Learning for Cooperative Transportation by Autonomous Humanoid Robots

(2004). Robo-Erectus: a low-cost autonomous humanoid soccer robot. Advanced Robotics: Vol. 18, No. 7, pp. 717-720.

This paper contributes to the problem of humanoid robot gait generation in unknown environments. The intention of the proposed method is to create an autonomous humanoid robot, able to take decisions and generate the appropriate optimal gait based on the information received by the eye system. Up to now, we have created two modules: walking and going upstairs. In order to create an autonomous humanoid robot, we plan to consider other tasks like going downstairs, creeping, obstacle overcoming, etc. In this paper, we present the simulation and experimental results for real time humanoid robot gait generation realized with the “Bonten‐Maru I” humanoid robot. The results showed that the Neural Network modules generate in a very short time a stable humanoid robot motion.

This work is part of an ongoing work for sign language tutoring with imitation based turn-taking and interaction games with humanoid robots. The aim of the project is to motivate the children with communication problems to understand and imitate the signs implemented by the humanoid robots. In this study, we implemented selected words from Turkish Sign Language Dictionary on autonomous humanoid robots (NAO H-25 and Robovie R3). We also examined the effects of interaction games on sign language teaching between participants with different age groups and the humanoid robot.

A behavior strategy of humanoid robot for obstacle avoidance based on four infrared sensors is proposed and implemented on an autonomous humanoid robot. A mechanical structure with 26 degrees of freedom is design so that an implemented small-size humanoid robot named TWNHR-Ⅲ is able to accomplish five walking motions. Three walking experiments are presented to illustrate that the proposed biped structure lets TWNHR-Ⅲ can move forward, turn, and slip. One electronic compass and four infrared sensors are mounted on TWNHR-Ⅲ to obtain the head direction of the robot and detect obstacles, respectively. Based on the obtained information from these sensors, a decision tree method is proposed to decide one behavior from five movements: walk forward, turn right and left, and slip right and left. Two MATLAB simulations and one real experiment are presented to illustrate that the robot can avoid obstacles autonomously and go to the destination effectively.

This paper presents a new design for formation control on a group of autonomous humanoid robots with obstacle and collision avoidance ability that inspired by human walking behavior in a pedestrian crowd. This human walking behavior model is known as Social Force Model (SFM) and has been widely used to predict individual movement in a crowd in many variety of situation. We combine consensus algorithm with virtual structure approach for formation control and SFM for obstacle and collision avoidance. In this research, we induced some factors in SFM into consensus equation and then implemented into a group of humanoid robots. The aim of the integration is to create a group of robots that are capable of carrying out its collective tasks while still able to maintain its safety. The use of perception effect, distinct obstacle and collision avoidance function on the algorithm will differentiate algorithm with the others. The attractive feature of this new design is the various behaviors of robots to go back to their initial formation after they avoid obstacles. In the case of robot is trapped in a crowded situation (singularity condition), robot will still trying to look a new position, until it find a condition that allow him to move forward. We verify the proposed algorithm by some simulations and experiments.

Active balancing in autonomous humanoid robots is a challenging task due to the complexity of combining a walking gait with dynamic balancing, vision and high-level behaviors. Humans not only walk successfully over even and uneven terrain, but can recover from the interaction of external forces such as impacts with obstacles and active pushes. While push recovery has been demonstrated successfully in expensive robots, it is more challenging with robots that are inexpensive, with limited power in actuators and less accurate sensing. This work describes a closed-loop control method that uses an accelerometer and gyroscope to allow an inexpensive humanoid robot to actively balance while walking and recover from pushes. An experiment is performed to test three hand-tuned closed-loop control configurations; using only a the gyroscope, only the accelerometer, and a combination of both sensors to recover from pushes. Experimental results show that the combination of gyroscope and accelerometer outperforms the other methods with 100% recovery from a light push and 70% recovery from a strong push.

Researchers’ goals shape the questions they raise, collaborators they choose, methods they use, and outcomes of their work. This article offers a fresh vision of artificial intelligence (AI) research by suggesting a simplification to two goals: 1) emulation to understand human abilities to build systems that perform tasks as well as or better than humans and 2) application of AI methods to build widely used products and services. Researchers and developers for each goal can fruitfully work along their desired paths, but this article is intended to limit the problems that arise when assumptions from one goal are used to drive work on the other goal. For example, autonomous humanoid robots are prominent with emulation researchers, but application developers avoid them, in favor of tool-like appliances or teleoperated devices for widely used commercial products and services. This article covers four such mismatches in goals that affect AI-guided application development: 1) intelligent agent or powerful tool; 2) simulated teammate or teleoperated device; 3) autonomous system or supervisory control; and 4) humanoid robot or mechanoid appliance. This article clarifies these mismatches to facilitate the discovery of workable compromise designs that will accelerate human-centered AI applications research. A greater emphasis on human-centered AI could reduce AI’s existential threats and increase benefits for users and society, such as in business, education, healthcare, environmental preservation, and community safety.

In these two decades, the technology of bipedal and humanoid robots has been made great progress. Up so far, many bipedal and humanoid robots are successfully developed (Hirai et al., 1998; Takanishi et al., 1985; Ogura et al., 2004; Kaneko et al., 2004; Nakasaka et al., 2004; Loffler et al., 2003; Lohmeier et al., 2004). In these robots, the gait planning and control for bipedal walking are based on ZMP concept (Vukobratovich et al., 1990; Goswami, 1999; Kakami, 2000). There are generally two methods for implementation of the dynamic and stable walking. The first one is based on a multiple rigid body robot model (Takanishi et al., 1985). This model is comparatively precise, but it requires a huge computation cost. The second one is simply based on the assumption that the whole robot mass is concentrated to robot’s CoG (Center of Gravity). The typical approach is using a 3D inverted pendulum model (Kajita & Kobayashi, 1987; Kajita et al., 2002; 2003; Zhu et al., 2003, 2004), in which, the robot locomotion in the sagittal and lateral planes are supposed to be independent. However, up to now, the bipedal walking nature and limitations of walking speed, stride, stride motion time, and so on, have not been completely understood. Partially this is because (1) few attentions are paid on the motion in double support phase and its effect to whole bipedal walking; (2) the investigation is not done intensively even for single support phase; for example, motion in sagittal plane are simply dealt with independent of the motion in lateral plane in most literatures on biped and humanoid robots. As revealed latter, in fact, such an approach is improper since the motions in the two planes are strongly coupled together. (3) ZMP is fixed to a point in robot sole for most of bipedal or humanoid robots. This paper mainly discusses the above first two problems based on ZMP concept and an inverted pendulum model with the assumption that ZMP is fixed at the center of the robot sole in single support phase. To do this, the relation between the motions in the sagittal and lateral planes is investigated first. By dividing a whole bipedal walking cycle into a double support, a deceleration, and an acceleration phases, and synchronizing the motions in the sagittal and lateral planes, we point out that the motions in these two planes are strongly coupled together, in other words, the motion in the lateral plane greatly restricts the motion in the sagittal plane, vice versa. Then, the role of the double support phase in biped walking is discussed. By changing the start and finish points of the double support phases in the lateral plane, the walking parameters such as walking speed, walking period, phase stride

This paper presents the 3D foot and center of gravity motion planning for the humanoid robot called the "local axis gait" (LAG) algorithm. It permits walking on different kinds of surfaces, such as planes, ramps or stairs. Furthermore, continuous change of the step length and orientation in real time will be possible, due to the real-time linear dynamics model of the walking pattern of the humanoid. The robot model is based on the cart table formulation for planning the center of gravity (COG) and zero moment point (ZMP) motion. The proposed algorithm takes into account physical robot constraints such as joint angles, angular velocity and torques. Torques are computed by the Lagrange method under screws and Lie groups. The LAG is divided into several stages: computation of the footprints; the decision of the ZMP limits around the footprints; the dynamic humanoid COG motion generation based on the cart table model; and joining the footprints of the swing foot by splines. In this way it is possible to generate each step online, using the desired footprints as input. In order to compute the joint torque limits, the Lagrangian method is used under the Lie groups and screw theory. The paper presents and discusses some successful results on the LAG in the full-size humanoid robot Rh-1 developed in the Roboticslab of University Carlos III of Madrid.

One of the core features of social robotics system is a physical interaction between humans and humanoid robots. This provides additional challenges, both from safety and usability prospectives. When dealing with human-robot interaction, human safety has the highest priority. While in industrial environment we have robot cells to protect humans, in social robotics, that we consider, physical contact is possible, as well as other interactions, with consequences that might be in psychological areas. For example, the conversation with children might have different requirements in comparison to the conversation with adults, the behavioural assumptions might be different, etc. This paper summarises the core results of a project on social robotics system, where an autonomous humanoid robot guides visitors through a lab tour. The results of our work were implemented on the humanoid PAL REEM robot. The implementation includes a web-application to support the management of robot-guided tours. The application also provides recommendations for the users as well as allows for a visual analysis of historical data on the tours.

This paper is focused on improvement of the humanoid robot walking by conducting dynamic walking experiments, based on human pattern gait parameters. The proposed approach has been tested and implemented to the Bioloid robot, firmly by simulating the robot on the Matlab program. Humanoid interaction considers that the robot starts and finishes the walk with the Double Support Feet (DSF). In this paper is presented the kinematic model of the Bioloid robot based on Denavit-Hartenberg (D-H) convention. To imitate the human walking, a three dimensions trajectory of the feet and the hip has been developed. For the design of the trajectories, first motion constraints during walking have been derived based on the human walking pattern. The sinusoidal spline interpolation is used to find the smooth trajectories for the foot and the hip in both single and double support phases are implemented on the Bioloid robot. In order to realize the humanoid walking in the sense of static stability, the robot’s centre of gravity during the walking was located within the support foot area. After tests with different step lengths and step heights, we are able to analyse the end-effector of each walking parameter before implementation into the real humanoid robot.

Emotional processes in human-robot interaction during brief cognitive testing

Autonomous robots need to learn intelligent behaviour by interacting with the real world and adapting to changing environments. In order to study the adaptive behaviour of intelligent systems, an autonomous humanoid robot has been developed which understands goals, senses its environment in context with the goal and operates independently by means of experience gathering and a self-generating will.

In this paper, a simple path correction and obstacle avoidance method with a bipedal robot for the learning stratification and performance, using an ultrasonic sensor and electronic compass sensor, is proposed. This bipedal robot was comprised using the Lego NXT Intelligent Bricks. The proposed method is implemented on an autonomous humanoid robot (the ARSR). One ultrasonic sensor and one electronic compass sensor are installed on the ARSR to detect environmental information including obstacles, the distance to the obstacle, and the directional angle of the robot. Based on the obtained information, an obstacle avoidance and path correct method is proposed to decide the ARSR's behavior so that it can avoid obstacles and move effectively to the destination area. Obstacle avoidance experiments are carried out to confirm the effectiveness of the proposed method and confirm the learning stratification and performance.

Many new applications for robots require them to work alongside people as capable members of human-robot teams. These include—in the long term—robots for homes, hospitals, and offices, but already exist in more advanced settings, such as space exploration. The work reported in this paper is part of an ongoing collaboration with NASA JSC to develop Robonaut, a humanoid robot envisioned to work with human astronauts on maintenance operations for space missions. To date, work with Robonaut has mainly investigated performing a joint task with a human in which the robot is being teleoperated. However, perceptive disorientation, sensory noise, and control delays make teleoperation cognitively exhausting even for a highly skilled operator. Control delays in long range teleoperation also make shoulder-to-shoulder teamwork difficult. These issues motivate our work to make robots collaborating with people more autonomous. Our work focuses on a scenario of a human and an autonomous humanoid robot working together shoulder-to-shoulder, sharing the workspace and the objects required to complete a task. A robotic member of such a team must be able to work towards a shared goal, and be in agreement with the human as to the sequence of actions that will be required to reach that goal, as well as dynamically adjust its plan according to the human’s actions. Human-robot collaboration of this nature is an important yet relatively unexplored kind of human-robot interaction. This paper describes our work towards building a dynamic collaborative framework enabling such an interaction. We discuss our architecture and its implementation for controlling a humanoid robot, working on a task with a human partner. Our approach stems from Joint Intention Theory, which shows that for joint action to emerge, teammates must communicate to maintain a set of shared beliefs and to coordinate their actions towards the shared plan. In addition, they must demonstrate commitment to doing their own part, to the others doing theirs, to providing mutual support, and finally—to a mutual belief as to the state of the task. We argue that to this end, the concept of task and action goals is central. We therefore present a goal-driven hierarchical task representation, and a resulting collaborative turn-taking system, implementing many of the above-mentioned requirements of a robotic teammate. Additionally, we show the implementation of relevant social skills supporting our collaborative framework. Finally, we present a demonstration of our system for collaborative execution of a hierarchical object manipulation task by a robot-human team. Our humanoid robot is able to divide the task between the participants while taking into consideration the collaborator’s actions when deciding what to do next. It is capable of asking for mutual support in the cases where it is unable to perform a certain action. To facilitate this interaction, the robot actively maintains a clear and intuitive channel of communication to synchronize goals, task states, and actions, resulting in a fluid, efficient collaboration.