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Two Ways of Walking: Contrasting a Reflexive Neuro-Controller and a LIP-Based ZMP-Controller on the humanoid Robot ARMAR-4

Two Ways of Walking: Contrasting a Reflexive Neuro-Controlle...

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IEEE-RAS International Conference on humanoid Robots

作者： Cornelius Goldbeck Lukas Kaul Nikolaus Vahrenkamp Florentin Worgotter Tamim Asfour Jan-Matthias Braun High Performance Humanoid Technologies Lab Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Department of Computational Neuroscience at the Georg-August-University

ISBN: (纸本)9781509047192

Full-size humanoid robots are traditionally controlled with the Zero Moment Point (ZMP)-paradigm and simplified dynamics, a well established method which can be applied to balancing, walking, and whole-body manipulation tasks. For pure walking control, approaches like pattern generators and reflexes are employed, often on optimized hardware. Both controller groups are developed on different platforms and therefore can only be indirectly compared in terms of human likeness or energy efficiency. We present a reflex based neuro-controller with an underlying, simple hill-type muscle model on the extremely versatile humanoid robot ARMAR-4. We demonstrate the reflexive controller's flexible capabilities in terms of walking speed, step length, energy efficiency and inherent robustness against fall due to small slopes and pushes along the frontal axis. We contrast this controller with a Linearized Inverted Pendulum (LIP)-based ZMP-controller on the same platform. The promising results of this study show that even general humanoid robots can benefit from reflexive control schemes and encourage further investigation in this field.

关键词： humanoid robots walking Reflex Reflexive Controllers Energy efficiency pushing

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Accelerating synchronization of movement primitives: Dual-arm discrete-periodic motion of a humanoid robot

Accelerating synchronization of movement primitives: Dual-ar...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： A. Gams A. Ude J. Morimoto Humanoid and Cognitive Robotics Lab Biocybernetics and Robotics Jozef Stefan Institute Ljubljana Slovenia Department of Brain Robot Interface (BRI) ATR Computational Neuroscience Labs Kyoto Japan Department of Brain Robot Interface (BRI) ATR Computational Neuroscience Labs Kyoto Japan

Human-demonstrated motion transferred to a robotic platform often needs to be adapted to the current state of the environment or to modified task requirements. Adaptation, i. e. learning of a modified behavior, needs to be fast to enable quick utilization of the robot either in industry or in future household-assistant tasks. In this paper we show how to accelerate trajectory adaptation based on learning of coupling terms in the framework of dynamic movement primitives (DMPs). Our method applies ideas from feedback error learning to iterative learning control (ILC). By taking into account the actual physical constraints of the synchronous motion - through synchronization of both positions (or forces) and velocities - it is not only a more faithful representation of actual real-world processes, but it also accelerates the speed of convergence. To show the applicability of the approach in the framework of DMPs, we tested it on a formulation which encodes an initial discrete motion, followed by a periodic behavior, all in a single system. Modifications of the original discrete-periodic formulation now also allow for the use of DMP temporal scaling property. In the paper we also show how the DMP coupling can be implemented in joint space, whereas the measured forces and previous approaches always remained in the task space. We applied our approach to an example dual-arm synchronization task on Sarcos humanoid robot CB-i.

关键词： Robots Couplings Acceleration Synchronization Trajectory Aerospace electronics Dynamics

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Summary for the Dagstuhl Seminar Cognition, Control and Learning for Robot Manipulation in Human Environments

Summary for the Dagstuhl Seminar Cognition, Control and Lear...

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Cognition, Control and Learning for Robot Manipulation in Human Environments 2009

作者： Beetz, Michael Brock, Oliver Peters, Jan Cheng, Gordon Technische Universität München Computer Science Department IX Boltzmannstr. 3 Garching b MünchenD-85748 Germany Department of Computer Science University of Massachusetts Amherst 140 Governors Drive AmherstMA01003 United States Department for Empirical Inference Max-Planck-Institute for Biological Cybernetics Spemannstr. 38 Tübingen72076 Germany Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories 2-2-2 Hikaridai Seika-cho Soraku-gun Kyoto619-0288 Japan

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Active 3-D vision on a humanoid head

Active 3-D vision on a humanoid head

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International Conference on Advanced robotics (ICAR)

作者： Ales Ude Erhan Oztop Department of Automatics Biocybernetics and Robotics Jožef Stefan Institute Slovenia Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Japan ICORP Computational Brain project Japan Science and Technology Agency Japan

In this paper we describe and experimentally evaluate how to model, control, and use the capabilities of a humanoid visual system with foveated vision. We present a computational process that can be utilized to identify and update the parameters of the robot's eyes under motion, which enables the use of 3D vision on an active humanoid head. We also derive the formulas expressing the geometry of our foveated vision setup. Based on these results we can actively control the eye gaze towards the potential regions of interest and analyze these areas using foveation and 3D vision processing. Experimental results showing the accuracy of the system are provided. The system has been demonstrated to be sufficiently accurate to realize grasping using active 3D vision.

关键词： Cameras Robot vision systems Computer vision Eyes humanoid robots Image resolution Humans Head Machine vision robotics and automation

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Compliant control of a hydraulic humanoid joint

Compliant control of a hydraulic humanoid joint

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IEEE-RAS International Conference on humanoid Robots

作者： Darrin C. Bentivegna Christopher G. Atkeson Jung-Yup Kim Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Kyoto Japan

This paper presents an analysis of a hydraulic joint on a humanoid robot. Various controllers have been designed that allow the limb to have a range of characteristics such as being stiff or compliant.

关键词： Torque control Leg humanoid robots Hydraulic actuators Valves Legged locomotion Hip Servomechanisms Force sensors Velocity control

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Online gain switching algorithm for joint position control of a hydraulic humanoid robot

Online gain switching algorithm for joint position control o...

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IEEE-RAS International Conference on humanoid Robots

作者： Jung-Yup Kim Christopher G. Atkeson Jessica K. Hodgins Darrin C. Bentivegna Sung Ju Cho Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Kyoto Japan

This paper proposes a gain switching algorithm for joint position control of a hydraulic humanoid robot. Accurate position control of the lower body is one of the basic requirements for robust balance and walking control. Joint position control is more difficult for hydraulic robots than it is for electric robots because of a slower actuator time constant and the back-drivability of hydraulic joints. Backdrivability causes external forces and torques to have a large effect on the position of the joints. External ground reaction forces therefore prevent a simple proportional-derivative (PD) controller from realizing accurate joint position control. We propose a state feedback controller for joint position control of the lower body, define three modes of state feedback gains, and switch the gains according to the zero moment point (ZMP) using linear interpolation. The performance of the algorithm is evaluated with a dynamic simulation of a hydraulic humanoid.

关键词： Position control humanoid robots Joints PD control State feedback Switches Robust control Legged locomotion Hydraulic actuators Force control

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2007 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Kai Welke Erhan Oztop Gordon Cheng Rudiger Dillmann IAIM Institute of Computer Science and Engineering (CSE) Karlsruhe Germany JST Computational Brain Project ICORP Kawaguchi Saitama Japan Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Soraku-gun Kyoto Japan

A cognitive robot system has to acquire and efficiently store vast knowledge about the world it operates in. To cope with every day tasks, a robot needs to learn, classify and recognize a manifold of different objects. Our work focuses on an object representation scheme that allows storing perceived objects in a compact way. This will enable the system to store extensive information about the world and will ease complex recognition tasks. The human visual system deploys several mechanisms to reduce the amount of information. Our goal is to develop an artificial system that mimics these mechanisms to create representations that can be used in cognitive tasks. In particular, in this paper we will present an approach that exploits similarities among different views of objects. The proposed representation scheme allows for reduction of storage required for the representation of objects and preserves the information about the similarity among objects. This is achieved by selecting 'important views' of objects, depending on their stability. Furthermore, by extending the same approach to multiple objects, we are able to exploit similarities between objects to find a common representation and to further reduce the storage requirements.

关键词： Cognitive robotics humanoid robots Intelligent robots Humans Robot sensing systems Object recognition USA Councils Computer science Visual system

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Synthesizing goal-directed actions from a library of example movements

Synthesizing goal-directed actions from a library of example...

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IEEE-RAS International Conference on humanoid Robots

作者： Ales Ude Marcia Riley Bojan Nemec Andrej Kos Tamim Asfour Gordon Cheng Jožýef Stefan Institute Department of Automatics Biocybernetics and Robotics Ljubljana Slovenia Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Kyoto Japan College of Computing Georgia Institute of Technology Atlanta GA USA Institute of Computer Science and Engineering c/o Technologiefabrik University of Karlsruhe Karlsruhe Germany Japan Science and Technology Agency ICORP Computational Brain Project Kawaguchi Saitama Japan

We present a new learning framework for synthesizing goal-directed actions from example movements. The approach is based on the memorization of training data and locally weighted regression to compute suitable movements for a large range of situations. The proposed method avoids making specific assumptions about an adequate representation of the task. Instead, we use a general representation based on fifth order splines. The data used for learning comes either from the observation of events in the Cartesian space or from the actual movement execution on the robot. Thus it informs us about the appropriate motion in the example situations. We show that by applying locally weighted regression to such data, we can generate actions having proper dynamics to solve the given task. To test the validity of the approach, we present simulation results under various conditions as well as experiments on a real robot.

关键词： Libraries humanoid robots Physics computing Hidden Markov models Orbital robotics robotics and automation Cybernetics Laboratories Computer science Data engineering

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2006 IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Darrin C. Bentivegna Christopher G. Atkeson Gordon Cheng Robotics Institute Carnegie Mellon University Pittsburgh PA USA Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Kyoto Japan ICORP Japan Science and Technology Agency Kyoto Japan

This paper presents a case study of learning to select behavioral primitives and generate subgoals from observation and practice. Our approach uses local features to generalize across tasks and global features to learn from practice. We demonstrate this approach applied to the marble maze task. Our robot uses local features to initially learn primitive selection and subgoal generation policies from observing a teacher maneuver a marble through a maze. The robot then uses this information as it tries to traverse another maze, and refines the information during learning from practice

关键词： Intelligent robots Orbital robotics humanoid robots Educational robots Hardware Libraries USA Councils Laboratories Education Navigation

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Unconstrained Real-time Markerless Hand Tracking for humanoid Interaction

Unconstrained Real-time Markerless Hand Tracking for Humanoi...

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IEEE-RAS International Conference on humanoid Robots

作者： Thomas Gumpp Pedram Azad Kai Welke Erhan Oztop Rudiger Dillmann Gordon Cheng Department of Humanoid Robotics and Computational Neuroscience ATR Computational Neuroscience Laboratories Kawaguchi Saitama Japan Institute for Computer Science and Engineering University of Karlsruhe Germany Computational Brain Project JST ICORP Kawaguchi Saitama Japan

ISBN: (纸本)1424401992;142440200X

Markerless hand tracking of humans can be applied to a broad range of applications, in robotics, animation and natural human-computer interaction. Traditional motion capture and tracking methods involve the usage of devices such as a data glove, or marker points that are fixed and calibrated on the object to perform tracking. Markerless tracking is free from such needs, and therefore allows for more freedom in movement and spontaneous interaction. In this paper, we analyze how a hand tracking system, which reliably tracks arbitrary hand movements can be implemented. We explored a model based approach that uses particle filters for tracking. In this study we also determine the degree to which the inherent parallel properties of particle filter can be exploited to achieve the goal of real-time tracking. We present the effectiveness of the tracking system via the realtime control of a 20 degrees of freedom dexterous robotic hand

关键词： Robot kinematics Human robot interaction Hidden Markov models Particle filters Particle tracking Image databases humanoid robots Laboratories Computer science Application software

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