A bioinspired hierarchical reinforcement learning architecture for modeling learning of multiple skills with continuous state and actions

Organisms, and especially primates, are able to learn several skills while avoiding catastrophic interference and enhancing generalisation. This paper proposes a novel hierarchical reinforcement learning (RL) architecture with a number of features that make it suitable to investigate such phenomena. The proposed system combines the mixture of experts architecture with the neural-network actor-critic architecture trained with the TD() reinforcement learning algorithm. In particular, responsibility signals provided by two gating networks (one for the actor and one for the critic) are used both to weight the outputs of the respective multiple (expert) controllers and to modulate their learning. The system is tested with a simulated dynamic 2D robotic arm that autonomously learns to reach a target in (up to) three different conditions. The results show that the system is able to appropriately allocate experts to tasks on the basis of the differences and similarities among the required sensorimotor mappings

Reinforcement learning algorithms that assimilate and accommodate skills with multiple tasks

Children are capable of acquiring a large repertoire of motor skills and of efficiently adapting them to novel conditions. In a previous work we proposed a hierarchical modular reinforcement learning model (RANK) that can learn multiple motor skills in continuous action and state spaces. The model is based on a development of the mixture-of-expert model that has been suitably developed to work with reinforcement learning. In particular, the model uses a high-level gating network for assigning responsibilities for acting and for learning to a set of low-level expert networks. The model was also developed with the goal of exploiting the Piagetian mechanisms of assimilation and accommodation to support learning of multiple tasks. This paper proposes a new model (TERL - Transfer Expert Reinforcement Learning) that substantially improves RANK. The key difference with respect to the previous model is the decoupling of the mechanisms that generate the responsibility signals of experts for learning and for control. This made possible to satisfy different constraints for functioning and for learning. We test both the TERL and the RANK models with a two-DOFs dynamic arm engaged in solving multiple reaching tasks, and compare the two with a simple, flat reinforcement learning model. The results show that both models are capable of exploiting assimilation and accommodation processes in order to transfer knowledge between similar tasks, and at the same time to avoid catastrophic interference. Furthermore, the TERL model is shown to significantly outperform the RANK model thanks to its faster and more stable specialization of experts

A bio-inspired learning signal for the cumulative learning of different skills

Building artificial agents able to autonomously learn new skills and to easily adapt in different and complex environments is an important goal for robotics and machine learning. We propose that providing artificial agents with a learning signal that resembles the characteristic of the phasic activations of dopaminergic neurons would be an advancement in the development of more autonomous and versatile systems. In particular, we suggest that the particular composition of such a signal, determined both by intrinsic and extrinsic reinforcements, would be suitable to improve the implementation of cumulative learning. To validate our hypothesis we performed some experiments with a simulated robotic system that has to learn different skills to obtain rewards. We compared different versions of the system varying the composition of the learning signal and we show that only the system that implements our hypothesis is able to reach high performance in the task

Cumulative learning through intrinsic reinforcements

Building artificial agents able to autonomously learn new skills and to easily adapt in different and complex environments is an important goal for robotics and machine learning. We propose that providing reinforcement learning artificial agents with a learning signal that resembles the charac- teristic of the phasic activations of dopaminergic neurons would be an advancement in the development of more autonomous and versatile systems. In particular, we suggest that the particular composition of such a signal, determined by both extrinsic and intrinsic reinforcements, would be suitable to improve the implementation of cumulative learning in artificial agents. To validate our hypothesis we performed experiments with a simulated robotic system that has to learn different skills to obtain extrinsic rewards. We compare different versions of the system varying the composition of the learning signal and we show that the only system able to reach high performance in the task is the one that implements the learning signal suggested by our hypothesis

Functions and mechanisms of intrinsic motivations: the knowledge versus competence distinction

Mammals, and humans in particular, are endowed with an exceptional capacity for cumulative learning. This capacity crucially de- pends on the presence of intrinsic motivations, i.e. motivations that are not directly related to an organism\u27s survival and reproduction but rather to its ability to learn. Recently, there have been a number of attempts to model and reproduce intrinsic motivations in artificial systems. Different kinds of intrinsic motivations have been proposed both in psychology and in machine learning and robotics: some are based on the knowl- edge of the learning system, while others are based on its competence. In this contribution we discuss the distinction between knowledge-based and competence-based intrinsic motivations with respect to both the functional roles that motivations play in learning and the mechanisms by which those functions are implemented. In particular, after arguing that the principal function of intrinsic motivations consists in allowing the development of a repertoire of skills (rather than of knowledge), we suggest that at least two different sub-functions can be identified: (a) discovering which skills might be acquired and (b) deciding which skill to train when. We propose that in biological organisms knowledge-based intrinsic motivation mechanisms might implement the former function, whereas competence-based mechanisms might underly the latter one

Modular and hierarchical brain organization to understand assimilation, accommodation and their relation to autism in reaching tasks: a developmental robotics hypothesis

By "assimilation" the child embodies the sensorimotor experience into already built mental structures. Conversely, by "accommodation" these structures are changed according to the child\u27s new experiences. Despite the intuitive power of these concepts to trace the course of sensorimotor development, they have gradually lost ground in psychology. This likely for a lack of brain related views capturing the dynamic mechanisms underlying them. Here we propose that brain modular and hierarchical organization is crucial to understanding assimilation/accommodation. We devised an experiment where a bio-inspired modular and hierarchical mixture-of-experts model guides a simulated robot to learn by trial-and-error different reaching tasks. The model gives a novel interpretation of assimilation/accommodation based on the functional organization of the experts allocated through learning. Assimilation occurs when the model adapts a copy of the expert trained for solving a task to face another task requiring similar sensorimotor mappings. Experts storing similar sensorimotor mappings belong to the same functional module. Accommodation occurs when the model uses non-trained experts to face tasks requiring different sensorimotor mappings (generating a new functional group of experts). The model provides a new theoretical framework to investigate impairments in assimilation/accommodation the autistic syndrome

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

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

The Role of Learning and Kinematic Features in Dexterous Manipulation: a Comparative Study with Two Robotic Hands

Dexterous movements performed by the human hand are by far more sophisticated than those achieved by current humanoid robotic hands and systems used to control them. This work aims at providing a contribution in order to overcome this gap by proposing a bio-inspired control architecture that captures two key elements underlying human dexterity. The first is the progressive development of skilful control, often starting from – or involving – cyclic movements, based on trial-and-error learning processes and central pattern generators. The second element is the exploitation of a particular kinematic features of the human hand, i.e. the thumb opposition. The architecture is tested with two simulated robotic hands having different kinematic features and engaged in rotating spheres, cylinders, and cubes of different sizes. The results support the feasibility of the proposed approach and show the potential of the model to allow a better understanding of the control mechanisms and kinematic principles underlying human dexterity and make them transferable to anthropomorphic robotic hands

DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience