Human-like Motion Research Articles

Design and experiments of a humanoid torso based on biological features

Among the components of a humanoid robot, a humanoid torso plays a vital role in supporting a humanoid robot to complete the desired motions. In this paper, a new LARMbot torso is developed to obtain better working performance based on biological features. By analyzing the anatomy of a human torso and spine, a parallel cable-driven mechanism is proposed to actuate the whole structure using two servo motors and two pulleys. Analysis is conducted to evaluate the properties of the proposed parallel cable-driven mechanism. A closed-loop control system is applied to control the whole LARMbot torso. Experiments are performed using the manufactured prototype in three modes to evaluate the characterizations of the proposed design. Results show that the proposed LARMbot can complete the desired motions properly, including two general human-like motions and a full rotation motion. When completing two general human-like motions, the maximum bending angle is 40 degrees. The maximum cable tension is 0.68 N, and the maximum required power is 18.3 W. In full rotation motion, the maximum bending angle is 30 degrees. The maximum cable tension is 0.75 N, and the maximum power required is 20.5 W. The proposed design is simplified and lightweight, with low energy consumption and flexible spatial motion performance that can meet the requirements of the humanoid robot torso's application in complex scenarios and commercial requirements.

Effects of Human-Like Characteristics in Sampling-Based Motion Planning on the Legibility of Robot Arm Motions

Conveying the intended goal of a robot arm motion has been shown to increase the quality of human-robot collaboration drastically. To this end, optimization-based approaches have been proposed that optimize the legibility of a robot’s motion. However, they are limited in two ways. First, they are typically not validated in environments with obstacles and narrow passages that require collision-free motion planning. Second, they do not consider the influence of the anthropomorphization process that might be caused by a human-like motion or appearance of the arm. This leads to the question of to what extent the legibility of motions is influenced by these factors. In this work, we study the influence of our previously proposed human-likeness function on the legibility of robot arm motions in the context of sampling-based motion planning. We evaluate it against three other motions: a functional motion, a recorded expert motion, and a legible motion based on a heuristic for the observer’s prediction. For this, we conduct an extensive user study with 94 participants. In contrast to other works, we manipulate the robot’s appearance and the complexity of the environment. We thus provide insights into how the legibility of robot motions is influenced by human-like characteristics in motion, appearance and restricting workspace conditions. The complete stimulus material used in this work is provided at https://mytuc.org/zpvt .

Development of a Cable-Driven Bionic Spherical Joint for a Robot Wrist.

Wrist movements play a crucial role in upper-limb motor tasks. As prosthetic and robotic hand technologies have evolved, increasing attention has been focused on replicating the anatomy and functionality of the wrist. Closely imitating the biomechanics and movement mechanisms of human limbs is expected to enhance the overall performance of bionic robotic hands. This study presents the design of a tendon-driven bionic spherical robot wrist, utilizing two pairs of cables that mimic antagonist muscle pairs. The cables are actuated by pulleys driven by servo motors, allowing for two primary wrist motions: flexion-extension and ulnar-radial deviation. The performance Please confirm if the "1583 Iiyama" is necessary. Same as belowof the proposed robot wrist is validated through manipulation experiments using a prototype, demonstrating its capability to achieve a full range of motion for both ulnar and radial deviation. This wrist mechanism is expected to be integrated into robotic systems, enabling greater flexibility and more human-like movement capabilities.

Modeling, Simulation, and Kinematic Validation of Transfemoral Prosthetic Mechanism With Ankle Varus–Valgus Characteristic

Abstract Inspired by the kinesiology of human bionic joints, a transfemoral prosthetic mechanism based on a functional structure of parallel mechanism is developed for the transfemoral amputees. The walking interactive simulation is implemented based on human-prosthesis modeling to verify the kinematics of the designed prosthetic mechanism, as well as to explore compatibility between the amputees and prosthesis. Then, simulation-based prosthetic optimization is performed to pursue an optimized human-prosthesis model with economic metabolic consumption while eliminating compatibility errors including the joints' misalignment error between the affected limb and healthy limb, and the assembly error between human and prosthesis, so that the potential physical health problems can be avoided efficiently. This method is valuable for the optimal design of interactive rehabilitation robots. Finally, a developed proportional-integral-derivative-based (PID-based) finite-state machine (FSM) strategy is used, and the kinematic validation is carried out. The results show that the designed prosthesis possesses ankle varus–valgus characteristic, and it has a high human-like motion accuracy due to the FSM control can track prosthetic motion in each gait event. What's more, the prosthetic optimization can be an efficient method to enhance the biomechanical performance of human-prosthetic model so that the amputees have a more natural and symmetry gait.

Design and Performance Analysis of Robotic Vertebral-Disc Unit with Cable-Driven Mechanism.

The humanoid torso is crucial for the overall performance of a humanoid robot. Developing an effective humanoid spine is essential for enhancing this mechanism. This paper introduces a one-vertebral-disc unit inspired by human spine anatomy. A prototype was created using 3D-printed parts and commercially available components. Two general human-like motions are achieved using two servo motors and two pulleys, reducing the number of servo motors needed. The results indicate that a one-vertebral-disc unit can bend up to 15 degrees. The proposed mechanism functions effectively and successfully mimics human movements. It holds potential for integration into humanoid torsos, enabling efficient performance in human-like tasks in the future.

Joint angle synergy-based humanoid robot motion generation with fascia-inspired nonlinear constraints

AbstractWhen generating simultaneous joint movements of a humanoid with multiple degrees of freedom to replicate human-like movements, the approach of joint synergy can facilitate the generation of whole-body robotic movement with a reduced number of control inputs. However, the trade-off of minimizing control inputs and keeping characteristics of movements makes it difficult to improve movement performance in a simple control manner. In this paper, we introduce an approach by connecting and constraining these joints. It is inspired by the fascia network of the human body, which constrains the whole-body movements of a human. Compared to when only joint synergy is used, the effectiveness of the proposed method is verified by calculating the errors of joint positions of generated movements and human movements. The paper provides a detailed exploration of the proposed method, presenting simulation-experimental results that affirm its effectiveness in generated movements that closely resemble human movements. Furthermore, we provide one possible method on how these concepts can be implemented in actual robotic hardware, offering a pathway to improve movement control in humanoid robots within their mechanical limitations.

Generalizable and precise control based on equilibrium-point hypothesis for musculoskeletal robotic system

PurposeThe purpose of this study is realizing human-like motions and performance through musculoskeletal robots and brain-inspired controllers. Human-inspired robotic systems, owing to their potential advantages in terms of flexibility, robustness and generality, have been widely recognized as a promising direction of next-generation robots.Design/methodology/approachIn this paper, a deep forward neural network (DFNN) controller was proposed inspired by the neural mechanisms of equilibrium-point hypothesis (EPH) and musculoskeletal dynamics.FindingsFirst, the neural mechanism of EPH in human was analyzed, providing the basis for the control scheme of the proposed method. Second, the effectiveness of proposed method was verified by demonstrating that equilibrium states can be reached under the constant activation signals. Finally, the performance was quantified according to the experimental results.Originality/valueBased on the neural mechanism of EPH, a DFNN was crafted to simulate the process of activation signal generation in human motion control. Subsequently, a bio-inspired musculoskeletal robotic system was designed, and the high-precision target-reaching tasks were realized in human manner. The proposed methods provide a direction to realize the human-like motion in musculoskeletal robots.

Pillar electrodes embedded in the skeletal muscle tissue for selective stimulation of biohybrid actuators with increased contractile distance.

Electrodes are crucial for controlling the movements of biohybrid robots, but their external placement outside muscle tissue often leads to inefficient and non-selective stimulation of nearby biohybrid actuators. To address this, we propose embedding pillar electrodes within the skeletal muscle tissue, resulting in enhanced contraction of the target muscle without affecting the neighbor tissue with a 4 mm distance. We use finite element method simulations to establish a selectivity model, correlating the VIE(volume integration of electric field intensity within muscle tissue) with actual contractile distances under different amplitudes of electrical pulses. The simulated selective index closely aligns with experimental results, showing the potential of pillar electrodes for effective and selective biohybrid actuator stimulation. In experiments, we validated that the contractile distance and selectivity achieved with these pillar electrodes exceed conventional Au rod electrodes. This innovation has promising implications for building biohybrid robots with densely arranged muscle tissue, ultimately achieving more human-like movements. Additionally, our selectivity model offers valuable predictive tools for assessing electrical stimulation effects with different electrode designs.

Coupling intention and actions of vehicle–pedestrian interaction: A virtual reality experiment study

The interactions between vehicles and pedestrians are complex due to their interdependence and coupling. Understanding these interactions is crucial for the development of autonomous vehicles, as it enables accurate prediction of pedestrian crossing intentions, more reasonable decision-making, and human-like motion planning at unsignalized intersections. Previous studies have devoted considerable effort to analyzing vehicle and pedestrian behavior and developing models to forecast pedestrian crossing intentions. However, these studies have two limitations. First, they mainly focus on investigating variables that explain pedestrian crossing behavior rather than predicting pedestrian crossing intentions. Moreover, some factors such as age, sensation seeking and social value orientation, used to establish decision-making models in these studies are not easily accessible in real-world scenarios. In this paper, we explored the critical factors influencing the decision-making processes of human drivers and pedestrians respectively by using virtual reality technology. To do this, we considered available kinematic variables and analyzed the internal relationship between motion parameters and pedestrian behavior. The analysis results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Furthermore, a mathematical relationship between a pedestrian’s intention and kinematic variables is established for the first time, which can help dynamically assess when pedestrians desire to cross. Finally, the results obtained in driver-yielding behavior analysis provide valuable insights for autonomous vehicle decision-making and motion planning.

Development of a Hand-Fan-Shaped Arm and a Model Predictive Controller for Leg Crossing, Walking, and One-Legged Balancing of a Wheeled-Bipedal Jumping Robot

Bipedal walking robots are advancing research by performing challenging human-like movements in complex environments. Particularly, wheeled-bipedal robots are used in many indoor environments by overcoming the speed and maneuverability limitations of bipedal walking robots without wheels. However, when both wheels lose contact with the ground, maintaining lateral balance becomes challenging, and there is an increased risk of toppling over. Furthermore, utilizing robotic arms similar to human arms, in addition to wheel-based balance, could enable more precise and stable control. In this paper, we develop a wheeled-bipedal robot that is able to jump and drive while also being able to cross its legs and balance on one leg (the OLEBOT). The OLEBOT is designed with a hand-fan-shaped end-effector capable of generating compensatory torque. By tilting the hand-fan-shaped end-effector in the opposite direction, OLEBOT achieves pitch control and single-leg balance. In jumping scenario, it imitates the arm movements of a person performing stationary high jumps, while utilizing a cam-based leg joint system to boost jump height. In addition, this paper develops a control architecture based on model predictive control (MPC) to ensure stable posture in driving, jumping, and one-legged balancing scenarios for OLEBOT. Finally, the experimental results demonstrate that OLEBOT is capable of maintaining a stable posture using a wheeled-bipedal system and achieving balance in a one-legged stance.

Sensorimotor Control Using Adaptive Neuro-Fuzzy Inference for Human-Like Arm Movement

In this study, a sensorimotor controller is designed to characterize the required muscle force to enable a robotics system to perform a human-like circular movement. When the appropriate muscle internal forces are chosen, the arm end-point tracks the desired path via joint-space feedback. An objective function of the least-change rate of muscle forces is determined to find suitable feedback gains. The parameter defining the muscle force is then treated as a learning parameter through an adaptive neuro-fuzzy inference system, incorporating the rate of change of muscle forces. In experimental section, the arm motion of healthy subjects is captured using the inertial measurement unit sensors, and then the image of the drawn path is processed. The inertial measurement unit sensors detect each segment motion’s orientation using quaternions, and the image is employed to identify the exact end-point position. Experimental data on arm movement are then utilized in the control parameter computation. The proposed brain–motor control mechanism enhances motion performance, resulting in a more human-like movement.

Robust Learning from Demonstration Based on GANs and Affine Transformation

Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expert demonstrations, offering versatility and an intuitive programming approach. However, many existing LfD methods encounter issues such as convergence failure and lack of generalization ability. In this paper, we propose: (1) a generative adversarial network (GAN)-based model with multilayer perceptron (MLP) architecture, coupled with a novel loss function designed to mitigate convergence issues; (2) an affine transformation-based generalization method aimed at enhancing LfD tasks by improving their generalization performance; (3) a data preprocessing method tailored to facilitate deployment on robotics platforms. We conduct experiments on a UR5 robotic platform tasked with handwritten digit recognition. Our results demonstrate that our proposed method significantly accelerates generation speed, achieving a remarkable processing time of 23 ms, which is five times faster than movement primitives (MPs), while preserving key features from demonstrations. This leads to outstanding convergence and generalization performance.

Partition and planning: A human-like motion decision for UAV in trap environment

Partition and planning: A human-like motion decision for UAV in trap environment

Human-like acceleration and deceleration control of a robot astronaut floating in a space station

The acceleration and deceleration (AD) motions are the basic motion modes of robot astronauts moving in a space station. Controlling the locomotion of the robot astronaut is very challenging due to the strong nonlinearity of its complex multi-body dynamics in a gravity-free environment. However, after training, humans can move well in space stations by pushing the bulkhead, and the motion mechanism of humans is a good reference for robot astronauts. The contribution of this study is modeling the human AD motion in a microgravity environment and proposing a human-like control method for robot astronauts moving in space stations. Specifically, the movement and contact force data of the human body during AD motion were collected on an air-floating platform. Through human AD modeling analysis, the mechanism of human motion is discovered, and semi-sinusoidal primitives of contact forces are proposed for AD motion. Then, a dynamic guidance model of human AD motion is built to complete motion planning under contact constraints, which is used as the expected model for the AD control of robot astronauts. Benefiting from the force primitives, accurate and safe planning of human-like AD motion can be completed. The characteristics and mechanism of human AD motion have been analyzed from the perspective of optimization. Lastly, based on the proposed dynamic guidance model, the AD motion policy is mapped to the robot astronaut system via a system control method based on the equivalent mapping of dynamic responses (force, velocity and pose). Through comparative analysis with real human motion data and simulation results under different conditions, the proposed AD control method can achieve human-like motion efficiently and stably. Even when confronted with errors in the robot’s contact velocities and inertia parameters, the method can significantly reduce the motion errors while ensuring stability.

Tracing curves in the plane: Geometric-invariant learning from human demonstrations.

The empirical laws governing human-curvilinear movements have been studied using various relationships, including minimum jerk, the 2/3 power law, and the piecewise power law. These laws quantify the speed-curvature relationships of human movements during curve tracing using critical speed and curvature as regressors. In this work, we provide a reservoir computing-based framework that can learn and reproduce human-like movements. Specifically, the geometric invariance of the observations, i.e., lateral distance from the closest point on the curve, instantaneous velocity, and curvature, when viewed from the moving frame of reference, are exploited to train the reservoir system. The artificially produced movements are evaluated using the power law to assess whether they are indistinguishable from their human counterparts. The generalisation capabilities of the trained reservoir to curves that have not been used during training are also shown.

Anthropomorphic Soft Hand: Dexterity, Sensing, and Machine Learning

Humans possess dexterous hands that surpass those of other animals, enabling them to perform intricate, complex movements. Soft hands, known for their inherent flexibility, aim to replicate the functionality of human hands. This article provides an overview of the development processes and key directions in soft hand evolution. Starting from basic multi-finger grippers, these hands have made significant advancements in the field of robotics. By mimicking the shape, structure, and functionality of human hands, soft hands can partially replicate human-like movements, offering adaptability and operability during grasping tasks. In addition to mimicking human hand structure, advancements in flexible sensor technology enable soft hands to exhibit touch and perceptual capabilities similar to humans, enhancing their performance in complex tasks. Furthermore, integrating machine learning techniques has significantly promoted the advancement of soft hands, making it possible for them to intelligently adapt to a variety of environments and tasks. It is anticipated that these soft hands, designed to mimic human dexterity, will become a focal point in robotic hand development. They hold significant application potential for industrial flexible gripping solutions, medical rehabilitation, household services, and other domains, offering broad market prospects.

Design and Testing of a New LARMbot Torso

Abstract Developing a robotic torso mechanism is crucial in replicating human mobility in humanoid robots. Previous research has presented the LARMbot humanoid's torso as a potential solution, which has now been improved with a novel design proposed in this paper. We conduct a kinematic analysis on the proposed LARMbot torso design, which is developed through an analysis of the human spine's structure. A kinematic model of the novel design is proposed using piecewise constant curvature to capture the relationship between input and output parameters and to represent the workspace. A prototype is utilized for conducting experiments to test the human-like movements of the novel mechanism. The outcomes show that the proposed LARMbot torso architecture can enhance performance. The generated humanoid torso prototype has the capacity to bend approximately 30 deg, and as such, it can be expected to make humanoid robots achieve human-like motion and tasks.

An Upper Limb Exoskeleton Motion Generation Algorithm Based on Separating Shoulder and Arm Motion.

Many rehabilitation exoskeletons have been used in the field of stroke rehabilitation. Generating human-like motion is necessary for exoskeletons to help patients perform activities of daily living (ADL) while maintaining interaction quality and ergonomics. However, most of the current motion generation algorithms utilize inverse kinematics (IK) to solve the final configuration before generation, and do not consider the movement of shoulder girdle. Separately considering the shoulder girdle motion and arm motion, this paper proposes an algorithm integrated IK to generate human-like motion. The arm moves towards the target with a bell-shaped velocity in the absence of the final configuration, and the shoulder girdle maintain natural passive motion. Moreover, the generated motion can be mapped to the configuration space of exoskeletons. Compared with the experimental data collected using a motion capture system, the values of RMSE and HPDI of the generated wrist trajectory in the task space are within 0.2 and 0.17, respectively, while those of RMSE in the joint space are within 15 deg, which demonstrates the human-like nature of the generated motion.

Design and Control of Continuous Jumping Gaits for Humanoid Robots Based on Motion Function and Reinforcement Learning

Continuous jumping of humanoid robots is a challenging problem that requires appropriate gait and force control for the robot to achieve. Reinforcement learning (RL) has been applied to the motion control of humanoid robots and has achieved significant progress. However, traditional RL control methods primarily rely on designing reward signals to generate human-like movements, and it requires developing complex reward signals for continuous jumping, resulting in limited actions and suboptimal jumping performance. To address this issue, we have designed motion functions for humanoid continuous jumping and incorporated them into the RL framework to obtain desired human-like continuous jumping actions. Furthermore, two different jumping gaits are designed to investigate the jumping effects produced by different jumping functions: bent-knee jumping (BJ) and straight-knee jumping (SJ). BJ is a typical jumping style where the legs flex during the jumping process to accumulate energy, enabling higher explosive power. SJ is more suitable for tasks requiring quick responses, as the legs remain upright during the jump, providing faster reaction speed. The performance in continuous jumping tasks has been evaluated by comparing the effects of training with different jumping functions. This research has particular guidance for improving the jumping ability of robots and enhancing the stability and efficiency of humanoid continuous jumping.

Design of Virtual Anchor Based on 3Dmax

With the rapid development of virtual reality and live streaming technologies, virtual anchors have become increasingly popular in recent years. In this paper, we propose a design method of virtual anchors based on 3DMAX. Through the use of modeling, rigging, and animation techniques, virtual anchors with realistic appearances and human-like movements can be created. We also explore the application of machine learning technologies in improving the interaction between virtual anchors and users. In addition, we provide a case study on the design and implementation of a virtual anchor for a popular live streaming platform. Our results show that the use of 3DMAX in virtual anchor design can greatly enhance user engagement and improve the overall user experience.Virtual anchor design technology based on 3DMAX is a highly complex design work, which requires designers to have a variety of skills and creative capabilities, and needs to fully consider the needs of the audience and the development trend of the industry. Designers should also have certain cultural accumulation and creative ability, and be able to design an attractive and valuable virtual anchor image from the perspective of the audience.This paper analyzes the production and design of the current virtual anchors, in order to provide some reference significance for the production, operation and commercial realization of the virtual anchors in the future.