Humanoid robot HRP-2
A development of humanoid robot HRP-2 is presented in this paper. HRP-2 is a humanoid robotics platform, which we developed in phase two of HRP. HRP was a humanoid robotics project, which had run by the 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 uneven surface, can walk at two third level of human speed, and can walk on a narrow path. 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 if HRP-2 tips over safely. In this paper, the appearance design, the mechanisms, the electrical systems, specifications, and features upgraded from its prototype are also introduced.
Humanoid robot HRP-3
In this paper, the development of humanoid robot HRP-3 is presented. HRP-3, which stands for Humanoid Robotics Platform-3, is a human-size humanoid robot developed as the succeeding model of HRP-2. One of features of HRP-3 is that its main mechanical and structural components are designed to prevent the penetration of dust or spray. Another is that its wrist and hand are newly designed to improve manipulation. Software for a humanoid robot in a real environment is also improved. We also include information on mechanical features of HRP-3 and together with the newly developed hand. Also included are the technologies implemented in HRP-3 prototype. Electrical features and some experimental results using HRP-3 are also presented.
Real-time humanoid motion generation through ZMP manipulation based on inverted pendulum control
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.
Resolved momentum control: humanoid motion planning based on the linear and angular momentum
We introduce a method to generate whole body motion of a humanoid robot such that the resulted total linear/angular momenta become specified values. First, we derive a linear equation, which gives to total momentum of a robot from its physical parameters, the base link speed and the joint speeds. Constraints between the legs and the environment are also considered. The whole body motion is calculated from a given momentum reference by using a pseudo-inverse of the inertia matrix. As examples, we generated the kicking and walking motions and tested on the actual humanoid robot HRP-2. This method, the resolved momentum control, gives us a unified framework to generate various maneuvers of humanoid robots.
Cognitive developmental robotics as a new paradigm for the design of humanoid robots
This paper proposes cognitive developmental robotics(CDR) as a new principle for the design of humanoid robots. This principle may provide ways of understanding human beings that go beyond the current level of explanation found in the natural and social sciences. Furthermore, a methodological emphasis on humanoid robots in the design of artificial creatures holds promise because they have many degrees of freedom and sense modalities and, thus, must face the challenges of scalability that are often side-stepped in simpler domains. We examine the potential of this new principle as well as issues that are likely to be important to CDR in the future. © 2001 Elsevier Science B.V. All rights reserved.
Development of a bipedal humanoid robot-control method of whole body cooperative dynamic biped walking
The authors have focused on the bipedal humanoid robot expected to play an active role in human living space, through studies on an anthropomorphic biped walking robot. As the first stage of developing a bipedal humanoid robot, the authors developed the human-size 35 active DOF bipedal humanoid robot "WABIAN" and the human-size 41 active DOF bipedal humanoid robot "WABIAN-R". The authors also proposed a basic control method of whole body cooperative dynamic biped walking that uses trunk or trunk-waist cooperative motion to compensate for three-axis (pitch, roll and yaw-axis) moment generated not only by the motion of the lower-limbs planned arbitrarily but by the time trajectory of the hands planned arbitrarily. Using these systems and the control method, normal biped walking (forward and backward), dynamic dance, waving arms and hip, dynamic carrying of a load using its arms, and trunk-waist cooperative dynamic walking are achieved.
Control of a humanoid robot by a noninvasive brain–computer interface in humans
We describe a brain-computer interface for controlling a humanoid robot directly using brain signals obtained non-invasively from the scalp through electroencephalography (EEG). EEG has previously been used for tasks such as controlling a cursor and spelling a word, but it has been regarded as an unlikely candidate for more complex forms of control owing to its low signal-to-noise ratio. Here we show that by leveraging advances in robotics, an interface based on EEG can be used to command a partially autonomous humanoid robot to perform complex tasks such as walking to specific locations and picking up desired objects. Visual feedback from the robot's cameras allows the user to select arbitrary objects in the environment for pick-up and transport to chosen locations. Results from a study involving nine users indicate that a command for the robot can be selected from four possible choices in 5 s with 95% accuracy. Our results demonstrate that an EEG-based brain-computer interface can be used for sophisticated robotic interaction with the environment, involving not only navigation as in previous applications but also manipulation and transport of objects.
Theory of Mind for a Humanoid Robot
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.
Incremental learning of gestures by imitation in a humanoid robot
We present an approach to teach incrementally human gestures to a humanoid robot. By using active teaching methods that puts the human teacher “in the loop” of the robot's learning, we show that the essential characteristics of a gesture can be efficiently transferred by interacting socially with the robot. In a first phase, the robot observes the user demonstrating the skill while wearing motion sensors. The motion of his/her two arms and head are recorded by the robot, projected in a latent space of motion and encoded probabilistically in a Gaussian Mixture Model (GMM). In a second phase, the user helps the robot refine its gesture by kinesthetic teaching, i.e. by grabbing and moving its arms throughout the movement to provide the appropriate scaffolds. To update the model of the gesture, we compare the performance of two incremental training procedures against a batch training procedure. We present experiments to show that different modalities can be combined efficiently to teach incrementally basketball officials' signals to a HOAP-3 humanoid robot.
Adapting human motion for the control of a humanoid robot
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.
Design of prototype humanoid robotics platform for HRP
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.
Dynamically-Stable Motion Planning for Humanoid Robots
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.
Compliant Control of Multicontact and Center-of-Mass Behaviors in Humanoid Robots
This paper presents a new methodology for the analysis and control of internal forces and center-of-mass (CoM) behavior, which are produced during multicontact interactions between humanoid robots and the environment. The approach leverages the virtual-linkage model that provides a physical representation of the internal and CoM resultant forces with respect to reaction forces on the supporting surfaces. A grasp/contact matrix describing the complex interactions between contact forces and CoM behavior is developed. Based on this model, a new torque-based approach for the control of internal forces is suggested and illustrated on the Asimo humanoid robot. The new controller is integrated into the framework for whole-body-prioritized multitasking, thus enabling the unified control of CoM maneuvers, operational tasks, and internal-force behavior. The grasp/contact matrix is also proposed to analyze and plan internal force and CoM control policies that comply with frictional properties of the links in contact.
Motion Planning for Humanoid Robots
Humanoid robotics hardware and control techniques have advanced rapidly during the last five years. Presently, several companies have announced the commercial availability of various humanoid robot prototypes. In order to improve the autonomy and overall functionality of these robots, reliable sensors, safety mechanisms, and general integrated software tools and techniques are needed. We believe that the development of practical motion planning algorithms and obstacle avoidance software for humanoid robots represents an important enabling technology. This paper gives an overview of some of our recent efforts to develop motion planning methods for humanoid robots for application tasks involving navigation, object grasping and manipulation, footstep placement, and dynamically-stable full-body motions. We show experimental results obtained by implementations running within a simulation environment as well as on actual humanoid robot hardware.
Centroidal dynamics of a humanoid robot
The center of mass (CoM) of a humanoid robot occupies a special place in its dynamics. As the location of its effective total mass, and consequently, the point of resultant action of gravity, the CoM is also the point where the robot’s aggregate linear momentum and angular momentum are naturally defined. The overarching purpose of this paper is to refocus our attention to centroidal dynamics: the dynamics of a humanoid robot projected at its CoM. In this paper we specifically study the properties, structure and computation schemes for the centroidal momentum matrix (CMM), which projects the generalized velocities of a humanoid robot to its spatial centroidal momentum. Through a transformation diagram we graphically show the relationship between this matrix and the well-known joint-space inertia matrix. We also introduce the new concept of “average spatial velocity” of the humanoid that encompasses both linear and angular components and results in a novel decomposition of the kinetic energy. Further, we develop a very efficient $$O(N)$$O(N) algorithm, expressed in a compact form using spatial notation, for computing the CMM, centroidal momentum, centroidal inertia, and average spatial velocity. Finally, as a practical use of centroidal dynamics we show that a momentum-based balance controller that directly employs the CMM can significantly reduce unnecessary trunk bending during balance maintenance against external disturbance.
A Seven-degrees-of-freedom Robot-arm Driven by Pneumatic Artificial Muscles for Humanoid Robots
Braided pneumatic artificial muscles, and in particular the better known type with a double helical braid usually called the McKibben muscle, seem to be at present the best means for motorizing robot-arms with artificial muscles. Their ability to develop high maximum force associated with lightness and a compact cylindrical shape, as well as their analogical behavior with natural skeletal muscle were very well emphasized in the 1980s by the development of the Bridgestone “soft robot” actuated by “rubbertuators”. Recent publications have presented ways for modeling McKibben artificial muscle as well as controlling its highly non-linear dynamic behavior. However, fewer studies have concentrated on analyzing the integration of artificial muscles with robot-arm architectures since the first Bridgestone prototypes were designed. In this paper we present the design of a 7R anthropomorphic robot-arm entirely actuated by antagonistic McKibben artificial muscle pairs. The validation of the robot-arm architecture was performed in a teleoperation mode.
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