Humanoid robot motion generation with sequential physical constraints

Motion generation for humanoid robots is a hard task because of the complex and unstable structures. We solve this task using MONGERN which is a method to generate robot motions based on GRN with an evolutionary approach. MON GERN does not require any calculation of dynamic properties but can generate suitable motions for robots. In this research, we tried to obtain three realistic motions in the simulator called Webots to show the robustness and effectiveness of MONGERN. The motions are trap motion in soccer, bracing motion for a predictable impact and continuous skating motion. In our experiments, we were able to obtain desirable motions which include unexpected motions. We were able to observe the robust and flexible motion-generation ability of this method. In the case of skating motion, it has been successfully applied to a real robot, KHR3HV designed by Kondo-Science. These experiments imply that the method can be useful in generating complex motions even in the real world.

This paper proposes an intuitive real-time robot control system using human body movement. Recently, it has been developed that motion generation for humanoid robots with reflecting human body movement, which is measured by a motion capture. However, in the existing studies about robot control system by human body movement, the detailed structure information of a robot, for example, degrees of freedom, the range of motion and forms, must be examined in order to calculate inverse kinematics. In this study, we have proposed Associative Motion Generation as humanoid robot motion generation method which does not need the detailed structure information. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis and Jordan recurrent neural network, and the associative motion is generated with the following three steps. First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using nonlinear principal component analysis. Last, the robot generates a new motion through calculation by Jordan recurrent neural network using the associative values. In this paper, we propose a real-time humanoid robot control system based on Associative Motion Generation, that enables user to control motion intuitively by human body movement. Through the task processing and subjective evaluation experiments, we confirmed the effective usability and affective evaluations of the proposed system.

We describe a method of generating new motions associatively from unfamiliar indications. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis (NLPCA) and Jordan recurrent neural network (JRNN). First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using NLPCA. Last, the robot generates a new motion through calculation by JRNN using the associative values. Experimental results demonstrated that our method enabled a humanoid robot, KHR-2HV, to associatively generate some kinds of motion depending on given unfamiliar indications.

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.

When a human needs to generate a large force, the human will try to apply an impulsive force cooperating whole body. However, exerting impulsive force is one of the most difficult tasks for humanoid robots, because a reaction force caused by the applied impulsive force may bring the humanoid robot down. This paper discusses a humanoid robot motion to generate a large force utilizing an impact. Breaking wooden boards by Karate-chop is taken as a case study because it is a typical example of tasks that utilize impulsive force. A humanoid robot motion for the Karate-chop is carefully designed to maximize the speed of a hand keeping the stability. In order to validate the designed motion, experiments are carried out using a small humanoid robot Fujitsu HOAP-2. In the experiments, three kinds of wooden boards are tested to break by the Karate-chop. The strength of wooden boards is estimated striking the boards with a free-falling plummet. Stabilizing control is also applied to the robot in order to stabilize the body after the impact. The experimentation results are reported in the paper

We aim to develop a humanoid robot which can go anywhere an ordinary human covers. For such purpose, we consider enhancing the walk and work capabilities of a humanoid robot by integrating manipulation and locomotion. Especially, we focus on three motions of a humanoid robot: pushing a heavy object placed on the floor, working while thrusting a hand on to a desk, and ascending large steps while holding a handrail for support. These motions are realized by experiment using the humanoid robot HRP-2.

Purpose Locomotion tracking is a critical capability for humanoid robots to navigate environments and perform loco-manipulation tasks. Achieving this requires fulfilling various kinematic and dynamic sub-objectives, such as accurate tracking of the robot’s base, joints and feet, environment-collision avoidance, and dynamic balance and stability. The purpose of this paper is to propose a controller to generate motions for humanoid robots considering all sub-objectives of locomotion tracking. Design/methodology/approach This paper introduces a hierarchical model predictive control (MPC) framework for the locomotion tracking control problem of humanoid robots. All kinematics sub-objectives are firstly solved at the high-level MPC using full kinematics with second-order kinematics of base. Both kinematics and dynamics sub-objectives are optimized in the low-level kinodynamic MPC considering centroidal dynamics and surface contact dynamics. Findings This paper validates the effectiveness of this method through extensive simulation and hardware experiments. In comparison to traditional whole-body MPC, the proposed method improves the locomotion tracking accuracy while reducing the violations of the system’s physical limit constraints and environment-collision avoidance constraints. Originality/value Both reinforcement learning (RL) and whole-body MPC have become popular approaches for motion control of legged robot. However, achieving all the sub-objectives of locomotion within a single policy remains a challenge for RL methods. Due to computation limitations and strict real-time requirements, it is difficult for the whole-body MPC to generate optimal motions over a short-time horizon while considering multiple tracking goals and nonlinear dynamics of humanoid robots.

Distributed formation control of multi humanoid robot system with obstacle avoidance and collision avoidance are presented. The architecture consists of several layers, namely consensus tracking, consensus based formation control, behaviour and physical robot layers. The formation control system was simulated in MATLAB software before it was implemented to humanoid NAO Aldebaran robots. In the experimental study the visual odometry was used to track the robots’ position and their moving direction. The result of the experiment showed that the distributed formation control design was succeed in directing the robots into their planned formation, avoiding obstacles, and avoiding collisions among themselves with certain communication topology, shape of formation, obstacles’ position and their number.

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.

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.

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 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.

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.

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.