Showing papers on "Humanoid robot published in 2002"

Movement imitation with nonlinear dynamical systems in humanoid robots

Abstract: Presents an approach to movement planning, on-line trajectory modification, and imitation learning by representing movement plans based on a set of nonlinear differential equations with well-defined attractor dynamics. The resultant movement plan remains an autonomous set of nonlinear differential equations that forms a control policy (CP) which is robust to strong external perturbations and that can be modified on-line by additional perceptual variables. We evaluate the system with a humanoid robot simulation and an actual humanoid robot. Experiments are presented for the imitation of three types of movements: reaching movements with one arm, drawing movements of 2-D patterns, and tennis swings. Our results demonstrate (a) that multi-joint human movements can be encoded successfully by the CPs, (b) that a learned movement policy can readily be reused to produce robust trajectories towards different targets, (c) that a policy fitted for one particular target provides a good predictor of human reaching movements towards neighboring targets, and (d) that the parameter space which encodes a policy is suitable for measuring to which extent two trajectories are qualitatively similar.

Learning Attractor Landscapes for Learning Motor Primitives

Abstract: Many control problems take place in continuous state-action spaces, e.g., as in manipulator robotics, where the control objective is often defined as finding a desired trajectory that reaches a particular goal state. While reinforcement learning offers a theoretical framework to learn such control policies from scratch, its applicability to higher dimensional continuous state-action spaces remains rather limited to date. Instead of learning from scratch, in this paper we suggest to learn a desired complex control policy by transforming an existing simple canonical control policy. For this purpose, we represent canonical policies in terms of differential equations with well-defined attractor properties. By nonlinearly transforming the canonical attractor dynamics using techniques from nonparametric regression, almost arbitrary new nonlinear policies can be generated without losing the stability properties of the canonical system. We demonstrate our techniques in the context of learning a set of movement skills for a humanoid robot from demonstrations of a human teacher. Policies are acquired rapidly, and, due to the properties of well formulated differential equations, can be re-used and modified on-line under dynamic changes of the environment. The linear parameterization of nonparametric regression moreover lends itself to recognize and classify previously learned movement skills. Evaluations in simulations and on an actual 30 degree-of-freedom humanoid robot exemplify the feasibility and robustness of our approach.

Real-time humanoid motion generation through ZMP manipulation based on inverted pendulum control

Abstract: A humanoid robot is expected to be a rational form of machine to act in the real human environment and support people through interaction with them. Current humanoid robots, however, lack in adaptability, agility, or high-mobility enough to meet the expectations. In order to enhance high-mobility, the humanoid motion should be generated in real-time in accordance with the dynamics, which commonly requires a large amount of computation and has not been implemented so far. We have developed a real-time motion generation method that controls the center of gravity (COG) by indirect manipulation of the zero moment point (ZMP). The real-time response of the method provides humanoid robots with high-mobility. In the paper, the algorithm is presented. It consists of four parts, namely, the referential ZMP planning, the ZMP manipulation, the COG velocity decomposition to joint angles, and local control of joint angles. An advantage of the algorithm lies in its applicability to humanoids with a lot of degrees of freedom. The effectiveness of the proposed method is verified by computer simulations.

Dexterous anthropomorphic robot hand with distributed tactile sensor: Gifu hand II

Abstract: This paper presents an anthropomorphic robot hand, called the Gifu hand II, which has a thumb and four fingers, all the joints of which are driven by servomotors built into the fingers and the palm. The thumb has four joints with four-degrees-of-freedom (DOF), the other fingers have four joints with 3-DOF, and two axes of the joints near the palm cross orthogonally at one point, as is the case in the human hand. The Gifu hand II can be equipped with six-axes force sensor at each fingertip, and a developed distributed tactile sensor with 624 detecting points on its surface. The design concepts and specifications of the Gifu hand II, the basic characteristics of the tactile sensor, and the pressure distributions at the time of object grasping are described and discussed herein. Our results demonstrate that the Gifu hand II has a high potential to perform dexterous object manipulations like the human hand.

All robots are not created equal: the design and perception of humanoid robot heads

Abstract: This paper presents design research conducted as part of a larger project on human-robot interaction. The primary goal of this study was to come to an initial understanding of what features and dimensions of a humanoid robot's face most dramatically contribute to people's perception of its humanness. To answer this question we analyzed 48 robots and conducted surveys to measure people's perception of each robot's humanness. Through our research we found that the presence of certain features, the dimensions of the head, and the total number of facial features heavily influence the perception of humanness in robot heads. This paper presents our findings and initial guidelines for the design of humanoid robot heads.

Theory of Mind for a Humanoid Robot

Abstract: If we are to build human-like robots that can interact naturally with people, our robots must know not only about the properties of objects but also the properties of animate agents in the world. One of the fundamental social skills for humans is the attribution of beliefs, goals, and desires to other people. This set of skills has often been called a “theory of mind.” This paper presents the theories of Leslie (1994) and Baron-Cohen (1995) on the development of theory of mind in human children and discusses the potential application of both of these theories to building robots with similar capabilities. Initial implementation details and basic skills (such as finding faces and eyes and distinguishing animate from inanimate stimuli) are introduced. I further speculate on the usefulness of a robotic implementation in evaluating and comparing these two models.

Adapting human motion for the control of a humanoid robot

Abstract: Using the pre-recorded human motion and trajectory tracking, we can control the motion of a humanoid robot for free-space, upper body gestures. However, the number of degrees of freedom, range of joint motion, and achievable joint velocities of today's humanoid robots are far more limited than those of the average human subject. In this paper, we explore a set of techniques for limiting human motion of upper body gestures to that achievable by a Sarcos humanoid robot located at ATR. We assess the quality of the results by comparing the motion of the human actor to that of the robot, both visually and quantitatively.

A realtime pattern generator for biped walking

Abstract: For real-time walking control of a biped robot, we analyze the dynamics of a three-dimensional inverted pendulum whose motions are constrained onto an arbitrarily defined plane. This analysis leads us a simple linear dynamics, the Three-Dimensional Linear Inverted Pendulum Mode (3D-LIPM). Geometric nature of trajectories under the 3D-LIPM is discussed, and an algorithm for walking pattern generation is presented. Experimental results of real-time walking control of a 12-DOF biped robot HRP-2L using an input device such as a game pad are also shown.

Design of prototype humanoid robotics platform for HRP

Abstract: This paper presents a prototype humanoid robotics platform developed for HRP-2. HRP-2 is a new humanoid robotics platform, which we have been developing in phase two of HRP HRP is a humanoid robotics project, which has been launched by Ministry of Economy, Trade and Industry (METI) of Japan from 1998FY to 2002FY for five years. The ability of the biped locomotion of HRP-2 is improved so that HRP-2 can cope with rough terrain in the open air and can prevent the possible damages to a humanoid robot's own self in the event of tipping over. The ability of whole body motion of HRP-2 is also improved so that HRP-2 can get up by a humanoid robot's own self even tough HRP-2 tips over. In this paper, the mechanisms and specifications of developed prototype humanoid robotics platform, and its electrical system are introduced.

Elastic Strips: A Framework for Motion Generation in Human Environments

Abstract: Robotic applications are expanding into dynamic, unstructured, and populated environments. Mechanisms specifically designed to address the challenges arising in these environments, such as humanoid robots, exhibit high kinematic complexity. This creates the need for new algorithmic approaches to motion generation, capable of performing task execution and real-time obstacle avoidance in high-dimensional configuration spaces. The elastic strip framework presented in this paper enables the execution of a previously planned motion in a dynamic environment for robots with many degrees of freedom. To modify a motion in reaction to changes in the environment, real-time obstacle avoidance is combined with desired posture behavior. The modification of a motion can be performed in a task-consistent manner, leaving task execution unaffected by obstacle avoidance and posture behavior. The elastic strip framework also encompasses methods to suspend task behavior when its execution becomes inconsistent with other const...

The role of expressiveness and attention in human-robot interaction

Abstract: This paper presents the results of an experiment in human-robot social interaction. Its purpose was to measure the impact of certain features and behaviors on people's willingness to engage in a short interaction with a robot. The behaviors tested were the ability to convey expression with a humanoid face and the ability to indicate attention by turning towards the person that the robot is addressing. We hypothesized that these features were minimal requirements for effective social interaction between a human and a robot. We will discuss the results of the experiment and their implications for the design of socially interactive robots.

Dynamically-Stable Motion Planning for Humanoid Robots

Abstract: We present an approach to path planning for humanoid robots that computes dynamically-stable, collision-free trajectories from full-body posture goals. Given a geometric model of the environment and a statically-stable desired posture, we search the configuration space of the robot for a collision-free path that simultaneously satisfies dynamic balance constraints. We adapt existing randomized path planning techniques by imposing balance constraints on incremental search motions in order to maintain the overall dynamic stability of the final path. A dynamics filtering function that constrains the ZMP (zero moment point) trajectory is used as a post-processing step to transform statically-stable, collision-free paths into dynamically-stable, collision-free trajectories for the entire body. Although we have focused our experiments on biped robots with a humanoid shape, the method generally applies to any robot subject to balance constraints (legged or not). The algorithm is presented along with computed examples using both simulated and real humanoid robots.

Humanoid robots

Abstract: The future promises lots of robots in our everyday lives; some, perhaps many, of them could look and behave like people but only if being humanoid represents a technological advantage over their relatively utilitarian counterparts.

Recognition of Affective Communicative Intent in Robot-Directed Speech

Abstract: Human speech provides a natural and intuitive interface for both communicating with humanoid robots as well as for teaching them. In general, the acoustic pattern of speech contains three kinds of information: who the speaker is, what the speaker said, and how the speaker said it. This paper focuses on the question of recognizing affective communicative intent in robot-directed speech without looking into the linguistic content. We present an approach for recognizing four distinct prosodic patterns that communicate praise, prohibition, attention, and comfort to preverbal infants. These communicative intents are well matched to teaching a robot since praise, prohibition, and directing the robot's attention to relevant aspects of a task, could be used by a human instructor to intuitively facilitate the robot's learning process. We integrate this perceptual ability into our robot's “emotion” system, thereby allowing a human to directly manipulate the robot's affective state. This has a powerful organizing influence on the robot's behavior, and will ultimately be used to socially communicate affective reinforcement. Communicative efficacy has been tested with people very familiar with the robot as well as with naive subjects.

Development and evaluation of an interactive humanoid robot "Robovie"

Abstract: In this paper, we report about a new interaction-oriented robot, which communicates with humans and will participate in human society as our partner. For realizing such a robot, we have started a new collaborative work between cognitive science and robotics. In the way of robotics, we have developed a humanoid robot named "Robovie" that has enough physical expression ability. On the other hand, through cognitive experiments, we obtained important ideas about the robot's body property. To incorporate these ideas, we have developed software architecture and implemented autonomous interactive behaviors to the robot. Further, we have evaluated the robot's performance of the interactive behaviors through psychological experiments. The experiments reveal how humans recognize the robot.

Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning

Abstract: Locally weighted learning (LWL) is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about complex phenomena during autonomous adaptive control of robotic systems. This paper introduces several LWL algorithms that have been tested successfully in real-time learning of complex robot tasks. We discuss two major classes of LWL, memory-based LWL and purely incremental LWL that does not need to remember any data explicitly. In contrast to the traditional belief that LWL methods cannot work well in high-dimensional spaces, we provide new algorithms that have been tested on up to 90 dimensional learning problems. The applicability of our LWL algorithms is demonstrated in various robot learning examples, including the learning of devil-sticking, pole-balancing by a humanoid robot arm, and inverse-dynamics learning for a seven and a 30 degree-of-freedom robot. In all these examples, the application of our statistical neural networks techniques allowed either faster or more accurate acquisition of motor control than classical control engineering.

Learning rhythmic movements by demonstration using nonlinear oscillators

Abstract: ATR Human Information Science Laboratories, Kyoto, JapanEmail: ijspeert@usc.edu, jun@his.atr.co.jp, sschaal@usc.eduAbstractThis paper presents a new approach to the generationof rhythmic movement patterns with nonlinear dy-namical systems. Starting from a canonical limit cy-cle oscillator with well-de ned stability properties, wemodify the attractor landscape of the canonical sys-temby meansof statisticallearning methods toembedarbitrary smooth target patterns, however, withoutlosing the stability properties of the canonical sys-tem. In contrast to non-autonomous movement rep-resentations like splines, the learned pattern gener-ators remain autonomous dynamical systems whichrobustly cope with external perturbations that disruptthe time ow of the original pattern, and which canalso be modi ed on-line by additional perceptual vari-ables. A simple extension allows to cope with mul-tiple degrees-of-freedom (DOF) patterns, where allDOFs share the same fundamental frequency but,otherwise, can move in arbitrary phase and ampli-tude o sets to each other. We evaluate our meth-ods in learning from demonstration with an actual30 DOF humanoid robot. Figure-8 and drummingmovements are demonstrated by a human, recordedin joint angle space with an exoskeleton, and em-bedded in multi-dimensional rhythmic pattern gener-ators. The learned patterns can be used by the robotin various workspace locations and from arbitraryinitial conditions. Spatial and temporal invarianceof the pattern generators allow easy amplitude andspeed scaling without losing the qualitative signatureof a movement. This novel way of creating rhyth-mic patterns could tremendously facilitate rhythmicmovement generation, in particular in locomotion ofrobots and neural prosthetics in clinical applications.

Deriving action and behavior primitives from human motion data

Abstract: We address the problem of creating basis behaviors for modularizing humanoid robot control and representing human activity. These behaviors, called perceptual-motor primitives, serve as a substrate for linking a system's perception of human activities and the ability to perform those activities. We present a data-driven method for deriving perceptual-motor action and behavior primitives from human motion capture data. In order to find these primitives, we employ a spatio-temporal non-linear dimension reduction technique on a set of motion segments. From this transformation, motions representing the same action can be clustered and generalized. Further dimension reduction iterations are applied to derive extended-duration behaviors.

Whole-body cooperative balancing of humanoid robot using COG Jacobian

Abstract: Since humanoid robots have a number of degrees-of-freedom in general, a pattern-based approach of the motion control reduces its difficulty. It is necessary, however, to absorb and compensate disturbances in order to maintain the stability of robots in the real world. We developed a balancing method for humanoid robots with a little modification of predesigned motion trajectories. The method proposed has an advantage that it is allowed to choose any combination of joints as modified properties, so that it has enough flexibility, being applicable for various types of robots and motions. It consists of two phases; in the first phase, the referential COG displacement is decided in accordance with both the short-term and the long-term absorption of disturbances. And in the second phase, the COG is manipulated with the whole-body cooperation, using the COG Jacobian. We verified the validity of the method with some simulations.

A Fast Dynamically Equilibrated Walking Trajectory Generation Method of Humanoid Robot

Abstract: This paper describes a fast dynamically equilibrated trajectory generation method for a humanoid robot. From a given input motion and the desired ZMP trajectory, the algorithm generates a dynamically equilibrated trajectory using the relationship between the robot's center of gravity and the ZMP. Three key issues are denoted: 1) an enhanced ZMP constraint which enables the calculation of robot stability even if several limbs are contacting the environment, 2) a simplified robot model is introduced that represents the relationship between its center of gravity and ZMP, 3) a convergence method is adopted to eliminate approximation errors arising from the simplified model. Combining these three key issues together with online ZMP compensation method, humanoid robot H5 have succeeded to walk, step down and so on. Experimental results using humanoid robot H5 are described.

Imitating human dance motions through motion structure analysis

Abstract: This paper presents a method for importing human dance motion into humanoid robots through visual observation. The human motion data is acquired from a motion capture system consisting of 8 cameras and 8 PC clusters. Then the whole motion sequence is divided into motion elements and clustered into groups according to the correlation of end-effector trajectories. We call these segments 'motion primitives'. New dance motions are generated by concatenating these motion primitives. We are also trying to make a humanoid dance these original or generated motions using inverse-kinematics and dynamic balancing techniques.

Humanoid Robots in Waseda University—Hadaly-2 and WABIAN

Abstract: This paper describes two humanoid robots developed in the Humanoid Robotics Institute, Waseda University. Hadaly-2 is intended to realize information interaction with humans by integrating environmental recognition with vision, conversation capability (voice recognition, voice synthesis), and gesture behaviors. It also possesses physical interaction functions for direct contact with humans and behaviors that are gentle and safe for humans. WABIAN is a robot with a complete human configuration that is capable of walking on two legs and carrying things as with humans. Furthermore, it has functions for information interactions suite for uses at home.

Games Children with Autism Can Play with Robota, a Humanoid Robotic Doll

Abstract: This chapter discusses the potential use of a small, humanoid robotic doll called Robota in autism therapy. Robota was specifically designed for engaging children in imitative interaction games. This work is associated to the Aurora project where we study the potential therapeutic role of robots in autism therapy. This section provides the necessary background information on autism (18.1.1), and motivates the application of interactive technology in autism therapy (18.1.2). Section 18.1.3 discusses the important role of imitation and interaction games in the development of social skills. Section 18.2 introduces the Aurora project. Sections 18.3 and 18.4 briefly describe the humanoid doll Robota and its potential use in autism therapy. Observations from preliminary trials are discussed in Section 18.5 before section 18.6 concludes this chapter.

Toe joints that enhance bipedal and fullbody motion of humanoid robots

Abstract: Addresses the extension of a humanoid's action capability by attaching toe joints. The effectiveness of toe joints is discussed in three aspects. One is utilizing it to speed up the walking, another is using it to enable a humanoid to go up higher steps, and the other is using it to whole-body action in which knees are contacting the ground. Feet with toe joints are developed for humanoid 'H6'. An experiment of the wholebody action in which knees are contacting the ground is carried out to show the usefulness of toe joints for such actions. Then the walking pattern generation system is extended to use toe joints. Using this extended system maximum speed of knee joints can be reduced at the same walking speed, and 80% faster walking speed is achieved on humanoid 'H6'.

Polynomial design of the nonlinear dynamics for the brain-like information processing of whole body motion

Abstract: For the development of an intelligent robot with many degrees-of-freedom, the reduction of the whole body motion and the implementation of the brain-like information system is necessary. We propose a reduction method of the whole body motion based on singular value decomposition and a design method of the brain-like information processing system using a nonlinear dynamics network with polynomial configuration. By using the proposed method, we design the humanoid whole body motion that is caused by the input sensor signals.

Open architecture humanoid robotics platform

Abstract: This paper introduces an open architecture humanoid robotics platform (OpenHRP) on which various building blocks of humanoid robotics can be investigated. OpenHRP is a virtual humanoid robot platform with a compatible humanoid robot, and consists of a simulator of humanoid robots and motion control library for them which can also be applied to a compatible humanoid robot as it is. OpenHRP is expected to initiate the exploration of humanoid robotics on an open architecture software and hardware, due to the unification of the controllers and the examined consistency between the simulator and a real humanoid robot.

Online walking pattern generation for biped humanoid robot with trunk

Abstract: This paper describes an online method generating walking patterns for biped humanoid robots having a trunk. Depending on the walking command, the motion patterns of the lower-limbs are created and connected to the prewalking patterns smoothly in online. For the stability of the biped robots, the trunk and the waist motion is generated by a walking stabilization control that is based on the ZMP trajectory and the motion of the lower-limbs. Experimental tests of versatile biped walking on a plane surface are conducted using an auditory interface, and the validity of the online pattern generation method is verified.

UKEMI: falling motion control to minimize damage to biped humanoid robot

Abstract: This paper investigates a method to minimize damage to a humanoid robot when it falls over to the ground. The strategy involves controlling the attitude of the robot while it is falling over so that it lands on the ground at one of shock-absorbing parts of the robot. A simulation study has confirmed that the proposed algorithm can make the robot land at specified shock-absorbing parts.

Statistical Learning for Humanoid Robots

Abstract: The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techniques offer a possible way to aid controller design if insufficient analytical knowledge is available, and learning approaches seem mandatory when humanoid systems are supposed to become completely autonomous. While recent research in neural networks and statistical learning has focused mostly on learning from finite data sets without stringent constraints on computational efficiency, learning for humanoid robots requires a different setting, characterized by the need for real-time learning performance from an essentially infinite stream of incrementally arriving data. This paper demonstrates how even high-dimensional learning problems of this kind can successfully be dealt with by techniques from nonparametric regression and locally weighted learning. As an example, we describe the application of one of the most advanced of such algorithms, Locally Weighted Projection Regression (LWPR), to the on-line learning of three problems in humanoid motor control: the learning of inverse dynamics models for model-based control, the learning of inverse kinematics of redundant manipulators, and the learning of oculomotor reflexes. All these examples demonstrate fast, i.e., within seconds or minutes, learning convergence with highly accurate final peformance. We conclude that real-time learning for complex motor system like humanoid robots is possible with appropriately tailored algorithms, such that increasingly autonomous robots with massive learning abilities should be achievable in the near future.