42 accepted papers
Online planning in continuous state, action, and observation spaces remains challenging for autonomous systems. While Monte Carlo Tree Search (MCTS) scales effectively via sampling, most continuous (PO)MDP solvers do not exploit gradient-based action optimization. We propose Action-Gradient MCTS (AG
Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon the recently introduced formulation of probabilistic belief-dependent constraints. In our methodology safety can be materi…
Online planning in Markov Decision Processes (MDPs) enables agents to make sequential decisions by simulating future trajectories from the current state, making it well-suited for large-scale or dynamic environments. Sample-based methods such as Sparse Sampling and Monte Carlo Tree Search (MCTS) are
Online planning under uncertainty remains a critical challenge in robotics and autonomous systems. While tree search techniques are commonly employed to construct partial future trajectories within computational constraints, most existing methods discard information from previous planning sessions c…
We introduce an innovative method for incremental nonparametric probabilistic inference in high-dimensional state spaces. Our approach leverages slices from highdimensional surfaces to efficiently approximate posterior distributions of any shape. Unlike many existing graph-based methods, our slices
Multi-robot belief space planning (MR-BSP) is essential for reliable and safe autonomy. While planning, each robot maintains a belief over the state of the environment and reasons how the belief would evolve in the future for different candidate actions. Yet, existing MR-BSP works have a common assu
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. Recent researches suggested machine learned probabilistic models as observation models, but their u…
Autonomous agents that operate in the real world must often deal with partial observability, which is commonly modeled as partially observable Markov decision processes (POMDPs). However, traditional POMDP models rely on the assumption of complete knowledge of the observation source, known as fully
Real-world problems often require reasoning about hybrid beliefs, over both discrete and continuous random variables. Yet, such a setting has hardly been investigated in the context of planning. Moreover, existing online partially observable Markov decision processes (POMDPs) solvers do not support
Autonomous agents operating in real-world scenarios frequently encounter uncertainty and make decisions based on incomplete information. Planning under uncertainty can be mathematically formalized using partially observable Markov decision processes (POMDPs). However, finding an optimal plan for POM…
It is a long-standing objective to ease the computation burden incurred by the decision-making problem under partial observability. Identifying the sensitivity to simplification of various components of the original problem has tremendous ramifications. Yet, algorithms for decision-making under unce…
Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms either cannot reason about uncertainty explicitly, or do so with high computational burden. Here, we focus on making informed decisions efficiently, using reward functions…
Unresolved data association in ambiguous and perceptually aliased environments leads to multi-modal hypotheses on both the robot's and the environment state. To avoid catastrophic results, when operating in such ambiguous environments, it is crucial to reason about data association within Belief Spa…
Semantic simultaneous localization and mapping is a subject of increasing interest in robotics and AI that directly influences the autonomous vehicles industry, the army industries, and more. One of the challenges in this field is to obtain object classification jointly with robot trajectory estimat…
Robust perception is a key required capability in robotics and AI when dealing with scenarios and environments that exhibit some level of ambiguity and perceptual aliasing. In this work we consider such a setting and contribute a framework that enables to update probabilities of externally-defined d
Qualitative approaches to various tasks, ranging from localization and mapping to active planning, are gaining considerable momentum in recent years. These approaches represent the environment through spatial relationships between small sets of landmarks in independent local coordinate systems. An e
Planning under uncertainty is a fundamental problem in robotics. Classical approaches rely on a metrical representation of the world and robot's states to infer the next course of action. While these approaches are considered accurate, they are often susceptible to metric errors and tend to be costl…
In this paper, we consider online planning in par-tially observable domains. Solving the corresponding POMDP problem is a very challenging task, particularly in an online setting. Our key contribution is a novel algorithmic approach, Simplified Information Theoretic Belief Space Planning (SITH-BSP),…
In probabilistic state inference, we seek to estimate the state of an (autonomous) agent from noisy observations. It can be shown that, under certain assumptions, finding the estimate is equivalent to solving a linear least squares problem. Solving such a problem is done by calculating the upper tri
At its core, decision making under uncertainty can be regarded as sorting candidate actions according to a certain objective. While finding the optimal solution directly is computationally expensive, other approaches that produce the same ordering of candidate actions, will result in the same select
We present an approach for multi-robot consistent distributed localization and semantic mapping in an unknown environment, considering scenarios with classification ambiguity, where objects' visual appearance generally varies with viewpoint. Our approach addresses such a setting by maintaining a dis
Autonomous navigation missions require online decision making abilities, in order to choose from a given set of candidate actions an action that will lead to the best outcome. In a partially observable setting, decision making under uncertainty, also known as belief space planning (BSP), involves re…
Simultaneous localization and mapping (SLAM) is essential in numerous robotics applications such as autonomous navigation. Traditional SLAM approaches infer the metric state of the robot along with a metric map of the environment. While existing algorithms exhibit good results, they are still sensit…
We present an approach for localization and semantic mapping in ambiguous scenarios by incrementally maintaining a hybrid belief over continuous states and discrete classification and data association variables. Unlike existing incremental approaches, we explicitly maintain data association componen…
Belief space planning (BSP) is a fundamental problem in robotics. Determining an optimal action quickly grows intractable as it involves calculating the expected accumulated cost (reward), where the expectation accounts for all future measurement realizations. State of the art approaches therefore r…
We propose to incorporate within bundle adjustment (BA) a new type of constraint that uses feature scale information, leveraging the scale invariance property of typical image feature detectors (e.g., SIFT). While feature scales play an important role in image matching, they have not been utilized t
Typical inference approaches that work with high-dimensional visual measurements use hand-engineered image features (e.g, SIFT) that require combinatorial data association, or predict only hidden state mean without considering its uncertainty and multi-modality aspects. We develop a novel approach t…
We propose an algorithm for robust visual classification of an object of interest observed from multiple views using a black-box Bayesian classifier which provides a measure of uncertainty, in the presence of significant ambiguity and classifier noise, and of localization error. The fusion of classi…
State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outputs thus far, they do not provide any indication regarding how reliable the posterio
In this paper we introduce a novel concept, topological belief space planning (BSP), that uses topological properties of the underlying factor graph representation of future posterior beliefs to direct the search for an optimal solution. This concept deviates from state-of-the-art BSP approaches and…
High accuracy navigation in GPS-deprived environments is of prime importance to various robotics applications and has been extensively investigated in the last two decades. Recent approaches have shown that incorporating sensor's calibration states in addition to the 6DOF pose states may cause bette…
We develop a computationally efficient approach for evaluating the information theoretic term within belief space planning (BSP) considering both unfocused and focused problem settings, where uncertainty reduction of the entire system or only of chosen variables is of interest, respectively. State-o
In this paper we introduce a novel approach for efficient decision making under uncertainty and belief space planning, in high dimensional state spaces. While recently developed methods focus on sparsifying the inference process, the sparsification here is done in the context of efficient decision m…
One key assumption of Belief Space Planning (BSP) is that the data association is known perfectly. In this paper, we relax this assumption in the context of non-myopic planning as well as belief being a Gaussian Mixture Model (GMM). Interestingly, explicit reasoning about the data association within…
In this paper we introduce a novel sparsification method for efficient decision making under uncertainty and belief space planning in high dimensional state spaces. By using a sparse version of the state's information matrix, we are able to improve the high computational cost of examination of all c…
Inference and decision making under uncertainty are essential in numerous robotics problems. In recent years, the similarities between inference and control triggered much work, from developing unified computational frameworks to pondering about the duality between the two. In spite of the aforement…
We develop a novel approach for decision making under uncertainty in high-dimensional state spaces, considering both active unfocused and focused inference, where in the latter case reducing the uncertainty of only a subset of variables is of interest. State of the art approaches typically first cal…
In this paper we develop a new approach for decentralized multi-robot belief space planning in high-dimensional state spaces while operating in unknown environments. State of the art approaches often address related problems within a sampling based motion planning paradigm, where robots generate can…
This letter is concerned with decision making under uncertainty in problems involving high dimensional state spaces. Inspired by conservative information fusion techniques, we propose a novel paradigm where decision making is performed over a conservative rather than the original information space.
We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment using indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop clos…
In this paper, we present an information-based approach to select a reduced number of landmarks and poses for a robot to localize itself and simultaneously build an accurate map. We develop an information theoretic algorithm to efficiently reduce the number of landmarks and poses in a SLAM estimate…
In this paper we address the problem of collaborative active state estimation within the framework of multi-robot simultaneous localization and mapping (SLAM). We assume each robot has to autonomously navigate to a pre-specified set of goals in unknown environments and develop an approach that enabl…