formulation of the subproblem explicitly incorporates uncertainty in both the function and derivative estimates, resulting in a tractable second-order cone program for high probability improvements under model uncertainty. A subsequent one-dimensional line search via constrained Thompson sampling selects the next evaluation point. Empirical results show thatBayeSQP outperforms state-of-the-art methods in specific high-dimensional settings. Our algorithm offers a principled and flexible framework that bridges classical optimization techniques with modern approaches to black-box optimization.
Cited by 0Open access
Carla Koikey, Emmanuel Mazer · 2015
Abstract — Evolve safely in an unchanged environment and possibly following an optimal trajectory is one big challenge presented by situated robotics research field. Col-lision avoidance is a basic security requirement and this paper proposes a solution based on a probabilistic approach called Bayesian Programming. This approach aims to deal with the uncertainty, imprecision and incompleteness of the information handled. Some examples illustrate the process of embodying the programmer preliminary knowledge into a Bayesian program and experimental results of these ex-amples implementation in an electrical vehicle are described and commented. Some videos illustrating these experiments can be found at
Cited by 0
J. Law, Darren J. Wilkinson · arXiv (Cornell University) · 2019
Bayesian inference involves the specification of a statistical model by a statistician or practitioner, with careful thought about what each parameter represents. This results in particularly interpretable models which can be used to explain relationships present in the observed data. Bayesian models are useful when an experiment has only a small number of observations and in applications where transparency of data driven decisions is important. Traditionally, parameter inference in Bayesian statistics has involved constructing bespoke MCMC (Markov chain Monte Carlo) schemes for each newly proposed statistical model. This results in plausible models not being considered since efficient inference schemes are challenging to develop or implement. Probabilistic programming aims to reduce the barrier to performing Bayesian inference by developing a domain specific language (DSL) for model specification which is decoupled from the parameter inference algorithms. This paper introduces functional programming principles which can be used to develop an embedded probabilistic programming language. Model inference can be carried out using any generic inference algorithm. In this paper Hamiltonian Monte Carlo (HMC) is used, an efficient MCMC method requiring the gradient of the un-normalised log-posterior, calculated using automatic differentiation. The concepts are illustrated using the Scala programming language.
Cited by 1Open access
Nicos Angelopoulos, James Cussens · International Journal of Approximate Reasoning · 2016
Cited by 8
Jonas Umlauft, Yunis Fanger, Sandra Hirche · mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) · 2017
Programming by Demonstration allows to transfer skills from human demonstrators to robotic systems by observation and reproduction. One aspect that is often overlooked is that humans show different trajectories over multiple demonstrations for the same task. Observed movements may be more precise in some phases and more diverse in others. It is well-known that the variability of the execution carries important information about the task. Therefore, we propose a Bayesian approach to model uncertainties from training data and to infer them in regions with sparse information. The approach is validated in simulation, where it shows higher precision than existing methods, and a robotic experiment with variance based impedance adaptation.
Cited by 19Open access
Fidel Aznar, Francisco A. Pujol, Mar Pujol López et al. · Lecture notes in computer science · 2009
Cited by 4
Kyle S. Honegger · Galter Health Sciences Library, Northwestern University · 2020
Contains link to a presentation at Biomedical Data Science Day, February 4, 2020.
Cited by 0Open access
Bagaev, Dmitry Vladislavovich; id_orcid 0000-0001-9655-7986 · TU/e Research Portal · 2023
Cited by 0Open access
Julien Diard, Olivier Lebeltel · HAL (Le Centre pour la Communication Scientifique Directe) · 2000
This paper presents a new robotic programming environment based on the probability calculus. We show how reactive behaviours, like obstacle avoidance, contour following, or even light following, can be programmed and learned by a Khepera robot with our system. We further demonstrate that behaviours can be combined either by programmation or learning. A homing behaviour is thus obtained by combining obstacle avoidance and light following.
Cited by 9Open access
Paul Brunzema, Sebastian Trimpe · arXiv (Cornell University) · 2026
We introduce BayeSQP, a novel algorithm for general black-box optimization that merges the structure of sequential quadratic programming with concepts from Bayesian optimization. BayeSQP employs second-order Gaussian process surrogates for both the objective and constraints to jointly model the function values, gradients, and Hessian from only zero-order information. At each iteration, a local subproblem is constructed using the GP posterior estimates and solved to obtain a search direction. Crucially, the formulation of the subproblem explicitly incorporates uncertainty in both the function and derivative estimates, resulting in a tractable second-order cone program for high probability improvements under model uncertainty. A subsequent one-dimensional line search via constrained Thompson sampling selects the next evaluation point. Empirical results show thatBayeSQP outperforms state-of-the-art methods in specific high-dimensional settings. Our algorithm offers a principled and flexible framework that bridges classical optimization techniques with modern approaches to black-box optimization.
Cited by 0Open access
Carla Koike, Cédric Pradalier, Pierre Bessìère et al. · CogPrints (University of Southampton) · 2003
Evolve safely in an unchanged environment\nand possibly following an optimal trajectory is one big\nchallenge presented by situated robotics research field. Collision\navoidance is a basic security requirement and this\npaper proposes a solution based on a probabilistic approach\ncalled Bayesian Programming. This approach aims to deal\nwith the uncertainty, imprecision and incompleteness of the\ninformation handled. Some examples illustrate the process\nof embodying the programmer preliminary knowledge into\na Bayesian program and experimental results of these examples\nimplementation in an electrical vehicle are described\nand commented. Some videos illustrating these experiments\ncan be found at http://www-laplace.imag.fr.
Cited by 3Open access
Swaraj Dash, Younesse Kaddar, Paquet, Hugo et al. · Zenodo (CERN European Organization for Nuclear Research) · 2022
Snapshot of LazyPPL (https://bitbucket.org/lazyppl/lazyppl-src, commit 902e710).
Cited by 0Open access
Jongmin Lee, Youngsoo Jang, Pascal Poupart et al. · 2017
In this paper, we consider the safe learning scenario where we need to restrict the exploratory behavior of a reinforcement learning agent. Specifically, we treat the problem as a form of Bayesian reinforcement learning in an environment that is modeled as a constrained MDP (CMDP) where the cost function penalizes undesirable situations. We propose a model-based Bayesian reinforcement learning (BRL) algorithm for such an environment, eliciting risk-sensitive exploration in a principled way. Our algorithm efficiently solves the constrained BRL problem by approximate linear programming, and generates a finite state controller in an off-line manner. We provide theoretical guarantees and demonstrate empirically that our approach outperforms the state of the art.
Cited by 7Open access
Yijia Zhao, Stephen D. Patek, Peter A. Beling · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2008
We consider decentralized Bayesian search problems that involve a team of multiple autonomous agents searching for targets on a network of search points operating under the following constraints: 1) interagent communication is limited; 2) the agents do not have the opportunity to agree in advance on how to resolve equivalent but incompatible strategies; and 3) each agent lacks the ability to control or predict with certainty the actions of the other agents. We formulate the multiagent search-path-planning problem as a decentralized optimal control problem and introduce approximate dynamic heuristics that can be implemented in a decentralized fashion. After establishing some analytical properties of the heuristics, we present computational results for a search problem involving two agents on a 5 x 5 grid.
Cited by 31
Christophe Coué, Cédric Pradalier, Christian Laugier · HAL (Le Centre pour la Communication Scientifique Directe) · 2003
A prerequisite to the design of future Advanced\nDriver Assistance Systems for cars is a sensing system\nproviding all the information required for high-level driving\nassistance tasks. In particular, target tracking is still\nchallenging in urban trafc situations, because of the large\nnumber of rapidly maneuvering targets. The goal of this\npaper is to present an original way to perform target position\nand velocity, based on the occupancy grid framework. The\nmain interest of this method is to avoid the decision problem\nof classical multi-target tracking algorithms. Obtained\noccupancy grids are combined with danger estimation to\nperform an elementary task of obstacle avoidance with an\nelectric car.
Cited by 1Open access
Tshilidzi Marwala · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Bayesian neural networks trained using Markov chain Monte Carlo (MCMC) and genetic programming in binary space within Metropolis framework is proposed. It is tested and compared to classical MCMC method and is observed to give better results than classical approach.
Cited by 8
Pierre Dangauthier, Pierre Bessìère, Anne Spalanzani · HAL (Le Centre pour la Communication Scientifique Directe) · 2005
Abstract — Domestic and real world robotics requires continuous learning of new skills and behaviors to interact with humans. Auto-supervised learning, a compromise between supervised and completely unsupervised learning, consist in relying on previous knowledge to acquire new skills. We propose here to realize auto-supervised learning by exploiting statistical regularities in the sensorimotor space of a robot. In our context, it corresponds to achieve feature selection in a Bayesian programming framework. We compare several feature selection algorithms and validate them on a real robotic experiment.
Cited by 0Open access
Kazuhiko Yanai, Hitoshi Iba · 2004
We propose estimation of distribution programming (EDP) based on a probability distribution expression using a Bayesian network. EDP is a population-based program search method, in which the population probability distribution is estimated, and individuals are generated based on the results. We focus our attention on the fact that the dependency relationship of nodes of the program (expressed as a tree structure) is explicit, and estimate the probability distribution of the program population using a Bayesian network. We compare EDP with GP (genetic programming) on several benchmark tests, i.e., a max problem and a Boolean function problem. We also discuss the trends of problems that are the forte of EDP.
Cited by 73