Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions.

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ing away from the specific coding of the spinal fields, the examples from neurobiology provide the framework for a motor control system based on a small number of additive primitives (or basis behaviors) sufficient for a rich output movement repertoire. Our previous work (Matari'c 1995, Matari'c 1997), inspired by the same biological results, has successfully applied the idea of basis behaviors to control of mobile robots 6 by fitting it directly into the modular behavior-based control paradigm. Applictions of schema theory (Arbib 1992) to behavior-based mobile robots (Arkin 1987) have employed a similar notion of composable behaviors, stemming from foundations in neuroscience (Arbib 1981, Arbib 1989). The idea of using such primitives for articulator control has been recently studied in robotics. Williamson (1996) and Marjanovi'c, Scassellati & Williamson (1996) developed a 6 DOF (degrees of freedom) robot arm controller. While in the biological and mobile robotics work primitives c...

This paper is concerned with the evaluation of a model of human imitation of arm movements. The model consists of a hierarchy of articial neural networks, which are abstractions of brain regions involved in visuo-motor control. These are the spinal cord, the primary and pre-motor cortexes (M1 & PM), the cerebellum, and the temporal cortex. A biomechanical simulation is developed which models the muscles and the complete dynamics of a 37 degree of freedom humanoid. Input to the model are data from human arm movements recorded using video and marker-based tracking systems. The model's performance is evaluated for reproducing reaching movements and oscillatory movements of the two arms. Results show a high qualitative and quantitative agreement with human data. In particular, the model reproduces the well known features of reaching movements in humans, namely the bellshaped curves for the velocity and quasi-linear hand trajectories. Finally, the model's performance is compared to that o...

The emerging viewpoint of embodied cognition holds that cognitive processes are deeply rooted in the body’s interactions with the world. This position actually houses a number of distinct claims, some of which are more controversial than others. This paper distinguishes and evaluates the following six claims: 1) cognition is situated; 2) cognition is time-pressured; 3) we off-load cognitive work onto the environment; 4) the environment is part of the cognitive system; 5) cognition is for action; 6) off-line cognition is body-based. Of these, the first three and the fifth appear to be at least partially true, and their usefulness is best evaluated in terms of the range of their applicability. The fourth claim, I argue, is deeply problematic. The sixth claim has received the least attention in the literature on embodied cognition, but it may in fact be the best documented and most powerful of the six claims.

Visual information is processed in posterior parietal cortex for the hypothesized purpose of extracting a variety of affordances for the generation of motor behavior. The term affordance is used to mean that visual cues are mapped directly to parameters that are relevant for motor interaction. In this paper, we present a model of the cortical involvement in grasping, which focuses on the interaction between anterior intra-parietal area (AIP) and premotor area F5. The model represents the role of other intra-parietal areas, working in concert with inferotemporal cortex and F5, to provide AIP with a full range of information from which affordances may be derived. The model also suggests how task information and other constraints may resolve the action opportunities provided by multiple affordances. Our model demonstrates that posterior parietal cortex is not only itself a network of interacting subsystems, but itself functions through a pattern of "cooperative computation" with a multipl...

This paper describes the use of task primitives in robot learning from observation. A framework has been developed that uses observed data to initially learn a task and then the agent goes on to increase its performance through repeated task performance (learning from practice). Data that is collected while a human performs a task is parsed into small parts of the task called primitives. Modules are created for each primitive that encode the movements required during the performance of the primitive, and when and where the primitives are performed. The feasibility of this method is currently being tested with agents that learn to play a virtual and an actual air hockey game. 1

This article presents a biologically inspired model for motor skills imitation. The model is composed of modules whose functinalities are inspired by corresponding brain regions responsible for the control of movement in primates. These modules are high-level abstractions of the spinal cord, the primary and premotor cortexes (M1 and PM), the cerebellum, and the temporal cortex. Each module is modeled at a connectionist level. Neurons in PM respond both to visual observation of movements and to corresponding motor commands produced by the cerebellum. As such, they give an abstract representation of mirror neurons. Learning of new combinations of movements is done in PM and in the cerebellum. Premotor cortexes and cerebellum are modeled by the DRAMA neural architecture which allows learning of times series and of spatio-temporal invariance in multimodal inputs. The model is implemented in a mechanical simulation of two humanoid avatars, the imitator and the imitatee. Three types of sequences learning are presented: (1) learning of repetitive patterns of arm and leg movements; (2) learning of oscillatory movements of shoulders and elbows, using video data of a human demonstration; 3) learning of precise movements of the extremities for grasp and reach

According to the motor theories of perception, the motor systems of an observer are actively involved in the perception of actions when these are performed by a demonstrator. In this paper we review our computational architecture, HAMMER (Hierarchical Attentive Multiple Models for Execution and Recognition), where the motor control systems of a robot are organised in a hierarchical, distributed manner, and can be used in the dual role of (a) competitively selecting and executing an action, and (b) perceiving it when perfomed by a demonstrator. We subsequently demonstrate that such arrangement can provide a principled method for the top-down control of attention during action perception, resulting in significant performance gains. We assess these performance gains under a variety of resource allocation strategies.

The cortical motor system has been classically considered as the unitary, output stage of the brain processing of sensory information. According to this idea, the motor cortex � the acting brain � receives the result of the perceptual processing Ž visual, acoustical, tactile, etc.. elaborated by the ‘associative cortex’. During the last two decades this perspective has been challenged by a series of anatomical, hodological, and neurophysiological data. This converging evidence delineates a dramatically changed picture. Far from being unitary, the cortical motor system appears to be constituted by a constellation of distinct areas, each of those endowed with specific functional properties and linked by reciprocal connections with distinct sectors of the parietal cortex. Furthermore, several ‘motor ’ neurons in addition to their motor discharge, are also activated by somatosensory and visual stimulation Ž somatomotor and visuomotor neurons.. In the present paper we will discuss the functional properties of those sensorimotor neurons located in the ventral part of the monkey premotor cortex. On the basis of electrophysiological data, we will propose that the apparent parodox stemming from the coexistence within the same neuron of motor and sensory properties can be solved by postulating that the motor system not only executes actions but also internally represents them in terms of ‘motor ideas’. These motor ideas may provide the neurobiological basis for space representation, understanding of actions made by others and, possibly, semantic categorization of objects.