A Keyframe-driven Whole-body Motion Control Framework for Humanoid Robots

The robust dynamic walking control of humanoid robots is the foundation for their application in complex scenarios. Model Predictive Control (MPC) can predict the robot’s motion state over a future time horizon to obtain optimal control inputs and has achieved significant success in walking control of legged robots. However, the high computational cost makes it challenging to ensure real-time performance when dealing with complex dynamic systems. A novel dynamic walking control framework for humanoid robots is introduced in this paper by integrating an efficient footstep planner and Task Space Inverse Dynamics based Whole Body Controller (TSID-WBC). This framework aims to enhance the dynamic performance and robustness of the humanoid robot’s walking control. Firstly, design a bionic step time generator based on relevant research in biology. Then, the MPC controller, combined with the differential equation solution of the Linear Inverted Pendulum (LIP) dynamics model, is used to predict the optimal footsteps for the humanoid robot. Finally, TSID-WBC is employed to optimize joint torques, joint accelerations, and generalized contact forces based on predefined task priorities. The proposed framework avoids using MPC for predictive control of complex dynamic models. Instead, it innovatively integrates the bionic step time generator proposed in this paper with an MPC based on the differential solution of the LIP dynamics model, resulting in an efficient footstep planner. The simulation and experimental results of the humanoid robot Kuavo demonstrate that the proposed control framework achieves better tracking performance for desired walking speeds and responds quickly to external disturbances.

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.

Wheeled-legged humanoid robots combine the mobility of wheels with the versatility of legs, offering significant advantages for locomotion. This paper proposes a hierarchical control framework for such robots, using a two-wheeled inverted pendulum with a roll joint (TWIP-R) as a template model. The framework integrates a motion planner and a whole-body controller. The motion planner utilizes a linear quadratic regulator (LQR) to dynamically adjust the zero moment point (ZMP), counteracting centrifugal forces and enabling stable, dynamic movements. Meanwhile, the whole-body controller (WBC), based on centroidal momentum, solves an optimization problem via quadratic programming (QP) while incorporating constraints from the rolling contact condition of the wheels and the dynamics of the humanoid robot. This framework generates optimized torque commands, enabling feasible and stable motion even in dynamic scenarios. Simulations featuring challenging maneuvers, such as slalom, demonstrate its ability to enhance stability and dynamic performance compared to traditional two-wheeled inverted pendulum (TWIP)-based methods by leveraging a dynamics model with roll motion. This framework demonstrates the potential of wheeled-legged humanoid robots to achieve dynamic, stable, and efficient locomotion in a variety of scenarios.

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 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.

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 present a vision-based control and interaction framework for mobile robots, and describe its implementation in a legged amphibious robot. The control scheme enables the robot to navigate, follow targets of interest, and interact with human operators. The visual framework presented in this paper enables deployment of the vehicle in underwater environments along with a human scuba diver as the operator, without requiring any external tethered control. We present the current implementation of this framework in our particular family of underwater robots, with a focus on the underlying software and hardware infrastructure. We look at the practical issues pertaining to system implementation as it applies to our framework, from choice of operating systems to communication bus design. While our system has been effectively used in both open-ocean andclosed-water environments, we perform some quantitative measurements with an effort to analyze the responsiveness and robustness of the complete architecture.

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.

While recent advances in approaches for control of humanoid robot systems show highly promising results, consideration of fully integrated humanoid systems for solving complex tasks such as disaster response has only gained focus recently. In this work, a software framework for humanoid disaster response robots is introduced. It provides newcomers as well as experienced researchers in humanoid robotics a comprehensive system comprising open source packages for locomotion, manipulation, perception, world modeling, behavior control, and operator interaction. The system uses the Robot Operating System (ROS) as a middleware, which has emerged as a de-facto standard in robotics research in recent years. The described architecture and components allow for flexible interaction between operator(s) and robot from teleoperation to remotely supervised autonomous operation while considering bandwidth constraints. The components are self-­contained and can be used either in combination with others or standalone. They have been developed and evaluated during participation in the DARPA Robotics Challenge and their use for different tasks and parts of this competition are described.

Inclusive and seamless control framework for safe robot-mediated therapy for upper limbs rehabilitation

Theoretically, the operational space control framework can be regarded to be the most advanced control framework for redundant robots. However, in practice, the control performance of this framework is significantly degraded in the presence of model uncertainties and discretizing effects. Using the singular perturbation theory, this paper shows that the same model uncertainties can create different effects on the task space and joint space control performance. From the analysis, a multi-rate operational space control was proposed to minimize the effects of model uncertainties on the control performance and while maintaining the advantages of the original operational space framework. In this paper, we present a stability analysis of the multi-rate operational space control framework using the Lyapunov's direct method.

This paper proposes an online gain adaptation approach to enhance the robustness of whole-body control (WBC) framework for legged robots under unknown external force disturbances. Without properly accounting for external forces, the closed-loop control system incorporating WBC may become unstable, and therefore the desired task goals may not be achievable. To study the effects of external disturbances, we analyze the behavior of our current WBC framework via the use of both full-body and centroidal dynamics. In turn, we propose a way to adapt feedback gains for stabilizing the controlled system automatically. Based on model approximations and stability theory, we propose three conditions to ensure that the adjusted gains are suitable for stabilizing a robot under WBC. The proposed approach has four contributions. We make it possible to estimate the unknown disturbances without force/torque sensors. We then compute adaptive gains based on theoretic stability analysis incorporating the unknown forces at the joint actuation level. We demonstrate that the proposed method reduces task tracking errors under the effect of external forces on the robot. In addition, the proposed method is easy-to-use without further modifications of the controllers and task specifications. The resulting gain adaptation process is able to run in real-time. Finally, we verify the effectiveness of our method both in simulations and experiments using the bipedal robot Draco2 and the humanoid robot Valkyrie.

This paper proposes a concept learning-based approach to motion control for humanoid robots. In this approach, the motion control system is implemented with decision tree learner for the acquisition of balancing property of itself body and movement and depth first search technique for the motion control based on the knowledge concerning balance and stability in the motion. Some performance results by humanoid robot HOAP-1 is reported: stable and anti-tumble motions to stand up from a chair.

This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize ("H Infinity") synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.