A Humanoid Robot that Breaks Wooden Boards Applying Impulsive Force

This paper proposed a new method for humanoid robot gait generation based on several objective functions. The proposed method is based on multiobjective evolutionary algorithm. In our work, we considered two competing objective functions: MCE and MTC. Based on simulation and experimental results, we conclude: Multiobjective evolution is efficient because optimal humanoid robot gaits with completely different characteristics can be found in one simulation run. The nondominated solutions in the obtained Pareto-optimal set are well distributed and have satisfactory diversity characteristics. The optimal gaits generated by simulation gave good performance when they were tested in the real hardware of "Bonten-Maru" humanoid robot. The optimal gait reduces the energy consumption and increases the stability during the robot motion. In the future, it will be interesting to investigate if the robot can learn in real time to switch between different gaits based on the environment conditions. In uneven terrains MTC gaits will be more

In this article, the authors present a new method for humanoid robot motion planning, satisfying multiple objectives. In this method, the multiple objectives humanoid robot motion is formulated as a multiobjective optimization problem, considering each objective as a separate fitness function. Three different objectives are considered: (1) minimum energy consumption; (2) stability; and (3) walking speed. The advantage of the proposed method is that, in a single run of multiobjective evolution, generated humanoid robot motions satisfy each objective separately or multiple objectives simultaneously. Therefore, the humanoid robot can switch between different gaits based on environmental conditions. The results show that humanoid robot gaits generated by multiobjective evolution are similar to that of humans. To further verify the performance of optimal motions, they are transferred to the “Bonten-Maru” humanoid robot.

In the near future, humanoid robots will act as the partners of human beings in daily life. Among numerous human-like competencies, motion of humanoid robots is critical for providing humans with richer interactions with such robots. Motion plays an essential role in complementing spoken communication. Moreover, the motions of humanoid robots generate nonverbal communication in various contexts. Through this nonverbal communication, humans can interact with robots not only directly but also indirectly or even unconsciously, as if the robot were simply part of the environment. Before the developments of humanoid robots, embodied conversational agents (ECAs) were introduced as virtual embodied representations of humans that communicated multi-modally with humans and there has been a great deal of research on ECA behavior. ECAs and humanoid robots share many features in terms of how they communicate with humans. Nevertheless, simply adapting knowledge gathered from current ECA studies to a humanoid robot study is insufficient for the following reasons: 1. ECA studies lack knowledge focused on nonverbal communication, which has become more important in the physical world; 2. ECA studies have focused on developing agent-centered intelligence rather than a user- centered experience; 3. ECA studies have developed logics to generate motions automatically rather than to provide designers with the practical knowledge necessary to design desirable motions. Motivated by these three arguments, we seek to pioneer a new field of motion design between robot engineering and design discipline. To bring this motion study into design discipline, we focused on human-centered experience through nonverbal communication with a humanoid robot. This paper aims to outline sharable user experience in order to help designer create desirable motions for humanoid robots in various speechless contexts.

Humanoid robots are similar in dimension and degrees of freedom to humans. Compared with other types of robots, humanoid robots are more adaptable to complex living environment and various tools of humans. The combination of wheeled robot and humanoid robot is an effective way to increase the moving speed, range of motion and environmental adaptability of humanoid robots, which can accelerate the application of humanoid robots in human living environments. In this way, the use of humanoid robots in industry and services can be effectively improved. This article introduces a novel electrically-driven wheel-legged humanoid robot BHR-W with 14 degrees of freedom. It can use wheels to achieve rapid motion on structured pavement. In addition, BHR-W has two degrees of freedom of hip joint, giving it excellent ability to perform complex actions such as walking, jumping and roller skating to cross unstructured environments. BHR-W is an innovative design which enables humanoid robots to have capabilities of bipedal walking and wheel rolling. This article introduces the mechanical and system design of the robot, and designs a controller for balance control. Finally, the stability and reliability of the system and the feasibility of the balance control strategies are verified by several practical experiments.

Learning-based algorithms remain a challenge in the field of humanoid robot motion control. Due to the diverse range of humanoid robot motions and the complexity of control, existing motion control methods often require the development of distinct control frameworks or using different network architectures. This paper proposes a keyframe-driven whole-body motion control framework for humanoid robots that allows the robot to perform a variety of motions through manually designed actions or video demonstrations. We introduce a humanoid robot motion classification method to enable the robot to learn various types of action. Moreover, we design a Dual Actor Single Critic Network(DASCN) network for whole-body control of the humanoid robot. Experimental results demonstrate that our method enables the humanoid robot H1 to execute a range of movements based solely on pre-designed motions or demonstrated videos.

This paper presents a component-based framework of humanoid robot task coordination using Predicate Transition Nets (PrT Nets). Humanoid robots have a large number of degrees of freedom (DOF) and they are expected to generate human-like stable behaviors to finish missions. Their motions have to satisfy a set of constraints (balance, coordinated motions, collision free movements), which increases the complexity of the system - each movement may consist of several types of movements and tasks simultaneously. Task is defined as a sequence of movements and/or actions towards to completing a given mission. It is key to represent the task coordination precisely and correctly to program on humanoid robots' motions. To solve the challenge issue of multiple task coordination for a humanoid robot, we presented a framework that integrates Component Based Software Development (CBSD) with Predicate Transition Nets to analyze the correctness of task co ordinations. Each component's behavior can be represented by a Predicate Transition Net, and the constraints of the behaviors are described by a set of temporal logic formulae. To ensure the task co ordinations are met, a rewriting logic-based model checker is applied to verify the system against the constraints. This formal framework is general and can be used for other type humanoid robots. It allows the representation of motion plans and provides the flexibility of semantic analysis on the humanoid robotics systems.

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.

The present study evaluates the cognitive representation of a kicking movement performed by a human and a humanoid robot, and how they are represented in experts and novices of soccer and robotics, respectively. To learn about the expertise-dependent development of memory structures, we compared the representation structures of soccer experts and robot experts concerning a human and humanoid robot kicking movement. We found different cognitive representation structures for both expertise groups under two different motor performance conditions (human vs. humanoid robot). In general, the expertise relies on the perceptual-motor knowledge of the human motor system. Thus, the soccer experts' cognitive representation of the humanoid robot movement is dominated by their representation of the corresponding human movement. Additionally, our results suggest that robot experts, in contrast to soccer experts, access functional features of the technical system of the humanoid robot in addition to their perceptual-motor knowledge about the human motor system. Thus, their perceptual-motor and neuro-functional machine representation are integrated into a cognitive representation of the humanoid robot movement.

Whole-Body Control has been extensively used to achieve humanoid robot force and motion tasks simultaneously during recent years. However, most existing results have not incorporated low-level actuator dynamics and time delays yet. In this study, we propose a novel time-delayed Whole-Body Operational Space control (WBOSC) with series elastic actuator (SEA) dynamics. This type of controller generalizes our previously proposed distributed control structure to multi-input and multi-output free floating humanoid robotic systems. Namely, Cartesian stiffness control is adopted to design the WBOSC at the centralized level while motor damping control is implemented at the embedded level to remedy the stability deterioration caused by time delays. Additionally, embedded-level torque feedback control is formulated and physically interpreted as a shaping of the motor inertia. To ensure passivity, we separate the overall system into two subsystems interconnected in a feedback configuration. By the Lyapunov-Krasovskii functional technique, we propose a delay-dependent passivity criterion of the closed-loop system in the form of linear matrix inequalities (LMIs), and solve for the allowable maximum time delays via the passivity criterion. Numerical simulations of a dynamic locomotion process are used to validate the proposed passivity criterion and the WBOSC framework.

In the last decade we have witnessed a rapid growth of Humanoid Robotics, which has already constituted an autonomous research field. Humanoid robots (or simply humanoids) are expected in all situations of humans’ everyday life, “living” and cooperating with us. They will work in services, in homes, and hospitals, and they are even expected to get involved in sports. Hence, they will have to be capable of doing diverse kinds of tasks. This forces the researchers to develop an appropriate mathematical model to support simulation, design, and control of these systems. Another important fact is that today’s, and especially tomorrow’s, humanoid robots will be more and more humanlike in their shape and behavior. A dynamic model developed for an advanced humanoid robot may become a very useful tool for the dynamic analysis of human motion in different tasks (walking, running and jumping, manipulation, various sports, etc.). So, we derive a general model and talk about a human-and-humanoid simulation system. The basic idea is to start from a human/humanoid considered as a free spatial system (“flier”). Particular problems (walking, jumping, etc.) are then considered as different contact tasks – interaction between the flier and various objects (being either single bodies or separate dynamic systems).

This paper presents a method to optimize and filter trajectories generated from recorded human motion for a humanoid robot with physical limits. The objective function is responsible for mimicking human trainers, enhancing the possibility for fast convergence, while constraints are used to transform motion within the limit of the capabilities of the humanoid robot. Those constraints for angle, velocity, and dynamic force are represented as B-spline coefficients. An iterative soft-constraint paradigm is proposed to enhance the quality of velocity and force constraints. Collision avoidance is also considered as a constraint. For precision refinement, in regions of high-frequency motion not adequately modeled by an initial splining, B-spline is extensible into a hierarchy so that optimization that meets global criteria can be performed locally. Furthermore, all of the constraints can be used solely as filtering. To use these filters, an effective method to directly decompose a trajectory to a B-spline is also presented

Planning collision-free, dynamically-balanced movements for humanoid robots is a challenging problem. An effective approach consists of first planning a motion satisfying geometric and kinematic constraints (such as collision avoidance, joint angle limits, velocity limits, etc.) and, in a second stage, modifying this motion so that it respects dynamic balance criteria, such as those relative to the Zero Moment Point (ZMP). However, this approach currently suffers from the issue that the modified motion may give rise to new collisions with respect to the original motion, which can be very costly to deal with, especially for systems with many degrees of freedom and cluttered environments. Here we present an algorithm to modify the motions of humanoid robots under ZMP constraints without changing the original motion path, making thereby new collision checks unnecessary. We do so by adapting the minimum-time path parameterization under torque constraints algorithm of Bobrow et al. to the case of ZMP constraints. In contrast with a previous approach based on finite differences and iterative optimization to find the optimal path parameterization under ZMP constraints, our Bobrow-based algorithm finds this optimal parameterization in a single pass. We demonstrate the efficiency of this algorithm by simulations.

Given the complexity and the high number of degrees of freedom humanoid robot motions often are generated off-line, stored in the robot memory and then reproduced. In the simplest cases motor commands are just replayed in open-loop (i.e. feed-forward), in more advanced implementations few parameters are changed on fly to adapt the motion to the external conditions. Indeed, several authors proposed a large variety of techniques to exploit the input from sensory feedback to modify a reference trajectory, in order to cope with environment changes and disturbances. In this paper, we propose a motion representation for humanoid robots that includes the sensory feedback information in the motion representation itself. This motion description allows stabilization against disturbances and environmental changes, but does not require any design or tuning of the relationships between sensory inputs and movement modification. We present experimental results on a simulated small humanoid robot equipped with motor encoders and touch sensors covering the whole body.

Recently, the robotics technologies are being used in the field of the performing art. As the motion of a humanoid robot motion is more sophisticated, the robot based performing art is became popular. In this paper, a low cost robot system for live performance is proposed. The proposed system includes the humanoid robot actors and authoring tools. The humanoid robot consists of several actuator modules and a control unit, and the authoring tools consist of the motion suit and motion editor software. The proposed system was utilized to a live performance and showed its potential possibility and usefulness.

Humanoid robots can achieve increasingly complex functions and adapt to more complex environments. To boost the development of humanoid robot technology, a team at Beijing Institute of Technology initiated the research on humanoid robots from 2000. Their research primarily focuses on stable walking, whole-body complex motion, human-robot interaction, and multimodal motion of humanoid robots. Thus far, the team has developed 6 generations of humanoid robots. The latest humanoid robot, BHR-6P, can achieve multi-mode motions (for example, walk, jump, fall protection, crawl and roll), which will significantly improve the ability of robot to adapt to the environment. This paper presented the historical evolution of BHR humanoid robots and outlined their functions and features.