Showing papers on "Humanoid robot published in 2011"

Policy search for motor primitives in robotics

Abstract: Many motor skills in humanoid robotics can be learned using parametrized motor primitives. While successful applications to date have been achieved with imitation learning, most of the interesting motor learning problems are high-dimensional reinforcement learning problems. These problems are often beyond the reach of current reinforcement learning methods. In this paper, we study parametrized policy search methods and apply these to benchmark problems of motor primitive learning in robotics. We show that many well-known parametrized policy search methods can be derived from a general, common framework. This framework yields both policy gradient methods and expectation-maximization (EM) inspired algorithms. We introduce a novel EM-inspired algorithm for policy learning that is particularly well-suited for dynamical system motor primitives. We compare this algorithm, both in simulation and on a real robot, to several well-known parametrized policy search methods such as episodic REINFORCE, `Vanilla' Policy Gradients with optimal baselines, episodic Natural Actor Critic, and episodic Reward-Weighted Regression. We show that the proposed method out-performs them on an empirical benchmark of learning dynamical system motor primitives both in simulation and on a real robot. We apply it in the context of motor learning and show that it can learn a complex Ball-in-a-Cup task on a real Barrett WAM™ robot arm.

Robonaut 2 - The first humanoid robot in space

Abstract: NASA and General Motors have developed the second generation Robonaut, Robonaut 2 or R2, and it is scheduled to arrive on the International Space Station in early 2011 and undergo initial testing by mid-year. This state of the art, dexterous, anthropomorphic robotic torso has significant technical improvements over its predecessor making it a far more valuable tool for astronauts. Upgrades include: increased force sensing, greater range of motion, higher bandwidth, and improved dexterity. R2's integrated mechatronic design results in a more compact and robust distributed control system with a fraction of the wiring of the original Robonaut. Modularity is prevalent throughout the hardware and software along with innovative and layered approaches for sensing and control. The most important aspects of the Robonaut philosophy are clearly present in this latest model's ability to allow comfortable human interaction and in its design to perform significant work using the same hardware and interfaces used by people. The following describes the mechanisms, integrated electronics, control strategies, and user interface that make R2 a promising addition to the Space Station and other environments where humanoid robots can assist people.

Kinematic Control of Redundant Manipulators: Generalizing the Task-Priority Framework to Inequality Task

Abstract: Redundant mechanical systems like humanoid robots are designed to fulfill multiple tasks at a time. A task, in velocity-resolved inverse kinematics, is a desired value for a function of the robot configuration that can be regulated with an ordinary differential equation (ODE). When facing simultaneous tasks, the corresponding equations can be grouped in a single system or, better, sorted in priority and solved each in the solutions set of higher priority tasks. This elegant framework for hierarchical task regulation has been implemented as a sequence of least-squares problems. Its limitation lies in the handling of inequality constraints, which are usually transformed into more restrictive equality constraints through potential fields. In this paper, we propose a new prioritized task-regulation framework based on a sequence of quadratic programs (QP) that removes the limitation. At the basis of the proposed algorithm, there is a study of the optimal sets resulting from the sequence of QPs. The algorithm is implemented and illustrated in simulation on the humanoid robot HRP-2.

Methods and Technologies for the Implementation of Large-Scale Robot Tactile Sensors

Abstract: Even though the sense of touch is crucial for humans, most humanoid robots lack tactile sensing. While a large number of sensing technologies exist, it is not trivial to incorporate them into a robot. We have developed a compliant “skin” for humanoids that integrates a distributed pressure sensor based on capacitive technology. The skin is modular and can be deployed on nonflat surfaces. Each module scans locally a limited number of tactile-sensing elements and sends the data through a serial bus. This is a critical advantage as it reduces the number of wires. The resulting system is compact and has been successfully integrated into three different humanoid robots. We have performed tests that show that the sensor has favorable characteristics and implemented algorithms to compensate the hysteresis and drift of the sensor. Experiments with the humanoid robot iCub prove that the sensors can be used to grasp unmodeled, fragile objects.

The Benefits of Interactions with Physically Present Robots over Video-Displayed Agents

Abstract: This paper explores how a robot’s physical presence affects human judgments of the robot as a social partner. For this experiment, participants collaborated on simple book-moving tasks with a humanoid robot that was either physically present or displayed via a live video feed. Multiple tasks individually examined the following aspects of social interaction: greetings, cooperation, trust, and personal space. Participants readily greeted and cooperated with the robot whether present physically or in live video display. However, participants were more likely both to fulfill an unusual request and to afford greater personal space to the robot when it was physically present, than when it was shown on live video. The same was true when the live video displayed robot’s gestures were augmented with disambiguating 3-D information. Questionnaire data support these behavioral findings and also show that participants had an overall more positive interaction with the physically present robot.

Capturability-Based Analysis and Control of Legged Locomotion

Abstract: This three-part paper discusses the analysis and control of legged locomotion in terms of N-step capturability: the ability of a legged system to come to a stop without falling by taking N or fewer steps. We consider this ability to be crucial to legged locomotion and a useful, yet not overly restrictive criterion for stability. Part 1 introduces the theoretical framework for assessing N-step capturability. Formal definitions of N-step capturability and related terms are given, and general disturbance robustness metrics based on capturability are proposed. Part 2 uses the theoretical framework developed in the current part to analyze N-step capturability for three simple gait models. Part 3 describes how the results for the simple models were used to control a complex lower body humanoid robot with two six degree of freedom legs.

Humanoid Multimodal Tactile-Sensing Modules

Abstract: In this paper, we present a new generation of active tactile modules (i.e., HEX-O-SKIN), which are developed in order to approach multimodal whole-body-touch sensation for humanoid robots. To better perform like humans, humanoid robots need the variety of different sensory modalities in order to interact with their environment. This calls for certain robustness and fault tolerance as well as an intelligent solution to connect the different sensory modalities to the robot. Each HEX-O-SKIN is a small hexagonal printed circuit board equipped with multiple discrete sensors for temperature, acceleration, and proximity. With these sensors, we emulate the human sense of temperature, vibration, and light touch. Off-the-shelf sensors were utilized to speed up our development cycle; however, in general, we can easily extend our design with new discrete sensors, thereby making it flexible for further exploration. A local controller on each HEX-O-SKIN preprocesses the sensor signals and actively routes data through a network of modules toward the closest PC connection. Local processing decreases the necessary network and high-level processing bandwidth, while a local analog-to-digital conversion and digital-data transfers are less sensitive to electromagnetic interference. With an active data-routing scheme, it is also possible to reroute the data around broken connections-yielding robustness throughout the global structure while minimizing wirings. To support our approach, multiple HEX-O-SKIN are embedded into a rapid-prototyped elastomer skin material and redundantly connected to neighboring modules by just four ports. The wiring complexity is shifted to each HEX-O-SKIN such that a power and data connection between two modules is reduced to four noncrossing wires. Thus, only a very simple robot-specific base frame is needed to support and wire the HEX-O-SKIN to a robot. The potential of our multimodal sensor modules is demonstrated experimentally on a robot platform.

Robotic roommates making pancakes

Abstract: In this paper we report on a recent public experiment that shows two robots making pancakes using web instructions. In the experiment, the robots retrieve instructions for making pancakes from the World Wide Web and generate robot action plans from the instructions. This task is jointly performed by two autonomous robots: The first robot opens and closes cupboards and drawers, takes a pancake mix from the refrigerator, and hands it to the robot B. The second robot cooks and flips the pancakes, and then delivers them back to the first robot. While the robot plans in the scenario are all percept-guided, they are also limited in different ways and rely on manually implemented sub-plans for parts of the task. We will thus discuss the potential of the underlying technologies as well as the research challenges raised by the experiment.

Humanoid robot HRP-4 - Humanoid robotics platform with lightweight and slim body

Abstract: This paper presents the development of humanoid robotics platform - 4 (or HRP-4 for short). The high-density implementation used for HRP-4C, the cybernetic human developed by AIST, is also applied to HRP-4. HRP-4 has a total of 34 degrees of freedom, including 7 degrees of freedom for each arm to facilitate object handling and has a slim, lightweight body with a height of 151 [cm] and weight 39 [kg]. The software platform OpenRTM-aist and a Linux kernel with the RT-Preempt patch are used in the HRP-4 software system. Design concepts and mechanisms are presented with its basic specification in this paper.

The DLR FSJ: Energy based design of a variable stiffness joint

Abstract: Bringing mechanically compliant joints to robots is in the focus of interest world wide, especially in the humanoid robotics community. Variable Stiffness Joints (VSJ) promise to gain a high performing and robust robotic system. The presented DLR Floating Spring Joint (FSJ) is a VSJ module designed for the first 4 axes of the anthropomorphic DLR Hand Arm System. The DLR Hand Arm System aims to match the skills of its natural archetype. For this purpose, the joints have to be extremely compact to fit into the arm. At the same time they require a high power density in order to approximate the human arm skills. The new DLR FSJ is designed completely from an energy based point of view. This addresses not only energy efficient components and low friction design, but also that the potential energy of the spring is used as good as possible. A demonstration of robustness is given by the investigation of a blunt impact to the tip of the arm.

Incremental kinesthetic teaching of motion primitives using the motion refinement tube

Abstract: We present an approach for kinesthetic teaching of motion primitives for a humanoid robot. The proposed teaching method starts with observational learning and applies iterative kinesthetic motion refinement using a forgetting factor. Kinesthetic teaching is supported by introducing the motion refinement tube, which represents an area of allowed motion refinement around the nominal trajectory. On the realtime control level, the kinesthetic teaching is handled by a customized impedance controller, which combines tracking performance with compliant physical interaction and allows to implement soft boundaries for the motion refinement. A novel method for continuous generation of motions from a hidden Markov model (HMM) representation of motion primitives is proposed, which incorporates time information for each state. The proposed methods were implemented and tested using DLR's humanoid upper-body robot Justin.

Development of open humanoid platform DARwIn-OP

Abstract: This paper presents the design method for a humanoid which has a network based modular structure and a standard PC architecture. Based on the proposed method, we developed DARwIn-OP which meets the requirements for an open humanoid platform. DARwIn-OP has an expandable system structure, high performance, simple maintenance, familiar development environment, and affordable prices. All resources of DARwIn-OP including source codes, circuit diagrams, mechanical CAD files, and parts information will be opened to the public.

Randomized multi-modal motion planning for a humanoid robot manipulation task

Abstract: Robots that perform complex manipulation tasks must be able to generate strategies that make and break contact with the object. This requires reasoning in a motion space with a particular multi-modal structure, in which the state contains both a discrete mode (the contact state) and a continuous configuration (the robot and object poses). In this paper we address multi-modal motion planning in the common setting where the state is high-dimensional, and there are a continuous infinity of modes. We present a highly general algorithm, Random-MMP, that repeatedly attempts mode switches sampled at random. A major theoretical result is that Random-MMP is formally reliable and scalable, and its running time depends on certain properties of the multi-modal structure of the problem that are not explicitly dependent on dimensionality. We apply the planner to a manipulation task on the Honda humanoid robot, where the robot is asked to push an object to a desired location on a cluttered table, and the robot is restricted to switch between walking, reaching, and pushing modes. Experiments in simulation and on the real robot demonstrate that Random-MMP solves problem instances that require several carefully chosen pushes in minutes on a PC.

Vibrotactile Recognition and Categorization of Surfaces by a Humanoid Robot

Abstract: This paper proposes a method for interactive surface recognition and surface categorization by a humanoid robot using a vibrotactile sensory modality. The robot was equipped with an artificial fingernail that had a built-in three-axis accelerometer. The robot interacted with 20 different surfaces by performing five different exploratory scratching behaviors on them. Surface-recognition models were learned by coupling frequency-domain analysis of the vibrations detected by the accelerometer with machine learning algorithms, such as support vector machine (SVM) and k-nearest neighbors (k -NN). The results show that by applying several different scratching behaviors on a test surface, the robot can recognize surfaces better than with any single behavior alone. The robot was also able to estimate a measure of similarity between any two surfaces, which was used to construct a grounded hierarchical surface categorization.

A momentum-based balance controller for humanoid robots on non-level and non-stationary ground

Abstract: A momentum-based balance controller controls a humanoid robot to maintain balance. The balance controller derives desired rates of change of linear and angular momentum from desired motion of the robot. The balance controller then determines desired center of pressure (CoP) and desired ground reaction force (GRF) to achieve the desired rates of change of linear and angular momentum. The balance controller determines admissible CoP, GRF, and rates of change of linear and angular momentum that are optimally close to the desired value while still allowing the robot to maintain balance. The balance controller controls the robot to maintain balance based on a human motion model such that the robot's motions are human-like. Beneficially, the robot can maintain balance even when subjected to external perturbations, or when it encounters non-level and/or non-stationary ground.

Inverse dynamics control of floating-base robots with external constraints: A unified view

Abstract: Inverse dynamics controllers and operational space controllers have proved to be very efficient for compliant control of fully actuated robots such as fixed base manipulators. However legged robots such as humanoids are inherently different as they are underactuated and subject to switching external contact constraints. Recently several methods have been proposed to create inverse dynamics controllers and operational space controllers for these robots. In an attempt to compare these different approaches, we develop a general framework for inverse dynamics control and show that these methods lead to very similar controllers. We are then able to greatly simplify recent whole-body controllers based on operational space approaches using kinematic projections, bringing them closer to efficient practical implementations. We also generalize these controllers such that they can be optimal under an arbitrary quadratic cost in the commands.

Synthesis of complex humanoid whole-body behavior: A focus on sequencing and tasks transitions

Abstract: We present a novel approach to deal with transitions while performing a sequence of dynamic tasks with a humanoid robot. The simultaneous achievement of several tasks cannot be ensured, so we use a strategy based on weights to represent their relative importance. The robot interacts with a changing environment, and the input torques are different depending on whether the robot performs tasks in a constrained state (e.g. in contact) or not. We develop a solution with smooth weights variations and transitional tasks which avoids sharp torque evolutions. In order to validate this approach, simulations are carried out on a virtual iCub robot which is assigned the realization of a complex mission involving various changing tasks.

The design of the lower body of the compliant humanoid robot “cCub”

Abstract: The “iCub ”is a robotic platform that was developed by the RobotCub [1] consortium to provide the cognition research community with an open “child-like ”humanoid platform for understanding and development of cognitive systems [1]. In this paper we present the mechanical realization of the lower body developed for the “cCub ”humanoid robot, a derivative of the original “iCub”, which has passive compliance in the major joints of the legs. It is hypothesized that this will give to the robot high versatility to cope with unpredictable disturbance ranging from small uneven terrain variations to unexpected collisions or even accidental falls. As part of the AMARSI European project, the passive compliance of this newly developed robot will be exploited for safer interaction, energy efficient and more aggressive damage-safe learning. The passive compliant actuation module used is a compact unit based on the series elastic actuator principle (SEA). In addition to the passive compliance the “cCub ”design includes other significant updates over the original prototype such as full joint state sensing including joint torque sensing and improved range of motion and torque capabilities. In this paper, the new leg mechanisms of the “cCub ”robot are introduced.

Large-area graphene realizing ultrasensitive photothermal actuator with high transparency: new prototype robotic motions under infrared-light stimuli

Abstract: As transparent flexible electronics have recently become an emerging technology, they present great demands on the development of transparent soft actuators in the construction of invisible soft robots that would satisfy our daily lives with artificial intelligence devices. However, the current methodologies in building traditional soft actuators have intrinsically hampered the realization of high transparency devices with ultrasensitive responses. Herein, the utilization of large-area graphene successfully realizes the construction of transparent soft actuators with ultrasensitive responses, thereby opening the door for the design of three-dimensional (3D) intelligent models of transparent humanoid robots and micro-/nano-machines. The multiple synergic advantages of large-area graphene, especially its excellent IR absorption ability and ultra-thin dimensions, successfully bring together the excellent actuating sensitivity and high transparency, with the fascinating advantages of remote control, excellent mechanical strength, high sensitivity and high energy conversion efficiency. The transparent actuator model then builds a new bridge between mechanical behavior and the photo-thermal conversion effect not yet realized in other systems. Using infrared-light as the driving energy to realize complex robotic motions not only represents new prototype soft robot models, but also offers novel prospects for highly efficient solar light utilization, as well as the design of novel intelligent soft robot models.

A friendly gesture: Investigating the effect of multimodal robot behavior in human-robot interaction

Abstract: Gesture is an important feature of social interaction, frequently used by human speakers to illustrate what speech alone cannot provide, e.g. to convey referential, spatial or iconic information. Accordingly, humanoid robots that are intended to engage in natural human-robot interaction should produce speech-accompanying gestures for comprehensible and believable behavior. But how does a robot's non-verbal behavior influence human evaluation of communication quality and the robot itself? To address this research question we conducted two experimental studies. Using the Honda humanoid robot we investigated how humans perceive various gestural patterns performed by the robot as they interact in a situational context. Our findings suggest that the robot is evaluated more positively when non-verbal behaviors such as hand and arm gestures are displayed along with speech. These findings were found to be enhanced when the participants were explicitly requested to direct their attention towards the robot during the interaction.

"humanoid robot equipped with a natural dialogue interface, method for controlling the robot and corresponding program"

Abstract: A humanoid robot equipped with an interface for natural dialog with an interlocutor is provided. Previously, the modalities of dialog between humanoid robots equipped moreover with evolved displacement functionalities and human beings are limited notably by the capabilities for voice and visual recognition processing that can be embedded onboard said robots. The present disclosure provides robots are presently equipped with capabilities to resolve doubt on a several modalities of communication of the messages that they receive and for combining these various modalities which make it possible to greatly improve the quality and the natural character of dialogs with the robots' interlocutors. This affords simple and user-friendly means for carrying out the programming of the functions making it possible to ensure the fluidity of these multimodal dialogs.

Imitating human reaching motions using physically inspired optimization principles

Abstract: We present an end-to-end framework which equips robots with the capability to perform reaching motions in a natural human-like fashion. A markerless, high-accuracy, model-based human motion tracker is used to observe how humans perform everyday activities in real-world scenarios. The obtained trajectories are clustered to represent different types of manipulation and reaching motions occurring in a kitchen environment. Using bilevel optimization methods a combination of physically inspired optimization principles is determined that describes the human motions best. For humanoid robots like the iCub these principles are used to compute reaching motion trajectories which are similar to human behavior and respect the individual requirements of the robotic hardware.

Operational Space Control of Constrained and Underactuated Systems

Abstract: The operational space formulation (Khatib, 1987), applied to rigid-body manipulators, describes how to decouple task-space and null space dynamics, and write control equations that correspond only to forces at the end-effector or, alternatively, only to motion within the null space. We would like to apply this useful theory to modern humanoids and other legged systems, for manipulation or similar tasks, however these systems present additional challenges due to their underactuated floating bases and contact states that can dynamically change. In recent work, Sentis et al. derived controllers for such systems by implementing a task Jacobian projected into a space consistent with the supporting constraints and underactuation (the so called support consistent reduced Jacobian). Here, we take a new approach to derive operational space controllers for constrained underactuated systems, by first considering the operational space dynamics within projected inverse-dynamics (Aghili, 2005), and subsequently resolving underactuation through the addition of dynamically consistent control torques. Doing so results in a simplified control solution compared with previous results, and importantly yields several new insights into the underlying problem of operational space control in constrained environments: 1) Underactuated systems, such as humanoid robots, cannot in general completely decouple task and null space dynamics. However, 2) there may exist an infinite number of control solutions to realize desired task-space dynamics, and 3) these solutions involve the addition of dynamically consistent null space motion or constraint forces (or combinations of both). In light of these findings, we present several possible control solutions, with varying optimization criteria, and highlight some of their practical consequences.

Human-Like Walking: Optimal Motion of a Bipedal Robot With Toe-Rotation Motion

Abstract: Fast human walking includes a phase where the stance heel rises from the ground and the stance foot rotates about the stance toe. This phase where the biped becomes underactuated is not present during the walk of current humanoid robots. The objective of this study is to determine whether the introduction of this phase for a 3-D bipedal robot is useful to reduce the energy consumed in the walking. In order to study the efficiency of this new gait, two cyclic gaits are presented. The first cyclic motion is composed of successive single-support phases with a flat stance foot on the ground, and the stance foot does not rotate. The second cyclic motion is composed of single-support phases that include a subphase of rotation of the supporting foot about the toe. The single-support phases are separated by a double-support phase. For simplicity, this double-support phase is considered as instantaneous (passive impact). For these two gaits, optimal motions are designed by minimizing the torques cost. The given performances of actuators are taken into account. It is shown that, for a fast motion, a foot-rotation subphase is useful to reduce the cost criterion. These gaits are illustrated with simulation results.

Humanoid robot NAO: Review of control and motion exploration

Abstract: Humanoids; a most intriguing subject to behold by both the engineers and the world at large. With the introduction of humanoid robot NAO by Aldebaran-Robotics in 2008, a performant biped robot is now available and affordable for research laboratories and the mass market. In this paper, an exploration of current trends in control methods of biped walks, behavior interface tools for motion control for NAO and imminent findings in both research areas are discussed. Future directions are for researchers to devise a unique controller with low power consumption without compromising the robot's speed and robustness.

Robot Cognitive Control with a Neurophysiologically Inspired Reinforcement Learning Model

Abstract: A major challenge in modern robotics is to liberate robots from controlled industrial settings, and allow them to interact with humans and changing environments in the real world. The current research attempts to determine if a neurophysiologically motivated model of cortical function in the primate can help to address this challenge. Primates are endowed with cognitive systems that allow them to maximize the feedback from their environment by learning the values of actions in diverse situations and by adjusting their behavioral parameters (i.e. cognitive control) to accommodate unexpected events. In such contexts uncertainty can arise from at least two distinct sources – expected uncertainty resulting from noise during sensory-motor interaction in a known context, and unexpected uncertainty resulting from the changing probabilistic structure of the environment. However, it is not clear how neurophysiological mechanisms of reinforcement learning and cognitive control integrate in the brain to produce efficient behavior. Based on primate neuroanatomy and neurophysiology, we propose a novel computational model for the interaction between lateral prefrontal and anterior cingulate cortex (LPFC and ACC) reconciling previous models dedicated to these two functions. We deployed the model in two robots and demonstrate that, based on adaptive regulation of a meta-parameter β that controls the exploration rate, the model can robustly deal with the two kinds of uncertainties in the real world. In addition the model could reproduce monkey behavioral performance and neurophysiological data in two problem-solving tasks. A last experiment extends this to human-robot interaction with the iCub humanoid, and novel sources of uncertainty corresponding to “cheating” by the human. The combined results provide concrete evidence for the ability of neurophysiologically inspired cognitive systems to control advanced robots in the real world.

Push Recovery Control for Force-Controlled Humanoid Robots

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State estimation for force-controlled humanoid balance using simple models in the presence of modeling error

Abstract: This paper considers the design of state estimators for dynamic balancing systems using a Linear Inverted Pendulum model with unknown modeling errors such as a center of mass measurement offset or an external force. A variety of process and output models are constructed and compared. For a system containing modeling error, it is shown that a naive estimator (one that doesn't account for this error) will result in inaccurate state estimates. These state estimators are evaluated on a force-controlled humanoid robot for a sinusoidal swaying task and a forward push recovery task.

Integration of Speech and Action in Humanoid Robots: iCub Simulation Experiments

Abstract: Building intelligent systems with human level competence is the ultimate grand challenge for science and technology in general, and especially for cognitive developmental robotics. This paper proposes a new approach to the design of cognitive skills in a robot able to interact with, and communicate about, the surrounding physical world and manipulate objects in an adaptive manner. The work is based on robotic simulation experiments showing that a humanoid robot (iCub platform) is able to acquire behavioral, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to understand basic instructions, and to adapt its abilities to changes in internal and environmental conditions.