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...

Abstract. We present a new method for representing human movement compactly, in terms of a linear superimposition of simpler movements termed primitives. This method is a part of a larger research project aimed at modeling motor control and imitation using the notion of perceptuo-motor primitives, a basis set of coupled perceptual and motor routines. In our model, the perceptual system is biased by the set of motor behaviors the agent can execute, so it automatically classifies observed movements into its executable repertoire. In this paper, we describe a method for automatically deriving a set of primitives directly from human movement data. We used data from a psychophysical experiment on human imitation to derive a set of primitives, and then used those primitives as a basis for superposition and sequencing to reconstruct the original movements. We performed principal component analysis on segments from these data, resulting in a set of basis vectors. Next we clustered in the space of projections of segments onto the eigenvectors, to obtain a set of frequently used movements. To validate the approach experimentally, we used the movement obtained by expanding the cluster points in terms of the eigenvectors as a sequence of via points to control a humanoid dynamic simulation. We also developed an error metric to measure the effectiveness of the process. 1

A theoretical framework for grounding language is introduced that provides a computational path from sensing and motor action to words and speech acts. The approach combines concepts from semiotics and schema theory to develop a holistic approach to linguistic meaning. Schemas serve as structured beliefs that are grounded in an agent’s physical environment through a causal-predictive cycle of action and perception. Words and basic speech acts are interpreted in terms of grounded schemas. The framework reflects lessons learned from implementations of several language processing robots. It provides a basis for the analysis and design of situated, multimodal communication systems that straddle symbolic and non-symbolic realms.

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Abstract. Motor control is a complex problem and imitation is a powerful mechanism for acquiring new motor skills. In this paper, we describe perceptuo-motor primitives, a biologically-inspired notion for a basis set of perceptual and motor routines. Primitives serve as a vocabulary for classifying and imitating observed human movements, and are derived from the imitator’s motor repertoire. We describe a model of imitation based on such primitives and demonstrate the feasibility of the model in a constrained implementation. We present approximate motion reconstruction generated from visually captured data of typically imitated tasks taken from aerobics, dancing, and athletics. 1

This paper presents a computational model of real-time task-oriented dialogue skills. The architecture, termed Ymir, bridges between multimodal perception and multimodal action and supports the creation of autonomous computer characters that afford full-duplex, real-time face-to-face interaction with a human. Ymir has been prototyped in software, and a humanoid created, called Gandalf, capable of fluid multimodal dialogue. Ymir demonstrates several new ideas in the creation of communicative computer agents, including perceptual integration of multimodal events, distributed planning and decision making, an explicit handling of real-time, and layered input analysis and motor control with human characteristics. This paper describes the architecture and explains its main elements. Examples ofimplementation and performance are given, and the architectures limitations and possibilities are discussed.

To operate e#ectively in complex environments learning agents have to selectively ignore irrelevant details by forming useful abstractions. In this article we outline a formulation of abstraction for reinforcement learning approaches to stochastic sequential decision problems modeled as semiMarkov Decision Processes (SMDPs). Building on existing algebraic approaches, we propose the concept of SMDP homomorphism and argue that it provides a useful tool for a rigorous study of abstraction for SMDPs. We apply this framework to di#erent classes of abstractions that arise in hierarchical systems and discuss relativized options, a framework for compactly specifying a related family of temporally-extended actions. Additional details of this work are described in refs. [1, 2, 3].

Formal models of animal sensorimotor behavior can provide effective methods for generating robotic intelligence. In this article we describe how schema-theoretic models of the praying mantis derived from behavioral and neuroscientific data can be implemented on a hexapod robot equipped with a real-time color vision system. This implementation incorporates a wide range of behaviors, including obstacle avoidance, prey acquisition, predator avoidance, mating, and chantlitaxia behaviors that can provide guidance to neuroscientists, ethologists, and roboticists alike. The goals of this study are threefold: to provide an understanding and means by which fielded robotic systems are not competing with other agents that are more effective at their designated task; to permit them to be successful competitors within the ecological system and capable of displacing less efficient agents; and that they are ecologically sensitive so that agent-environment dynamics are well-modeled and as predictable ...

Schema Language (ASL) MissionLab (Mlab) Perceptual-Motor Weitzenfeld Arkin Cervantes Predictions Results Common Language Simulations Robot Experiments Biological Data Neural Simulation Language (NSL) Schema Model Figure 1: Interdisciplinary Interactions 1.1 Neuroscience and Ethology The biological group has been studying visuomotor coordination phenomena in amphibia (toad) and insects (praying mantis). These animals live within a three dimensional environment, rich in multiple modes of sensory signals, but their behavior is mainly guided by visual information. From an ecological point of view, these animals react to visual environmental domains of interaction which can be classified into two groups: moving and non-moving objects. Diverse stationary objects may influence the animal's next action which, in general, is directed to improve the animal 's survival chances. For example, frogs moved towards zones in the visual field where blue is preponderant, a situation that might be asso...

How can we build artificial agents that can autonomously explore and understand their environments? An immediate requirement for such an agent is to learn how its own sensory state corresponds to the external world properties: It needs to learn the semantics of its internal state (i.e., grounding). In principle, we as programmers can provide the agents with the required semantics, but this will compromise the autonomy of the agent. To overcome this problem, we may fall back on natural agents and see how they acquire meaning of their own sensory states, their neural firing patterns. We can learn a lot about what certain neural spikes mean by carefully controlling the input stimulus while observing how the neurons fire. However, neurons embedded in the brain do not have direct access to the outside stimuli, so such a stimulus-to-spike association may not be learnable at all. How then can the brain solve this problem? (We know it does.) We propose that motor interaction with the environment is necessary to overcome this conundrum. Further, we provide a simple yet powerful criterion, sensory invariance, for learning the meaning of sensory states. The basic idea is that a particular form of action sequence that maintains invariance of a sensory state will express the key property of the environmental stimulus that gave rise to the sensory state. Our experiments with a sensorimotor agent trained on natural images show that sensory invariance can indeed serve as a powerful objective for semantic grounding.