A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot

By Ruben Martinez-Cantin, Nando de Freitas, Eric Brochu, José A. Castellanos, Arnaud Doucet. 2009

256 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computational Theory and Mathematics

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Citations in the corpus

Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016, background): “This interactive Bayesian optimization strategy is particulary effective as humans can be very good at comparing examples, but unable to produce an objective function whose optimum is the example of interest.”
Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age (2016, positive): “but tractable solutions for active SLAM include Bayesian Optimization [169] or efficient Gaussian beliefs propagation [195], among others.”
Learning Awareness Models (2018, background): “When stochastic transition and observation models are available, it is possible to use simulation to infer optimal plans for exploring environments (Martinez-Cantin et al., 2009).”
Learning model-free robot control by a Monte Carlo EM algorithm (2009, background): “Reinforcement Learning (RL) is a notable paradigm for robot control with several reported successes in recent years (Tedrake et al., 2005; Abbeel et al., 2007; Kober and Peters, 2009; Riedmiller et al., 2009; Martinez-Cantin et al., 2009).”
Portfolio Allocation for Bayesian Optimization (2010, background): “Examples can be found in robot gait design [26], online path planning [28, 29], intelligent user interfaces for animation [6, 4], algorithm configuration [20], efficient MCMC [34], sensor placement [38, 33], and reinforcement learning [5].”
A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers (2022, methodology): “This premise soon led the community to explore DRL, building on existing methods that approached active SLAM with RL [188] and using neural networks to represent the policies or value functions.”
Learning Exploration Policies for Navigation (2019, background): “Past works have formulated active SLAM as Partially Observable Markov Decision Processes (POMDPs) (Martinez-Cantin et al., 2009), or as choosing actions that reduce uncertainty in the estimated map (Carrillo et al., 2012).”
Guest Editorial Active Learning and Intrinsically Motivated Exploration in Robots: Advances and Challenges (2010, background): “mapping [21], [22], reinforcement learning [23], body schema learning [25], imitation [26], [27], exploration of objects and”
Stable Bayesian optimization (2018, methodology): “Apart from hyperparameter tuning, Bayesian optimization has also been used for optimal sensor placement [5], for gait design [12] and optimal path planning [13], etc.”
Body schema acquisition through active learning (2010, unknown): “2) Choose θ ∗ = arg min θ C ( θ ) .”
Robot task planning and explanation in open and uncertain worlds (2015, methodology): “[29] give a POMDP formulation of active visual mapping, use direct policy search to find a solution, and use Monte Carlo simulation to generate imaginary observations and action outcomes during optimization.”