Community Research and Development Information Service - CORDIS

H2020

GOAL-Robots Report Summary

Project ID: 713010
Funded under: H2020-EU.1.2.1.

Periodic Reporting for period 1 - GOAL-Robots (Goal-based Open-ended Autonomous Learning Robots)

Reporting period: 2016-11-01 to 2017-10-31

Summary of the context and overall objectives of the project

What is the problem/issue being addressed?
Imagine your friend asks you to tidy up her room which is full of furniture and objects. Now imagine that your friend described through a photo how the room should look like after being tidy up. This task would be boring for you, but you would nevertheless be able to carry it out with ease. Indeed, when you were a child you played with all sort of objects and so, driven by curiosity, you learn many flexible sensorimotor skills to manipulate them at will.
Differently from you, current robots are ill-suited for these type of challenges. Indeed, although new architecture and algorithms are developed to control robots, leading them to accomplish tasks in unstructured environments still requires much programming or supervised training. Moreover, in such current robots can become able to solve specific tasks but in comparison to biological agents they have a very limited autonomy and flexibility.

Why the project is important for society?
For many applications, future robots should be able to learn how to solve multiple tasks in unstructured environments in an autonomous way. The project aims to build artificial intelligence architectures that should allow robots to self-generate goals and autonomously learn sensorimotor skills to accomplish them, based on the cumulative acquisition, modification, and recombination of previously acquired skills. This will allow robots to accomplish users’ tasks (e.g., to set a room in a desired state) with little additional learning.

What are the project objectives?
The project follows a previous European project called IM-CLeVeR (“Intrinsically Motivated Cumulative Learning Versatile Robots”) which investigated how intrinsic motivations (IMs) can support autonomous learning in biological and artificial agents. IMs, related to novelty, surprise, and competence acquisition, are maximally apparent in children at play: driven by IMs, children explore and interact with objects in the environment thus getting to know how they work and to acquire a wide set of sensorimotor skills suitable to manipulate them at will.
GOAL-Robots central idea is that IMs can fully support open-ended learning only if a further critical ingredient is considered: goals. A goal is an internal representation of a state (or set of states, or trajectories of states) of the world that can be internally activated by the agent in the absence of its current perception: when a goal representation is internally activated, it can contribute to move the agent’s attention, behaviour, and learning resources towards the accomplishment of the goal. The project idea is that endowing robots with the capacity of self-generating goals and using them to learn the skills for their accomplishment is the key ingredient to have truly autonomous open-ended learning robots.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

The project concept is pursued through three main objectives: (1) advance our understanding of how goals are formed and underlie skill learning in children; (2) develop innovative computational architectures and algorithms supporting (2a) the self-generation of useful goals based on user/task-independent mechanisms such as intrinsic motivations, and (2b) the use of such goals to efficiently and autonomously build large repertoires of skills; (3) demonstrate the potential of project idea with a series of increasingly challenging demonstrators in which robots autonomously acquire complex skills and use them to solve difficult challenges in real-life scenarios.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The project aims to develop a new paradigm to build open-ended learning robots. This paradigm rests upon two key insights. First, to exhibit an autonomous open-ended learning process, robots should be able to self-generate goals, and hence tasks to practice. Second, new learning algorithms can leverage self-generated goals to dramatically accelerate skill learning. The new paradigm will allow robots to acquire a large repertoire of flexible skills in conditions unforeseeable at design time with little human intervention, and then to exploit these skills to efficiently solve new user-defined tasks with no/little additional learning. The project will advance our understanding of the fundamental principles of open-ended learning in autonomous robotics by producing robots that can autonomously accumulate complex skills and knowledge in a truly open-ended way. This innovation will be essential in the design of future service robots addressing pressing societal needs.

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