2,822 research outputs found
A proposal for a global task planning architecture using the RoboEarth cloud based framework
As robotic systems become more and more capable of assisting in human domains, methods are sought to compose robot executable plans from abstract human instructions. To cope with the semantically rich and highly expressive nature of human instructions, Hierarchical Task Network planning is often being employed along with domain knowledge to solve planning problems in a pragmatic way. Commonly, the domain knowledge is specific to the planning problem at hand, impeding re-use. Therefore this paper conceptualizes a global planning architecture, based on the worldwide accessible RoboEarth cloud framework. This architecture allows environmental state inference and plan monitoring on a global level. To enable plan re-use for future requests, the RoboEarth action language has been adapted to allow semantic matching of robot capabilities with previously composed plans
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Between MDPs and Semi-MDPs:Learning, Planning, and Representing Knowledge at Multiple Temporal Scales
Learning, planning, and representing knowledge at multiple levels of temporal abstraction are key challenges for AI. In this paper we develop an approach to these problems based on the mathematical framework of reinforcement learning and Markov decision processes (MDPs). We extend the usual notion of action to include options|whole courses of behavior that may be temporally extended, stochastic, and contingent on events. Examples of options include picking up an object, going to lunch, and traveling to a distant city, as well as primitive actions such as muscle twitches and joint torques. Options may be given a priori, learned by experience, or both. They may be used interchangeably with actions in a variety of planning and learning methods. The theory of semi-Markov decision processes (SMDPs) can be applied to model the consequences of options and as a basis for planning and learning methods using them. In this paper we develop these connections, building on prior work by Bradtke and Du (1995), Parr (in prep.) and others. Our main novel results concern the interface between the MDP and SMDP levels of analysis. We show how a set of options can be altered by changing only their termination conditions to improve over SMDP methods with no additional cost. We also introduce intra-option temporal-dierence methods that are able to learn from fragments of an option\u27s execution. Finally, we propose a notion of subgoal which can be used to improve the options themselves. Overall, we argue that options and their models provide hitherto missing aspects of a powerful, clear, and expressive framework for representing and organizing knowledge
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior
This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic
causal model for predicting the behavior generated by modern percept-driven
robot plans. PHAMs represent aspects of robot behavior that cannot be
represented by most action models used in AI planning: the temporal structure
of continuous control processes, their non-deterministic effects, several modes
of their interferences, and the achievement of triggering conditions in
closed-loop robot plans.
The main contributions of this article are: (1) PHAMs, a model of concurrent
percept-driven behavior, its formalization, and proofs that the model generates
probably, qualitatively accurate predictions; and (2) a resource-efficient
inference method for PHAMs based on sampling projections from probabilistic
action models and state descriptions. We show how PHAMs can be applied to
planning the course of action of an autonomous robot office courier based on
analytical and experimental results
Principles for Consciousness in Integrated Cognitive Control
In this article we will argue that given certain conditions for the evolution of bi- \ud
ological controllers, these will necessarily evolve in the direction of incorporating \ud
consciousness capabilities. We will also see what are the necessary mechanics for \ud
the provision of these capabilities and extrapolate this vision to the world of artifi- \ud
cial systems postulating seven design principles for conscious systems. This article \ud
was published in the journal Neural Networks special issue on brain and conscious- \ud
ness
Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R
This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud
To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud
A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems
Final report key contents: main results accomplished by the EU-Funded project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots
This document has the goal of presenting the main scientific and technological achievements of the project IM-CLeVeR. The document is organised as follows: 1. Project executive summary: a brief overview of the project vision, objectives and keywords. 2. Beneficiaries of the project and contacts: list of Teams (partners) of the project, Team Leaders and contacts. 3. Project context and objectives: the vision of the project and its overall objectives 4. Overview of work performed and main results achieved: a one page overview of the main results of the project 5. Overview of main results per partner: a bullet-point list of main results per partners 6. Main achievements in detail, per partner: a throughout explanation of the main results per partner (but including collaboration work), with also reference to the main publications supporting them
Accelerating decision making under partial observability using learned action priors
Thesis (M.Sc.)--University of the Witwatersrand, Faculty of Science, School of Computer Science and Applied Mathematics, 2017.Partially Observable Markov Decision Processes (POMDPs) provide a principled mathematical
framework allowing a robot to reason about the consequences of actions and
observations with respect to the agent's limited perception of its environment. They
allow an agent to plan and act optimally in uncertain environments. Although they
have been successfully applied to various robotic tasks, they are infamous for their high
computational cost. This thesis demonstrates the use of knowledge transfer, learned
from previous experiences, to accelerate the learning of POMDP tasks. We propose
that in order for an agent to learn to solve these tasks quicker, it must be able to generalise
from past behaviours and transfer knowledge, learned from solving multiple tasks,
between di erent circumstances. We present a method for accelerating this learning
process by learning the statistics of action choices over the lifetime of an agent, known
as action priors. Action priors specify the usefulness of actions in situations and allow
us to bias exploration, which in turn improves the performance of the learning process.
Using navigation domains, we study the degree to which transferring knowledge
between tasks in this way results in a considerable speed up in solution times.
This thesis therefore makes the following contributions. We provide an algorithm
for learning action priors from a set of approximately optimal value functions and two
approaches with which a prior knowledge over actions can be used in a POMDP context.
As such, we show that considerable gains in speed can be achieved in learning subsequent
tasks using prior knowledge rather than learning from scratch. Learning with
action priors can particularly be useful in reducing the cost of exploration in the early
stages of the learning process as the priors can act as mechanism that allows the agent
to select more useful actions given particular circumstances. Thus, we demonstrate how
the initial losses associated with unguided exploration can be alleviated through the
use of action priors which allow for safer exploration. Additionally, we illustrate that
action priors can also improve the computation speeds of learning feasible policies in a
shorter period of time.MT201
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