Stochastic sequential decision-making systems — such as Markov decision processes and their variants — are increasingly used in areas such as transportation, healthcare, and communication. However, the ability to explain these systems’ outputs to non-technical end users has not kept pace with their widespread adoption. This paper addresses that gap by extending prior work and presenting a unified framework for generating causal explanations of agent behavior in sequential decision-making settings, grounded in the structural causal model (SCM) paradigm. Our framework supports the generation of multiple, semantically distinct explanations for agent actions — capabilities that were previously unattainable. In addition to introducing a novel taxonomy of explanations for MDPs to guide empirical investigation, we develop both exact and approximate causal inference methods within the SCM framework. We analyze their applicability and derive run-time bounds for each. This leads to the proposed algorithm, MeanRESP, which operates flexibly across a spectrum of approximations tailored to external constraints. We further analyze the sample complexity and error rates of approximate MeanRESP, and provide a detailed comparison of its outputs — under varying definitions of responsibility — with popular Shapley-value-based methods. Empirically, we performed a series of experiments to evaluate the practicality and effectiveness of the proposed system, focusing on real-world computational demands and the validity and reliability of metrics for comparing approximate and exact causal methods. Finally, we present two user studies that reveal user preferences for certain types of explanations and demonstrate a strong preference for explanations generated by our framework compared to those from other state-of-the-art systems.

Reinforcement learning algorithms typically necessitate extensive exploration of the state space to find optimal policies. However, in safety-critical applications, the risks associated with such exploration can lead to catastrophic consequences. Existing safe exploration methods attempt to mitigate this by imposing constraints, which often result in overly conservative behaviours and inefficient learning. Heavy penalties for early constraint violations can trap agents in local optima, deterring exploration of risky yet high-reward regions of the state space. To address this, we introduce a method that explicitly learns state-conditioned safety representations. By augmenting the state features with these safety representations, our approach naturally encourages safer exploration without being excessively cautious, resulting in more efficient and safer policy learning in safety-critical scenarios. Empirical evaluations across diverse environments show that our method significantly improves task performance while reducing constraint violations during training, underscoring its effectiveness in balancing exploration with safety.

Many robotic systems require extended deployments in complex, dynamic environments. In such deployments, parts of the environment may change between subsequent robot observations. Most robotic mapping or environment modeling algorithms are incapable of representing dynamic features in a way that enables predicting their future state. Instead, they opt to filter certain state observations, either by removing them or some form of weighted averaging. This paper introduces Perpetua, a method for modeling the dynamics of semi-static features. Perpetua is able to: incorporate prior knowledge about the dynamics of the feature if it exists, track multiple hypotheses, and adapt over time to enable predicting of future feature states. Specifically, we chain together mixtures of "persistence" and "emergence" filters to model the probability that features will disappear or reappear in a formal Bayesian framework. The approach is an efficient, scalable, general, and robust method for estimating the states of features in an environment, both in the present as well as at arbitrary future times. Through experiments on simulated and real-world data, we find that Perpetua yields better accuracy than similar approaches while also being online adaptable and robust to missing observations.

Stochastic sequential decision-making systems — such as Markov decision processes and their variants — are increasingly used in areas such as transportation, healthcare, and communication. However, the ability to explain these systems’ outputs to non-technical end users has not kept pace with their widespread adoption. This paper addresses that gap by extending prior work and presenting a unified framework for generating causal explanations of agent behavior in sequential decision-making settings, grounded in the structural causal model (SCM) paradigm. Our framework supports the generation of multiple, semantically distinct explanations for agent actions — capabilities that were previously unattainable. In addition to introducing a novel taxonomy of explanations for MDPs to guide empirical investigation, we develop both exact and approximate causal inference methods within the SCM framework. We analyze their applicability and derive run-time bounds for each. This leads to the proposed algorithm, MeanRESP, which operates flexibly across a spectrum of approximations tailored to external constraints. We further analyze the sample complexity and error rates of approximate MeanRESP, and provide a detailed comparison of its outputs—under varying definitions of responsibility—with popular Shapley-value-based methods. Empirically, we performed a series of experiments to evaluate the practicality and effectiveness of the proposed system, focusing on real-world computational demands and the validity and reliability of metrics for comparing approximate and exact causal methods. Finally, we present two user studies that reveal user preferences for certain types of explanations and demonstrate a strong preference for explanations generated by our framework compared to those from other state-of-the-art systems.

Ethically compliant autonomous systems (ECAS) are the prevailing approach to building robotic systems that perform sequential decision making subject to ethical theories in fully observable environments. However, in real-world robotics settings, these systems often operate under partial observability because of sensor limitations, environmental conditions, or limited inference due to bounded computational resources. Therefore, this paper proposes a partially observable ECAS (PO-ECAS), bringing this work one step closer to being a practical and useful tool for roboticists. First, we formally introduce the PO-ECAS framework and a MILP-based solution method for approximating an optimal ethically compliant policy. Next, we extend an existing ethical framework for prima facie duties to belief space and offer an ethical framework for virtue ethics inspired by Aristotle’s Doctrine of the Mean. Finally, we demonstrate that our approach is effective in a simulated campus patrol robot domain.

Nearly all state-of-the-art SLAM algorithms are designed to exploit patterns in data from specific sensing modalities, such as time-of-flight and structured light depth sensors, or RGB cameras. This specialization increases localization accuracy in domains where the given modality detects many high-quality features, but comes at the cost of decreasing performance in other, less favorable environments. For robotic systems that may experience a wide variety of sensing conditions, this difficulty in generalization presents a significant challenge. In this paper, we propose running several computationally cheap SLAM front ends in parallel and choosing the most promising feature set online. This problem is similar to the Algorithm Selection Problem (ASP), but has several complicating factors that preclude application of existing methods. We first provide an extension of the ASP formalism that captures the unique challenges in the SLAM setting, and then, based on this formalism, we propose modeling the SLAM ASP as a partially observable Markov decision process (POMDP). Our experiments show that dynamically selecting SLAM front ends, even myopically, improves localization robustness compared to selecting a static front end, and that using a POMDP policy provides even greater improvement.

Robotic systems are often composed of modular algorithms that each perform a specific function within a larger architecture, ranging from state estimation and task planning to trajectory optimization and object recognition. Existing work for specifying these systems as a formal composition of contract algorithms has limited expressiveness compared to the variety of sophisticated architectures that are commonly used in practice. Therefore, in this paper, we (1) propose a novel metareasoning framework for formally composing robotic systems as a contract program with programming constructs for functional, conditional, and looping semantics and (2) introduce a recursive hill climbing algorithm that finds a locally optimal time allocation of a contract program. In our experiments, we demonstrate that our approach outperforms baseline techniques in a simulated pick-and-place robot domain.

Markov decision processes (MDPs) are a common general-purpose model used in robotics for representing sequential decision-making problems. Given the complexity of robotics applications, a popular approach for approximately solving MDPs relies on state aggregation to reduce the size of the state space but at the expense of policy fidelity-offering a trade-off between policy quality and computation time. Naturally, this poses a challenging metareasoning problem: how can an autonomous system dynamically select different state abstractions that optimize this trade-off as it operates online? In this paper, we formalize this metareasoning problem with a notion of time-dependent utility and solve it using deep reinforcement learning. To do this, we develop several general, cheap heuristics that summarize the reward structure and transition topology of the MDP at hand to serve as effective features. Empirically, we demonstrate that our metareasoning approach outperforms several baseline approaches and a strong heuristic approach on a standard benchmark domain.

Opponent modeling is the ability to use prior knowledge and observations in order to predict the behavior of an opponent. This survey presents a comprehensive overview of existing opponent modeling techniques for adversarial domains, many of which must address stochastic, continuous, or concurrent actions, and sparse, partially observable payoff structures. We discuss all the components of opponent modeling systems, including feature extraction, learning algorithms, and strategy abstractions. These discussions lead us to propose a new form of analysis for describing and predicting the evolution of game states over time. We then introduce a new framework that facilitates method comparison, analyze a representative selection of techniques using the proposed framework, and highlight common trends among recently proposed methods. Finally, we list several open problems and discuss future research directions inspired by AI research on opponent modeling and related research in other disciplines.

Enabling autonomous systems to comply with an ethical theory is critical given their accelerating deployment in domains that impact society. While many ethical theories have been studied extensively in moral philosophy, they are still challenging to implement by developers who build autonomous systems. This paper proposes a novel approach for building ethically compliant autonomous systems that optimize completing a task while following an ethical framework. First, we introduce a definition of an ethically compliant autonomous system and its properties. Next, we offer a range of ethical frameworks for divine command theory, prima facie duties, and virtue ethics. Finally, we demonstrate the accuracy and usability of our approach in a set of autonomous driving simulations and a user study of planning and robotics experts.

Ethically compliant autonomous systems (ECAS) are the state-of-the-art for solving sequential decision-making problems under uncertainty while respecting constraints that encode ethical considerations. This paper defines a novel concept in the context of ECAS that is from moral philosophy, the moral community, which leads to a nuanced taxonomy of explicit ethical agents. We then propose new ethical frameworks that extend the applicability of ECAS to domains where a moral community is required. Next, we provide a formal analysis of the proposed ethical frameworks and conduct experiments that illustrate their differences. Finally, we discuss the implications of explicit moral communities that could shape research on standards and guidelines for ethical agents in order to better understand and predict common errors in their design and communicate their capabilities.

Autonomous systems often use approximate planners that exploit state abstractions to solve large MDPs in real-time decision-making problems. However, these planners can eliminate details needed to produce effective behavior in autonomous systems. We therefore propose a novel model, a partially abstract MDP, with a set of abstract states that each compress a set of ground states to condense irrelevant details and a set of ground states that expand from a set of expanded abstract states to retain relevant details. This papers offers (1) a definition of a partially abstract MDP that (2) generalizes its ground MDP and its abstract MDP and exhibits bounded optimality depending on its abstract MDP along with (3) a lazy algorithm for planning and execution in autonomous systems. The result is a scalable approach that computes near-optimal solutions to large problems in minutes rather than hours.

Autonomous mobile robots need maps for effective, safe navigation, and SLAM in general is still an unsolved problem. Nonetheless, certain combinations of environmental characteristics and sensors admit tractable solutions. In particular, detection and tracking of linear features such as line segments (2D) or planar facets (3D) has been proven robust in many man-made environments. However, these types of features produce rank-deficient constraints, which create challenges for graph-based SLAM optimizers. We present techniques for using rank-deficient features and constraints more robustly by analyzing the approximate null-space of the constraints for each node in the factor graph representing the trajectory. We also extend auxiliary methods for correspondence calculations and map update routines, the combination of which yields state-of-the-art performance for a rank-deficient SLAM system. We present results from quantitative experiments comparing memory use, compute load, accuracy, and robustness for several ablation tests on real and simulated data.

The prevailing methodology for integrating decision making and ethics is to modify autonomous systems in an ad hoc way to incorporate moral sensibility. However, these provisional modifications often lead to behavior that jeopardizes the intentions of developers or the values of stakeholders. We propose a novel approach for building moral autonomous systems that optimally completes a task and follows an ethical framework by decoupling ethical compliance from task completion. This paper offers a formal definition of our approach along with its key properties, an example based on prima facie duties, and a demonstration that uses our open source library.

As mobile robot capabilities improve and deployment times increase, tools to analyze the growing volume of data are becoming necessary. Current state-of-the-art logging, playback, and exploration systems are insufficient for practitioners seeking to discover systemic points of failure in robotic systems. This paper presents a suite of algorithms for similarity-based queries of robotic perception data and implements a system for storing 2D LiDAR data from many deployments cheaply and evaluating top-k queries for complete or partial scans efficiently. We generate compressed representations of laser scans via a convolutional variational autoencoder and store them in a database, where a light-weight dense network for distance function approximation is run at query time. Our query evaluator leverages the local continuity of the embedding space to generate evaluation orders that, in expectation, dominate full linear scans of the database. The accuracy, robustness, scalability, and efficiency of our system is tested on real-world data gathered from dozens of deployments and synthetic data generated by corrupting real data. We find our system accurately and efficiently identifies similar scans across a number of episodes where the robot encountered the same location, or similar indoor structures or objects.

Building large-scale, globally consistent maps is a challenging problem, made more difficult in environments with limited access, sparse features, or when using data collected by novice users. For such scenarios, where state-of-the-art mapping algorithms produce globally inconsistent maps, we introduce a systematic approach to incorporating sparse human corrections, which we term Human-in-the-Loop Simultaneous Localization and Mapping (HitL-SLAM). Given an initial factor graph for pose graph SLAM, HitL-SLAM accepts approximate, potentially erroneous, and rank-deficient human input, infers the intended correction via expectation maximization (EM), back-propagates the extracted corrections over the pose graph, and finally jointly optimizes the factor graph including the human inputs as human correction factor terms, to yield globally consistent large-scale maps. We thus contribute an EM formulation for inferring potentially rank-deficient human corrections to mapping, and human correction factor extensions to the factor graphs for pose graph SLAM that result in a principled approach to joint optimization of the pose graph while simultaneously accounting for multiple forms of human correction. We present empirical results showing the effectiveness of HitL-SLAM at generating globally accurate and consistent maps even when given poor initial estimates of the map.

Autonomous vehicles (AVs) require accurate metric and topological location estimates for safe, effective navigation and decision-making. Although many high-definition (HD) roadmaps exist, they are not always accurate since public roads are dynamic, shaped unpredictably by both human activity and nature. Thus, AVs must be able to handle situations in which the topology specified by the map does not agree with reality. We present the Variable Structure Multiple Hidden Markov Model (VSM-HMM) as a framework for localizing in the presence of topological uncertainty, and demonstrate its effectiveness on an AV where lane membership is modeled as a topological localization process. VSM-HMMs use a dynamic set of HMMs to simultaneously reason about location within a set of most likely current topologies and therefore may also be applied to topological structure estimation as well as AV lane estimation. In addition, we present an extension to the Earth Mover's Distance which allows uncertainty to be taken into account when computing the distance between belief distributions on simplices of arbitrary relative sizes.

Autonomous service mobile robots need to consistently, accurately, and robustly localize in human environments despite changes to such environments over time. Episodic non-Markov Localization addresses the challenge of localization in such changing environments by classifying observations as arising from Long-Term, Short-Term, or Dynamic Features. However, in order to do so, EnML relies on an estimate of the Long-Term Vector Map (LTVM) that does not change over time. In this paper, we introduce a recursive algorithm to build and update the LTVM over time by reasoning about visibility constraints of objects observed over multiple robot deployments. We use a signed distance function (SDF) to filter out observations of short-term and dynamic features from multiple deployments of the robot. The remaining long-term observations are used to build a vector map by robust local linear regression. The uncertainty in the resulting LTVM is computed via Monte Carlo resampling the observations arising from long-term features. By combining occupancy-grid based SDF filtering of observations with continuous space regression of the filtered observations, our proposed approach builds, updates, and amends LTVMs over time, reasoning about all observations from all robot deployments in an environment. We present experimental results demonstrating the accuracy, robustness, and compact nature of the extracted LTVMs from several long-term robot datasets.