Author Archives: Juan-antonio Fernández-madrigal

Classical task planning at an abstract level for achieving good low level motion planning under uncertainty

July 11, 2023 09:00 ,

Antony Thomas, Fulvio Mastrogiovanni, Marco Baglietto, MPTP: Motion-planning-aware task planning for navigation in belief space, . Robotics and Autonomous Systems, Volume 141, 2021 DOI: 10.1016/j.robot.2021.103786.

We present an integrated Task-Motion Planning (TMP) framework for navigation in large-scale environments. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In this paper, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as belief space planning. The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our approach by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work.

Posted in: Robot motion planning, Robot task planning , Tagged:

Physiological bases of navigation

July 11, 2023 08:54 ,

Eva Zita Patai, Hugo J. Spiers, The Versatile Wayfinder: Prefrontal Contributions to Spatial Navigation, . Trends in Cognitive Sciences, Volume 25, Issue 6, 2021, Pages 520-533 DOI: 10.1016/j.tics.2021.02.010.

The prefrontal cortex (PFC) supports decision-making, goal tracking, and planning. Spatial navigation is a behavior that taxes these cognitive processes, yet the role of the PFC in models of navigation has been largely overlooked. In humans, activity in dorsolateral PFC (dlPFC) and ventrolateral PFC (vlPFC) during detours, reveal a role in inhibition and replanning. Dorsal anterior cingulate cortex (dACC) is implicated in planning and spontaneous internally-generated changes of route. Orbitofrontal cortex (OFC) integrates representations of the environment with the value of actions, providing a ‘map’ of possible decisions. In rodents, medial frontal areas interact with hippocampus during spatial decisions and switching between navigation strategies. In reviewing these advances, we provide a framework for how different prefrontal regions may contribute to different stages of navigation.

Posted in: Psycho-physiological bases of engineering , Tagged:

Generating contrafactual explanations of Deep RL decisions to identify flawed agents

July 11, 2023 08:44 ,

Matthew L. Olson, Roli Khanna, Lawrence Neal, Fuxin Li, Weng-Keen Wong, Counterfactual state explanations for reinforcement learning agents via generative deep learning, . Artificial Intelligence, Volume 295, 2021 DOI: 10.1016/j.artint.2021.103455.

Counterfactual explanations, which deal with “why not?” scenarios, can provide insightful explanations to an AI agent’s behavior [Miller [38]]. In this work, we focus on generating counterfactual explanations for deep reinforcement learning (RL) agents which operate in visual input environments like Atari. We introduce counterfactual state explanations, a novel example-based approach to counterfactual explanations based on generative deep learning. Specifically, a counterfactual state illustrates what minimal change is needed to an Atari game image such that the agent chooses a different action. We also evaluate the effectiveness of counterfactual states on human participants who are not machine learning experts. Our first user study investigates if humans can discern if the counterfactual state explanations are produced by the actual game or produced by a generative deep learning approach. Our second user study investigates if counterfactual state explanations can help non-expert participants identify a flawed agent; we compare against a baseline approach based on a nearest neighbor explanation which uses images from the actual game. Our results indicate that counterfactual state explanations have sufficient fidelity to the actual game images to enable non-experts to more effectively identify a flawed RL agent compared to the nearest neighbor baseline and to having no explanation at all.

Posted in: Reinforcement learning in AI , Tagged: ,

POMDPs to combine human semantic sensing with robot sensing

July 10, 2023 08:37 ,

Luke Burks, Nisar Ahmed, Ian Loefgren, Luke Barbier, Jeremy Muesing, Jamison McGinley, Sousheel Vunnam, Collaborative human-autonomy semantic sensing through structured POMDP planning, . Robotics and Autonomous Systems, Volume 140, 2021 DOI: 10.1016/j.robot.2021.103753.

Autonomous unmanned systems and robots must be able to actively leverage all available information sources — including imprecise but readily available semantic observations provided by human collaborators. This work develops and validates a novel active collaborative human–machine sensing solution for robotic information gathering and optimal decision making problems, with an example implementation of a dynamic target search scenario. Our approach uses continuous partially observable Markov decision process (CPOMDP) planning to generate vehicle trajectories that optimally exploit imperfect detection data from onboard sensors, as well as semantic natural language observations that can be specifically requested from human sensors. The key innovations are a method for the inclusion of a human querying/sensing model in a CPOMDP based autonomous decision making process, as well as a scalable hierarchical Gaussian mixture model formulation for efficiently solving CPOMDPs with semantic observations in continuous dynamic state spaces. Unlike previous state-of-the-art approaches this allows planning in large, complex, highly segmented environments. Our solution is demonstrated and validated with a real human–robot team engaged in dynamic indoor target search and capture scenarios on a custom testbed..

Posted in: Human-robot interaction , Tagged: ,

Studying magician tricks to understand decision making and how to influence it

July 10, 2023 08:31 ,

Alice Pailhès, Gustav Kuhn, Mind Control Tricks: Magicians’ Forcing and Free Will, . Trends in Cognitive Sciences, Volume 25, Issue 5, 2021, Pages 338-341 DOI: 10.1016/j.tics.2021.02.001.

A new research program has recently emerged that investigates magicians’ mind control tricks, also called forces. This research highlights the psychological processes that underpin decision-making, illustrates the ease by which our decisions can be covertly influenced, and helps answer questions about our sense of free will and agency over choices.

Posted in: Psycho-physiological bases of engineering , Tagged:

Improving POMDP solving efficiency by eliminating variables in the state structure

July 10, 2023 08:27 ,

Eric A. Hansen, An integrated approach to solving influence diagrams and finite-horizon partially observable decision processes, . Artificial Intelligence, Volume 294, 2021 DOI: 10.1016/j.artint.2020.103431.

We show how to integrate a variable elimination approach to solving influence diagrams with a value iteration approach to solving finite-horizon partially observable Markov decision processes (POMDPs). The integration of these approaches creates a variable elimination algorithm for influence diagrams that has much more relaxed constraints on elimination order, which allows improved scalability in many cases. The new algorithm can also be viewed as a generalization of the value iteration algorithm for POMDPs that solves non-Markovian as well as Markovian problems, in addition to leveraging a factored representation for improved efficiency. The development of a single algorithm that integrates and generalizes both of these classic algorithms, one for influence diagrams and the other for POMDPs, unifies these two approaches to solving Bayesian decision problems in a way that combines their complementary advantages.

Posted in: Artificial Intelligence , Tagged: ,

Cubature (fixed point representation of uncertainties, as in UKF) Kalman Filter

July 7, 2023 11:53 ,

Juan-Carlos Santos-León, Ramón Orive, Daniel Acosta, Leopoldo Acosta, The Cubature Kalman Filter revisited, . Automatica, Volume 127, 2021 DOI: 10.1016/j.automatica.2021.109541.

In this paper, the construction and effectiveness of the so-called Cubature Kalman Filter (CKF) is revisited, as well as its extensions for higher degrees of precision. In this sense, some stable (with respect to the dimension) cubature rules with a quasi-optimal number of nodes are built, and their numerical performance is checked in comparison with other known formulas. All these cubature rules are suitably placed in the mathematical framework of numerical integration in several variables. A method based on the discretization of higher order partial derivatives by certain divided differences is used to provide stable rules of degrees d=5 and d=7, though it can also be applied for higher dimensions. The application of these old and new formulas to the filter algorithm is tested by means of some examples.

Posted in: Bayesian filtering, Control Engineering , Tagged: ,

A hierarchical POMDP system for robot manipulation

July 7, 2023 11:42 ,

Wenrui Zhao, Weidong Chen, Hierarchical POMDP planning for object manipulation in clutter, . Robotics and Autonomous Systems, Volume 139, 2021 DOI: 10.1016/j.robot.2021.103736.

Object manipulation planning in clutter suffers from perception uncertainties due to occlusion, as well as action constraints required by collision avoidance. Partially observable Markov decision process (POMDP) provides a general model for planning under uncertainties. But a manipulation task usually have a large action space, which not only makes task planning intractable but also brings significant motion planning effort to check action feasibility. In this work, a new kind of hierarchical POMDP is presented for object manipulation tasks, in which a brief abstract POMDP is extracted and utilized together with the original POMDP. And a hierarchical belief tree search algorithm is proposed for efficient online planning, which constructs fewer belief nodes by building part of the tree with the abstract POMDP and invokes motion planning fewer times by determining action feasibility with observation function of the abstract POMDP. A learning mechanism is also designed in case there are unknown probabilities in transition and observation functions. This planning framework is demonstrated with an object fetching task and the performance is empirically validated by simulations and experiments.

Posted in: Industrial robots , Tagged: ,

A hierarchical robot control architecture that supports learning of skills at different levels through “curriculum learning” and an interesting approach to mix behaviours

July 7, 2023 11:37 ,

Suro, F., Ferber, J., Stratulat, T. et al., A hierarchical representation of behaviour supporting open ended development and progressive learning for artificial agents, . Auton Robot 45, 245–264 (2021) DOI: 10.1007/s10514-020-09960-7.

One of the challenging aspects of open ended or lifelong agent development is that the final behaviour for which an agent is trained at a given moment can be an element for the future creation of one, or even several, behaviours of greater complexity, whose purpose cannot be anticipated. In this paper, we present modular influence network design (MIND), an artificial agent control architecture suited to open ended and cumulative learning. The MIND architecture encapsulates sub behaviours into modules and combines them into a hierarchy reflecting the modular and hierarchical nature of complex tasks. Compared to similar research, the main original aspect of MIND is the multi layered hierarchy using a generic control signal, the influence, to obtain an efficient global behaviour. This article shows the ability of MIND to learn a curriculum of independent didactic tasks of increasing complexity covering different aspects of a desired behaviour. In so doing we demonstrate the contributions of MIND to open-ended development: encapsulation into modules allows for the preservation and re-usability of all the skills acquired during the curriculum and their focused retraining, the modular structure serves the evolving topology by easing the coordination of new sensors, actuators and heterogeneous learning structures.

Posted in: Applications of reinforcement learning to robots, Developmental robotics , Tagged: ,

Model-based (on ordinary differential equations) and partially model-free Policy Iteration on continuous space and time

July 7, 2023 11:28 ,

Jaeyoung Lee, Richard S. Sutton, Policy iterations for reinforcement learning problems in continuous time and space — Fundamental theory and methods, . Automatica, Volume 126, 2021 DOI: 10.1016/j.automatica.2020.109421.

Policy iteration (PI) is a recursive process of policy evaluation and improvement for solving an optimal decision-making/control problem, or in other words, a reinforcement learning (RL) problem. PI has also served as the fundamental for developing RL methods. In this paper, we propose two PI methods, called differential PI (DPI) and integral PI (IPI), and their variants, for a general RL framework in continuous time and space (CTS), where the environment is modeled by a system of ordinary differential equations (ODEs). The proposed methods inherit the current ideas of PI in classical RL and optimal control and theoretically support the existing RL algorithms in CTS: TD-learning and value-gradient-based (VGB) greedy policy update. We also provide case studies including (1) discounted RL and (2) optimal control tasks. Fundamental mathematical properties – admissibility, uniqueness of the solution to the Bellman equation (BE), monotone improvement, convergence, and optimality of the solution to the Hamilton–Jacobi–Bellman equation (HJBE) – are all investigated in-depth and improved from the existing theory, along with the general and case studies. Finally, the proposed ones are simulated with an inverted-pendulum model and their model-based and partially model-free implementations to support the theory and further investigate them beyond.

Posted in: Reinforcement learning in AI , Tagged: ,