Se-Hyoung Cho

Laser-Based Kinematic Calibration of Robot Manipulator Using Differential Kinematics

This paper proposes a novel systematic technique to estimate entire kinematic parameter errors of robot manipulator. Small errors always exist in link length and link twist for physical manipulators, which affect the precision in kinematic equations leading to calculate wrong joint angle values in inverse kinematic equations. In order to solve these problems, the proposed technique employs a structured laser module (SLM), a stationary camera, the Jacobian matrices, and an extended Kalman filter (EKF). The SLM is attached to the end-effector of the manipulator arm and the stationary camera is used to determine an accurate position where the laser comes out. Variances between actual and measured positions of laser beams are represented by the Jacobian matrices formulated from differential kinematics. Then, the EKF is used to estimate kinematic parameters. Effectiveness of the proposed technique is verified with 7 DOF humanoid manipulator arm by computer simulation and 4 DOF manipulator by actual experiment.

Nonlinear Slip Dynamics for an Omniwheel Mobile Robot Platform

This study investigates the nonlinear dynamics of traction for an omniwheel mobile robot platform. A nonlinear slip model is incorporated into the dynamics of the system and the resulting equations of motion are derived using Euler-Lagrange formulation. These are additionally transformed into slip-space, where the dynamical equations lend themselves to a convenient analysis of the slip dynamics. The conventional assumptions for ideal rolling are also explored and a reduced expression for the nonlinear dynamics is generated for such situations. Preliminary explorations toward a comprehensive analysis of the dynamics for omniwheel platforms under various control schemes is also initiated.

A Cognitive Control Architecture for an Artificial Creature using Episodic Memory

This paper describes a new cognitive control architecture incorporating episodic memory for the artificial creature Rity, the software robot component of the Ubibot, the ubiquitous robot system. The episodic memory is defined as a scalable structure that stores episodic perceptual snapshots as Ritys experience grows. The system also utilizes a temporally variant spatial map to store spatial information and a higher level procedural memory using finite state machines. The system is designed to enable Rity to be cognitive in its approach to task selection through the dual process of experiential and spatial learning. This is brought about through a multi-agent strategy based on six principal modules: perception module, internal state module, behavior selection module, interactive learning module, memory module, and motor module, to control its behavior considering its internal state. Experiments on completion of task and maintenance of ideal internal state are described. The results show that the artificial creature possesses the ability to improve its performance as its experience grows

Two-Layered Confabulation Architecture for an Artificial Creature's Behavior Selection

This paper proposes a novel two-layered confabulation architecture for an artificial creature to select a proper behavior considering the internally generated will and the context of the external environment consecutively. The architecture is composed of seven main modules for processing perception, internal state, context, memory, learning, behavior selection, and actuation. The two-layered confabulation in a behavior module is processed by a will-based confabulation and a context-based confabulation consecutively by referring to confabulation probabilities in a memory module. An arbiter in the behavior module chooses a proper behavior among the suggested ones from the two confabulations, which is to be put into an action. To demonstrate the effectiveness of the proposed architecture, experiments are carried out for an artificial creature, implemented in the 3-D virtual environment, which behaves as per its will considering the context in the environment.

Fuzzy integral-based composite facial expression generation for a robotic head

Conventional methods produced composite facial expressions by interpolating the representative facial expressions under the assumption that the transitions between facial expressions are linear. Considering the nonlinear property, this paper proposes a fuzzy integral-based method for generating composite facial expressions. Fuzzy measures represent the relationship among emotions and the partial evaluation of current emotion state is obtained from a predefined error function of the ideal basic emotion states and the current emotion state. Fuzzy integral of the partial evaluation with respect to fuzzy measures is employed to globally evaluate the current emotion state for generating composite facial expressions. The effectiveness of the proposed method for generating composite facial expressions is demonstrated through the experiments with a robotic head with 19 degrees of freedom, developed in RIT Laboratory, KAIST.

Software Robot in a PDA for Human Interaction and Seamless Service

In this paper, a new architecture is proposed for the efficient human interaction with software robot (Sobot) in a PDA and the Sobot transmission between PDAs. Sobot can move to any mobile device through a wireless communication network. This ability is required to follow its user by moving itself to a device in his/her new location. Sobot as an artificial creature, has genetic code which is a set of computerized codes representing the personality. It has an internal state which consists of motivation, homeostasis, and emotion. The data set of genetic code and current internal state are transmitted through the network when it moves to the other device. A main server manages IP addresses of registered devices, Sobots data set, and Sobots location for the transmission. To demonstrate the effectiveness of the proposed scheme, Sobot is implemented in windows mobile environment of PDA such that it can interact with a human being and move to the other mobile device without spatial limitation.

Approach to integrate episodic memory into cogency-based behavior planner for robots

This paper proposes a novel scheme of integrating episodic memory into semantic memory based task planner. Task planners have taken an important role in AI research along with semantic memory to better perform tasks for robots. Episodic memory memorizes and retrieves temporal sequence of situated behaviors by which temporal relationship between behaviors can be defined. None of any research, however, has implemented it into their work for task planning. By introducing episodic memory into task planner, the temporal causal relationship between situated behaviors, which are stored in semantic memory, is taken into consideration. The integrated architecture proves its effectiveness by notably reducing the number of nodes traversed in finding solutions. Robots can reduce time complexity in solving given problems by retrieving previous memories. Deep Adaptive Resonance Theory (Deep-ART) neural model and cogency-based hierarchical behavior planner are used for the episodic memory and the task planner, respectively. Cogency-based hierarchical behavior planner proves its capability of solving given problems in experiment with humanoid robot Mybot, and Deep-ART is augmented to the planner and tested in simulations. Therefore, the contribution of this approach lies on developing a framework which takes advantage of implementing episodic memory and planner in one place.

Facial Expression Generation Using Fuzzy Integral for Robotic Heads

This paper proposes a generation method of facial expressions using fuzzy measure and fuzzy integral for robotic heads. Human’s emotion state can be represented by a fuzzy measure which can effectively deal with ambiguity. Because facial expressions are usually ambiguous such that it is difficult to discern emotions and assign a sharp boundary to each emotion. In this method, users can adjust the personality of robot by assignign fuzzy measure to every set of emotions. The partial evaluation values of the current emotion state are obtained from a difference between the ideal basic emotion states and the current emotion state. The Choquet integral of the partial evaluation values with respect to the fuzzy measure is calculated to decide which emotion should occur. The effectiveness of the proposed method is demonstrated through computer simulations and experiments with a robotic head with 19 degrees of freedom, developed in RIT Lab., KAIST.

Behavior Selection and Memory-based Learning for Artificial Creature Using Two-layered Confabulation

Confabulations, where millions of items of relevant knowledge are applied in parallel in the human brain, are typically employed in thinking. This paper proposes a novel behavior selection architecture and memory-based learning method for an artificial creature based on two-layered confabulation. A behavior is selected by considering both internally generated will and the context of the external environment. Proposed behavior selection using a confabulation scheme is a parallel process in which a number of behaviors are considered simultaneously. An arbitration mechanism is employed to choose a proper behavior which is to be put into an action. Also memory-based behavior learning is proposed, w here the memory has the selection probabilities of behaviors based on will and context. The learning module updates the contents of memory according to the user-given reward or penalty signal. To demonstrate the effectiveness of the proposed scheme, an artificial creature is implemented in the 3D virtual environment such that it can interact with a human being considering its will and context.

Cogent confabulation-based hierarchical behavior planner for task performance

This paper proposes a novel hierarchical behavior planner with a multi-layered confabulation based behavior selection structure for robots to perform tasks. The proposed planner integrates a STRIPS based behavior selection approach and cogent confabulation approach. The STRIPS based behavior selection approach is a goal tree search that induces goal-oriented sequences of behaviors, while the cogent confabulation approach is based on conditional probabilities between input symbols and target behaviors, aims to model human thinking mechanism. Our planner is applied with a set of behaviors defined in a multi-layered structure to show that it can plan a hierarchical sequences of behaviors to perform given tasks. The effectiveness and applicability of the proposed scheme is demonstrated through the experiments with the robot Mybot, developed in the Robot Intelligence Technology Lab. at KAIST.