Gabriel Gómez

Exploiting body dynamics for controlling a running quadruped robot

Exploiting the body dynamics to control the behavior of robots is one of the most challenging issues, because the use of body dynamics has a significant potential in order to enhance both complexity of the robot design and the speed of movement. In this paper, we explore the control strategy of rapid four-legged locomotion by exploiting the intrinsic body dynamics. Based on the fact that a simple model of four-legged robot is known to exhibit interesting locomotion behavior, this paper analyzes the characteristics of the dynamic locomotion for the purpose of the locomotion control. The results from a series of running experiments with a robot show that, by exploiting the unique characteristics induced by the body dynamics, the forward velocity can be controlled by using a very simple method, in which only one control parameter is required. Furthermore it is also shown that a few of such different control parameters exist, each of them can control the forward velocity. Interestingly, with these parameters, the robot exhibits qualitatively different behavior during the locomotion, which could lead to our comprehensive understanding toward the behavioral diversity of adaptive robotic systems

Haptic discrimination of material properties by a robotic hand

One of the key aspects of understanding human intelligence is to investigate how humans interact with their environment. Performing articulated movement and manipulation tasks in a constantly changing environment, have proven more difficult than expected. The difficulties of robot manipulation are in part due to the unbalanced relation between vision and haptic sensing. Most robots are equipped with high resolution cameras, which images are processed by well established computer vision algorithms such as color segmentation, motion detection, edge detection, etc. However, the majority of robots have very limited haptic capabilities. This paper presents our attempt to overcome this difficulties by: (a) using a tendon driven robotic hand with rich dynamical movements and (b) covering the hand with a set of haptic sensors on the palm and the fingertips, the sensors are based on a simplified version of an artificial skin with strain gauges and PVDF (polyvinylidene fluoride) films. The results show that if the robotic hand actively explores different objects using the exploratory procedures: tapping and squeezing, material properties such as hardness and texture can be used to discriminate haptically between different objects.

Interacting with the real world: design principles for intelligent systems

The last two decades in the field of artificial intelligence have clearly shown that true intelligence always requires the interaction of an agent with a real physical and social environment. The concept of embodiment that has been introduced to designate the modern approach to designing intelligence has far-reaching implications. Rather than studying computation alone, we must consider the interplay between morphology, materials, brain (control), and the environment. A number of case studies are presented, and it is demonstrated how artificial evolution and morphogenesis can be used to systematically investigate this interplay. Taking these ideas into account requires entirely novel ways of thinking, and often leads to surprising results.

Quantifying patterns of agent-environment interaction

This article explores the assumption that a deeper (quantitative) understanding of the information-theoretic implications of sensory-motor coordination can help endow robots not only with better sensory morphologies, but also with better exploration strategies. Speciflcally, we investigate by means of statistical and informationtheoretic measures, to what extent sensory-motor coordinated activity can generate and structure information in the sensory channels of a simulated agent interacting with its surrounding environment. The results show how the usage of correlation, entropy, and mutual information can be employed (a) to segment an observed behavior into distinct behavioral states, (b) to analyze the informational relationship between the difierent components of the sensory-motor apparatus, and (c) to identify patterns (or flngerprints) in the sensory-motor interaction between the agent and its local environment.