Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version

Mapping trajectories of becoming: four forms of behaviour in co-housing initiatives

In order learn about planning in a world increasingly characterised by resource interdependencies and a plurality of governing agencies, this paper follows the processes of becoming for two co-housing initiatives. Self-organisation – understood as the emergence of actor-networks – is the leading theoretical concept, complemented by translation from actor-network theory and individuation from assemblage theory. This theoretical hybrid distinguishes four forms of behaviour (decoding, coding, expansion and contraction) that are used to analyse the dynamics of becoming in the two cases. As a result, information is revealed on the conditions that give rise to co-housing initiatives, and the dynamic interactions between planning authorities, (groups of) initiators and other stakeholders that gave shape to the initiatives. Differences between these actors become blurred, as both try to create meaning and reasoning in a non-linear, complex and uncertain world. The paper concludes with a view on planning as an act of adaptive navigation, an act equally performed by professionals working for planning authorities and a case initiator

Intelligent execution monitoring and error analysis in planning involving processes

An intelligent agent, operating in an external world which cannot be fully described in its internal world model, must be able to monitor the success of a previously generated plan and to respond to any errors which may have occurred. The process of error analysis requires the ability to reason in an expert fashion about time and about processes occurring in the world. Reasoning about time is needed to deal with causality. Reasoning about processes is needed since the direct effects of a plan action can be completely specified when the plan is generated, but the indirect effects cannot. For example, the action `open tap' leads with certainty to `tap open', whereas whether there will be a fluid flow and how long it might last is more difficult to predict. The majority of existing planning systems cannot handle these kinds of reasoning, thus limiting their usefulness. This thesis argues that both kinds of reasoning require a complex internal representation of the world. The use of Qualitative Process Theory and an interval-based representation of time are proposed as a representation scheme for such a world model. The planning system which was constructed has been tested on a set of realistic planning scenarios. It is shown that even simple planning problems, such as making a cup of coffee, require extensive reasoning if they are to be carried out successfully. The final Chapter concludes that the planning system described does allow the correct solution of planning problems involving complex side effects, which planners up to now have been unable to solve

Towards The Integration of Model Predictive Control into an AI Planning Framework

This paper describes a framework for a hybrid algorithm that combines both AI Planning and Model Predictive Control approaches to reason with processes and events within a domain. This effectively utilises the strengths of search-based and model-simulation-based methods. We explore this control approach and show how it can be embedded into existing, modern AI Planning technology. This preserves the many advantages of the AI Planning approach, to do with domain independence through declarative modelling, and explicit reasoning, while leveraging the capability of MPC to deal with continuous processes computation within such domains. The developed technique is tested on an urban traffic control application and the results demonstrate the potential in utilising MPC as a heuristic to guide planning search

iCORPP: Interleaved Commonsense Reasoning and Probabilistic Planning on Robots

Robot sequential decision-making in the real world is a challenge because it requires the robots to simultaneously reason about the current world state and dynamics, while planning actions to accomplish complex tasks. On the one hand, declarative languages and reasoning algorithms well support representing and reasoning with commonsense knowledge. But these algorithms are not good at planning actions toward maximizing cumulative reward over a long, unspecified horizon. On the other hand, probabilistic planning frameworks, such as Markov decision processes (MDPs) and partially observable MDPs (POMDPs), well support planning to achieve long-term goals under uncertainty. But they are ill-equipped to represent or reason about knowledge that is not directly related to actions. In this article, we present a novel algorithm, called iCORPP, to simultaneously estimate the current world state, reason about world dynamics, and construct task-oriented controllers. In this process, robot decision-making problems are decomposed into two interdependent (smaller) subproblems that focus on reasoning to "understand the world" and planning to "achieve the goal" respectively. Contextual knowledge is represented in the reasoning component, which makes the planning component epistemic and enables active information gathering. The developed algorithm has been implemented and evaluated both in simulation and on real robots using everyday service tasks, such as indoor navigation, dialog management, and object delivery. Results show significant improvements in scalability, efficiency, and adaptiveness, compared to competitive baselines including handcrafted action policies

Consumer Thinking in Decision-Making: Applying a Cognitive Framework to Trip Planning

As magnetic resonance studies (fMRI) evolve, we will learn more about how the brain operates during consumer decision making. Until that time, social science research can assist with understanding consumers’ thought processes, as in the case of this report, which focuses on consumer decision making in connection with travel planning. This paper examines the application of a cognitive framework that is currently used in education to better understand, address, and improve thinking skills, which appears to apply to hospitality consumers’ decision making. Two pilot studies of trip planning, by graduate and undergraduate students, demonstrate the potential usefulness of this cognitive framework. Since so much of the cognitive processing involved in trip planning appears to occur unconsciously, bringing the thinking involved to our conscious awareness may improve the process for consumers. Using the pilot studies and personal experience, this report explains the framework’s use for consumer decision-making and suggests ways that may help us better understand and address the cognition that happens as consumers make complex travel decisions. After an early model developed in 1961 by Albert Upton, the model of eight forms of cognition examined here was formed more recently by David Hyerle. The eight forms of thinking are: (1) Defining in context, (2) Describing attributes, (3) Sequencing, (4) Causal reasoning, (5) Using analogies, (6) Comparing and contrasting, (7) Categorical reasoning, and (8) Spatial reasoning. Some of these forms of cognition appear to apply more strongly than others in trip planning, such as determining the context of the trip, describing and comparing attributes, and considering spatial or location issues. Not expressly included in the framework, but essential to an understanding of trip planning processes is the social context of the trip and those planning to travel. Moreover, since fMRI studies have shown that the brain is parsimonious and attempts to operate as efficiently as possible, any aid to decision making should be well received. A better understanding of these specific forms of thinking may allow those in the hospitality industry opportunities to create specific ways of streamlining and purposefully addressing consumer decision-making thinking processes more strategically. In particular, for example, those in the industry might consider ways to facilitate each of these forms of thinking at different stages of the trip-planning process

Developing and validating tools to assess higher level cognition in children and adolescents

Collaborating with CBS, we will create a unique platform for understanding, detecting and predicting delays in cognition during the formative period from childhood to adolescence. The aim of this project is to develop and validate a battery of tests specifically for children and adolescents between the ages of 7 and 15 to measure various aspects of higher-level cognitive abilities. These include short-term and episodic memory, planning, reasoning, verbal abilities and executive functioning (those processes necessary to control behaviour, such as controlling attention and inhibition, working memory, reasoning and problem solving).https://ir.lib.uwo.ca/brainscanprojectsummaries/1042/thumbnail.jp