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Found 24 papers, 2 papers with code
A Slices Perspective for Incremental Nonparametric Inference in High Dimensional State Spaces
We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces.
Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice
Solving partially observable Markov decision processes (POMDPs) with high dimensional and continuous observations, such as camera images, is required for many real life robotics and planning problems.
No Compromise in Solution Quality: Speeding Up Belief-dependent Continuous POMDPs via Adaptive Multilevel Simplification
Using the general theoretical results, we present three algorithms to accelerate continuous POMDP online planning with belief-dependent rewards.
Measurement Simplification in ρ-POMDP with Performance Guarantees
In both cases we show a significant speed-up in planning with performance guarantees.
Data Association Aware POMDP Planning with Hypothesis Pruning Performance Guarantees
To overcome this challenge, we introduce a pruning-based approach for planning with ambiguous data associations.
Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints
Moreover, using our proposed framework, we contribute an adaptive method to find a maximal feasible return (e. g., information gain) in terms of Value at Risk for the candidate action sequence with substantial acceleration.
Monte Carlo Planning in Hybrid Belief POMDPs
Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables.
involve-MI: Informative Planning with High-Dimensional Non-Parametric Beliefs
Moreover, we apply the dimensionality reduction for cases in which the new states are augmented to the previous, yet again without sacrificing accuracy.
Risk Aware Adaptive Belief-dependent Probabilistically Constrained Continuous POMDP Planning
In addition, with an arbitrary confidence parameter, we did not find any analogs to our approach.
Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints
We present a computationally efficient method to solve the nonmyopic Belief Space Planning problem while reasoning about data association.
D2A-BSP: Distilled Data Association Belief Space Planning with Performance Guarantees Under Budget Constraints
Unresolved data association in ambiguous and perceptually aliased environments leads to multi-modal hypotheses on both the robot's and the environment state.
Adaptive Information Belief Space Planning
Reasoning about uncertainty is vital in many real-life autonomous systems.
Efficient Belief Space Planning in High-Dimensional State Spaces using PIVOT: Predictive Incremental Variable Ordering Tactic
In this work, we examine the problem of online decision making under uncertainty, which we formulate as planning in the belief space.
Simplified Belief-Dependent Reward MCTS Planning with Guaranteed Tree Consistency
In particular, we present Simplified Information-Theoretic Particle Filter Tree (SITH-PFT), a novel variant to the MCTS algorithm that considers information-theoretic rewards but avoids the need to calculate them completely.
Probabilistic Loss and its Online Characterization for Simplified Decision Making Under Uncertainty
On top of this extension, our key contribution is a novel framework to simplify decision making while assessing and controlling online the simplification's impact.
Online POMDP Planning via Simplification
Our key contribution is a novel algorithmic approach, Simplified Information Theoretic Belief Space Planning (SITH-BSP), which aims to speed-up POMDP planning considering belief-dependent rewards, without compromising on the solution's accuracy.
iX-BSP: Incremental Belief Space Planning
We demonstrate how iX-BSP could benefit existing approximations of the general problem, introducing iML-BSP, which re-uses calculations across planning sessions under the common Maximum Likelihood assumption.
Neural Spectrum Alignment: Empirical Study
In contrast, in this paper we empirically explore these properties along the optimization and show that in practical applications the NTK changes in a very dramatic and meaningful way, with its top eigenfunctions aligning toward the target function learned by NN.
Simplified decision making in the belief space using belief sparsification
In this work, we introduce a new and efficient solution approach for the problem of decision making under uncertainty, which can be formulated as decision making in a belief space, over a possibly high-dimensional state space.
Bayesian Incremental Inference Update by Re-using Calculations from Belief Space Planning: A New Paradigm
Inference and decision making under uncertainty are key processes in every autonomous system and numerous robotic problems.
General Purpose Incremental Covariance Update and Efficient Belief Space Planning via Factor-Graph Propagation Action Tree
Our approach, rAMDL-Tree, extends our previous BSP method rAMDL, by exploiting incremental covariance calculation and performing calculation re-use between common parts of non-myopic candidate actions, such that these parts are evaluated only once, in contrast to existing approaches.
General Probabilistic Surface Optimization and Log Density Estimation
In our experiments we demonstrate this technique to be superior over state-of-the-art baselines in density estimation task for multimodal 20D data.
Deep PDF: Probabilistic Surface Optimization and Density Estimation
For example, kernel density estimation (KDE) methods require meticulous parameter search and are extremely slow at querying new points.
Robust Active Perception via Data-association aware Belief Space planning
We develop a belief space planning (BSP) approach that advances the state of the art by incorporating reasoning about data association (DA) within planning, while considering additional sources of uncertainty.
