Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots, and deploying these systems for long-term installations in security and care environments. Over four deployments, our robots have been operational for a combined duration of 104 days autonomously performing end-user defined tasks, covering 116km in the process. In this article we describe the approach we have used to enable long-term autonomous operation in everyday environments, and how our robots are able to use their long run times to improve their own performance
We present a method to specify tasks and synthesise cost-optimal policies for Markov decision processes using co-safe linear temporal logic. Our approach incorporates a dynamic task handling procedure which allows for the addition of new tasks during execution and provides the ability to replan an optimal policy on-the-fly. This new policy minimises the cost to satisfy the conjunction of the current tasks and the new one, taking into account how much of the current tasks has already been executed. We illustrate our approach by applying it to motion planning for a mobile service robot.
Abstract-In planning for deliberation or navigation in realworld robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot's chances of successively accomplishing actions in this future. Hence, a robot's plan can only be as good as its predictions about the world. In this paper, we present a novel approach to specifically represent changes that stem from periodic events in the environment (e.g. a door being opened or closed), which impact on the success probability of planned actions. We show that our approach to model the probability of action success as a set of superimposed periodic processes allows the robot to predict action outcomes in a long-term data obtained in two real-life offices better than a static model. We furthermore discuss and showcase how this knowledge gathered can be successfully employed in a probabilistic planning framework to devise better navigation plans. The key contributions of this paper are (i) the formation of the spectral model of action outcomes from non-uniform sampling, the (ii) analysis of its predictive power using two long-term datasets, and (iii) the application of the predicted outcomes in an MDP-based planning framework.
We present a framework for mobile service robot task planning and execution, based on the use of probabilistic verification techniques for the generation of optimal policies with attached formal performance guarantees. Our approach is based on a Markov decision process model of the robot in its environment, encompassing a topological map where nodes represent relevant locations in the environment, and a range of tasks that can be executed in different locations. The navigation in the topological map is modeled stochastically for a specific time of day. This is done by using spatio-temporal models that provide, for a given time of day, the probability of successfully navigating between two topological nodes, and the expected time to do so. We then present a methodology to generate cost optimal policies for tasks specified in co-safe linear temporal logic. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We introduce a task progression function and present an approach to generate policies that are formally guaranteed to, in decreasing order of priority: maximize the probability of finishing the task; maximize progress towards completion, if this is not possible; and minimize the expected time or cost required. We illustrate and evaluate our approach with a scalability evaluation in a simulated scenario, and report on its implementation in a robot performing service tasks in an office environment for long periods of time.
This work presents an approach for control, stateestimation and learning model (hyper)parameters for robotic manipulators. It is based on the active inference framework, prominent in computational neuroscience as a theory of the brain, where behaviour arises from minimizing variational freeenergy. The robotic manipulator shows adaptive and robust behaviour compared to state-of-the-art methods. Additionally, we show the exact relationship to classic methods such as PID control. Finally, we show that by learning a temporal parameter and model variances, our approach can deal with unmodelled dynamics, damps oscillations, and is robust against disturbances and poor initial parameters. The approach is validated on the 'Franka Emika Panda' 7 DoF manipulator.
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