Multimodal Imitation using Self-learned Sensorimotor Representations

Although many tasks intrinsically involve multiple modalities, often only data from a single modality are used to improve complex robots acquisition of new skills. We present a method to equip robots with multimodal learning skills to achieve multimodal imitation on-the-fly on multiple concurrent task spaces, including vision, touch and proprioception, only using self-learned multimodal sensorimotor relations, without the need of solving inverse kinematic problems or explicit analytical models formulation. We evaluate the proposed method on a humanoid iCub robot learning to interact with a piano keyboard and imitating a human demonstration. Since no assumptions are made on the kinematic structure of the robot, the method can be also applied to different robotic platforms

Computational and Robotic Models of Early Language Development: A Review

We review computational and robotics models of early language learning and development. We first explain why and how these models are used to understand better how children learn language. We argue that they provide concrete theories of language learning as a complex dynamic system, complementing traditional methods in psychology and linguistics. We review different modeling formalisms, grounded in techniques from machine learning and artificial intelligence such as Bayesian and neural network approaches. We then discuss their role in understanding several key mechanisms of language development: cross-situational statistical learning, embodiment, situated social interaction, intrinsically motivated learning, and cultural evolution. We conclude by discussing future challenges for research, including modeling of large-scale empirical data about language acquisition in real-world environments. Keywords: Early language learning, Computational and robotic models, machine learning, development, embodiment, social interaction, intrinsic motivation, self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J. Horst and J. von Koss Torkildsen, Routledg

Towards Anchoring Self-Learned Representations to Those of Other Agents

In the future, robots will support humans in their every day activities. One particular challenge that robots will face is understanding and reasoning about the actions of other agents in order to cooperate effectively with humans. We propose to tackle this using a developmental framework, where the robot incrementally acquires knowledge, and in particular 1) self-learns a mapping between motor commands and sensory consequences, 2) rapidly acquires primitives and complex actions by verbal descriptions and instructions from a human partner, 3) discovers correspondences between the robots body and other articulated objects and agents, and 4) employs these correspondences to transfer the knowledge acquired from the robots point of view to the viewpoint of the other agent. We show that our approach requires very little a-priori knowledge to achieve imitation learning, to find correspondent body parts of humans, and allows taking the perspective of another agent. This represents a step towards the emergence of a mirror neuron like system based on self-learned representations

Imitation and Mirror Systems in Robots through Deep Modality Blending Networks

Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, can be conditioned on any desired sensory/motor value at any time-step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in parallel avoiding accumulation of prediction errors. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect based imitation behavior. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users

Sensorimotor processing in speech examined in automatic imitation tasks

The origin of humans’ imitative capacity to quickly map observed actions onto their motor repertoire has been the source of much debate in cognitive psychology. Past research has provided a comprehensive account of how sensorimotor associative experience forges and modulates the imitative capacity underlying familiar, visually transparent manual gestures. Yet, little is known about whether the same associative mechanism is also involved in imitation of visually opaque orofacial movements or novel actions that were not part of the observers’ motor repertoire. This thesis aims to establish the role of sensorimotor experience in modulating the imitative capacity underlying communicative orofacial movements, namely speech actions, that are either familiar or novel to perceivers. Chapter 3 first establishes that automatic imitation of speech occurs due to perception- induced motor activation and thus can be used as a behavioural measure to index the imitative capacity underlying speech. Chapter 4 demonstrates that the flexibility observed for the imitative capacity underlying manual gestures extends to the imitative capacity underlying visually perceived speech actions, suggesting that the associative mechanism is also involved in imitation of visually opaque orofacial movements. Chapter 5 further shows that sensorimotor experience with novel speech actions modulates the imitative capacity underlying both novel and familiar speech actions produced using the same articulators. Thus, findings from Chapter 5 suggest that the associative mechanism is also involved in imitation of novel actions and that experience-induced modification probably occurs at the feature level in the perception-production link presumably underlying the imitative capacity. Results are discussed with respect to previous imitation research and more general action-perception research in cognitive and experimental psychology, sensorimotor interaction studies in speech science, and native versus non-native processing in second language research. Overall, it is concluded that the development of speech imitation follows the same basic associative learning rules as the development of imitation in other effector systems

Being-in-the-world-with: Presence Meets Social And Cognitive Neuroscience

In this chapter we will discuss the concepts of “presence” (Inner Presence) and “social presence” (Co-presence) within a cognitive and ecological perspective. Specifically, we claim that the concepts of “presence” and “social presence” are the possible links between self, action, communication and culture. In the first section we will provide a capsule view of Heidegger’s work by examining the two main features of the Heideggerian concept of “being”: spatiality and “being with”. We argue that different visions from social and cognitive sciences – Situated Cognition, Embodied Cognition, Enactive Approach, Situated Simulation, Covert Imitation - and discoveries from neuroscience – Mirror and Canonical Neurons - have many contact points with this view. In particular, these data suggest that our conceptual system dynamically produces contextualized representations (simulations) that support grounded action in different situations. This is allowed by a common coding – the motor code – shared by perception, action and concepts. This common coding also allows the subject for natively recognizing actions done by other selves within the phenomenological contents. In this picture we argue that the role of presence and social presence is to allow the process of self-identification through the separation between “self” and “other,” and between “internal” and “external”. Finally, implications of this position for communication and media studies are discussed by way of conclusion