Strategic and interactive learning of a hierarchical set of tasks by the Poppy humanoid robot

Duminy, Nicolas; Nguyen, Sao Mai; Duhaut, Dominique

doi:10.1109/devlrn.2016.7846820

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…The SGIM-ACTS algorithm relies on the empirical evaluation of its learning progress. It showed its potential to learn on a real high dimensional robot a set of hierarchically organized tasks in Duminy et al (2016). This is why we consider to extend SGIM-ACTS to learn to associate a large number of tasks to motor policy sequences.…”

Section: Introductionmentioning

confidence: 99%

Learning a Set of Interrelated Tasks by Using a Succession of Motor Policies for a Socially Guided Intrinsically Motivated Learner

2019

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We aim at a robot capable to learn sequences of actions to achieve a field of complex tasks. In this paper, we are considering the learning of a set of interrelated complex tasks hierarchically organized. To learn this high-dimensional mapping between a continuous high-dimensional space of tasks and an infinite dimensional space of unbounded sequences of actions, we introduce a new framework called “procedures”, which enables the autonomous discovery of how to combine previously learned skills in order to learn increasingly complex combinations of motor policies. We propose an active learning algorithmic architecture, capable of organizing its learning process in order to achieve a field of complex tasks by learning sequences of primitive motor policies. Based on heuristics of active imitation learning, goal-babbling and strategic learning using intrinsic motivation, our algorithmic architecture leverages our procedures framework to actively decide during its learning process which outcome to focus on and which exploration strategy to apply. We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies by adapting the complexity of its policies to the task at hand. We also show that our “procedures” enable the learning agent to discover the task hierarchy and exploit his experience of previously learned skills to learn new complex tasks.

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Section: Introductionmentioning

confidence: 99%

Learning a Set of Interrelated Tasks by Using a Succession of Motor Policies for a Socially Guided Intrinsically Motivated Learner

2019

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“…To build the upper part of the robot, we used the open-source project Poppy Torso (Lapeyre et al, 2014;Duminy et al, 2016). The robot is based on Dynamixel smart servomotors and 3D printed plastic elements.…”

Section: Robot and Avatar Designmentioning

confidence: 99%

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

et al. 2020

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Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood. Here we show how our novel concept, known as time compaction, provides a natural way of representing semantic knowledge of actions in time-changing situations. As a testbed, we model a fencing scenario with a subject deciding between attack and defense strategies. The semantic content of each action in terms of lethality, versatility, and imminence is then structured as a spatial (static) map representing a particular fencing (dynamic) situation. The model allows deploying a variety of cognitive strategies in a fast and reliable way. We validate the approach in virtual reality and by using a real humanoid robot.

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“…The method used is described in [15]. The choice of strategy and goal outcome is based on the empirical progress measured in each region R n of the outcome space Ω, as in [11].…”

Section: Algorithm 1 Procedures Modification Before Executionmentioning

confidence: 99%

Learning a Set of Interrelated Tasks by Using Sequences of Motor Policies for a Strategic Intrinsically Motivated Learner

Duminy

Nguyen

Duhaut

2018

2018 Second IEEE International Conference on Robotic Computing (IRC)

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We propose an active learning architecture for robots, capable of organizing its learning process to achieve a field of complex tasks by learning sequences of motor policies, called Intrinsically Motivated Procedure Babbling (IM-PB). The learner can generalize over its experience to continuously learn new tasks. It chooses actively what and how to learn based by empirical measures of its own progress. In this paper, we are considering the learning of a set of interrelated tasks outcomes hierarchically organized.We introduce a framework called "procedures", which are sequences of policies defined by the combination of previously learned skills . Our algorithmic architecture uses the procedures to autonomously discover how to combine simple skills to achieve complex goals. It actively chooses between 2 strategies of goaldirected exploration: exploration of the policy space or the procedural space. We show on a simulated environment that our new architecture is capable of tackling the learning of complex motor policies, to adapt the complexity of its policies to the task at hand. We also show that our "procedures" framework helps the learner to tackle difficult hierarchical tasks.

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Strategic and interactive learning of a hierarchical set of tasks by the Poppy humanoid robot

Cited by 9 publications

References 23 publications

Learning a Set of Interrelated Tasks by Using a Succession of Motor Policies for a Socially Guided Intrinsically Motivated Learner

Learning a Set of Interrelated Tasks by Using a Succession of Motor Policies for a Socially Guided Intrinsically Motivated Learner

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations

Learning a Set of Interrelated Tasks by Using Sequences of Motor Policies for a Strategic Intrinsically Motivated Learner

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