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Rehabilitation is necessary for those with restricted arm mobility to enhance arm movement efficiency and offer dynamic stability. Recent research initiatives are aimed at supporting the rehabilitation of individuals with disabilities or injuries that limit arm movement. Accordingly, the RRSS (Revolute-Revolute-Spherical-Spherical) exoskeleton mechanism for circumduction motion is proposed in this study. The objective is to ascertain the parameters of the mechanism required to accomplish or approximate a sequence of prescribed circumduction positions. When a rigid body needs to operate according to a precise displacement sequence, this design is appropriate which is considered as a nonlinear kinostatic optimization problem. The problem can be formulated with constraints concerning driven link buckling, driver static torque, and driver elastic deflection. The suggested RRSS circumduction motion production process is used to simulate and analyze the workspace of the arm and shoulder as well as muscle activity. Significant changes in the reactions of the muscles, bones, and joints movement were noted during virtual testing of the suggested exoskeleton on a human arm. Some tremendous results of exoskeleton joints and human arm fusion were found. Some computations were made for the deltoid muscles, which control arm movement in the scapular plane, the FCU (Flexor Carpi Ulnaris) muscle, which is located in the forearm and controls hand flexion and adduction, and the caput breve, a short head of biceps brachii muscles. [ABSTRACT FROM AUTHOR]

AbstractA motion generation method for a planar four-bar mechanism without prescribed timing is proposed in this article. A characteristic of coupler points is found: for the coupler points of a four-bar mechanism in a standard installation position rotated by the corresponding input angle clockwise around the origin of the Cartesian coordinate system xOy, the generated points lie on the feature coupler circles. Next, based on this, the design process is divided into three steps. In the first step, the relative input angles, the relative coupler angles and five geometric parameters of the desired four-bar mechanism are optimized. In the second step, the basic dimensional types and initial input angle are determined, and then, the input angles are obtained. In the third step, the real size of the desired linkage mechanism is calculated. Because the dimension of the design variables in each step is exceptionally small, the motion synthesis method can yield design solutions with high precision in a short time. Six comparison examples are presented to demonstrate the efficacy of the proposed method. In addition, the method is used to design a robot for lower limb rehabilitation for application to the human foot. [ABSTRACT FROM AUTHOR]

This research paper presents a new Latin hypercube sampling method, aimed at enhancing its performance in quantifying uncertainty and reducing computation time. The new Latin hypercube sampling (LHS) method serves as a tool in reliability-based design optimization (RBDO). The quantification technique is termed LHSMDF (LHS with maximum diversity factor). The quantification techniques, such as Latin hypercube sampling (LHS), optimum Latin hypercube sampling (OLHS), and Latin hypercube sampling with maximum diversity factor (LHSMDF), are tested against mechanical components, including a circular shaft housing, a connecting rod, and a cantilever beam, to evaluate its comparative performance. Subsequently, the new method is employed as the basis of RBDO in the synthesis of a six-bar high-lift mechanism (HLM) example to enhance the reliability of the resulting mechanism compared to Monte Carlo simulation (MCS). The design problem of this mechanism is classified as a motion generation problem, incorporating angle and position of the flap as an objective function. The six-bar linkage is first adapted to be a high-lift mechanism (HLM), which is a symmetrical device of the aircraft. Furthermore, a deterministic design, without consideration of uncertainty, may lead to unacceptable performance during the manufacturing step due to link length tolerances. The techniques are combined with an efficient metaheuristic known as teaching–learning-based optimization with a diversity archive (ATLBO-DA) to identify a reliable HLM. Performance testing of the new LHSMDF reveals that it outperforms the original LHS and OLHS. The HLM problem test results demonstrate that achieving optimum HLM with high reliability necessitates precision without sacrificing accuracy in the manufacturing process. Moreover, it is suggested that the six-bar HLM could emerge as a viable option for developing a new high-lift device in aircraft mechanisms for the future. [ABSTRACT FROM AUTHOR]

Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory settings. When dealing with estimated skeleton data in real-world videos, such methods perform poorly due to the large variations across subjects and camera viewpoints. To address this issue, we introduce ViA, a novel View-Invariant Autoencoder for self-supervised skeleton action representation learning. ViA leverages motion retargeting between different human performers as a pretext task, in order to disentangle the latent action-specific 'Motion' features on top of the visual representation of a 2D or 3D skeleton sequence. Such 'Motion' features are invariant to skeleton geometry and camera view and allow ViA to facilitate both, cross-subject and cross-view action classification tasks. We conduct a study focusing on transfer-learning for skeleton-based action recognition with self-supervised pre-training on real-world data (e.g., Posetics). Our results showcase that skeleton representations learned from ViA are generic enough to improve upon state-of-the-art action classification accuracy, not only on 3D laboratory datasets such as NTU-RGB+D 60 and NTU-RGB+D 120, but also on real-world datasets where only 2D data are accurately estimated, e.g., Toyota Smarthome, UAV-Human and Penn Action. Code and models will be publicly available at https://walker-a11y.github.io/ViA-project. [ABSTRACT FROM AUTHOR]

The novelty of this work is that for the first time, spatial four-bar linkage motion generation and axode generation models have been applied for the design of a joint to replicate natural canine knee motion. This canine knee design method incorporates spatial four-bar Revolute-Revolute-Spherical-Spherical linkage motion generation and axode generation models to ultimately produce the rolling cam surfaces needed to produce natural spatial canine knee motion. This work presents the latest findings from an ongoing study where a transfemoral prosthetic knee design method is being applied to produce concept prosthetic knee joints for canines that replicate natural spatial knee motion during treadmill gait. To date, this study has considered three canine subjects, ranging from ages 1.1 to 10 years and treadmill speeds ranging from 1.3 to 2.9 km/h. [ABSTRACT FROM AUTHOR]

This study explores text conversion to motion using large language models like the generative pre-trained transformer (GPT) series and other artificial intelligence (AI) technologies like Large Language Model Meta AI (LLaMA) and the T5 model. It analyzes the structure and functions of these models in detail, comparing their effectiveness in generating motion videos. Various techniques like root mean square normalization (RMSNorm) and absolute encoding were employed to identify the best method for text-to-motion conversion. The findings indicate that the T5 model generates actions based on textual descriptions, especially in presenting critical motions and avoiding unnecessary movements, offering valuable insights for future advancements in motion generation technology. [ABSTRACT FROM AUTHOR]

Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks. [ABSTRACT FROM AUTHOR]

This paper proposes, analyzes, and evaluates a deep learning architecture based on transformers for generating sign language motion from sign phonemes (represented using HamNoSys: a notation system developed at the University of Hamburg). The sign phonemes provide information about sign characteristics like hand configuration, localization, or movements. The use of sign phonemes is crucial for generating sign motion with a high level of details (including finger extensions and flexions). The transformer-based approach also includes a stop detection module for predicting the end of the generation process. Both aspects, motion generation and stop detection, are evaluated in detail. For motion generation, the dynamic time warping distance is used to compute the similarity between two landmarks sequences (ground truth and generated). The stop detection module is evaluated considering detection accuracy and ROC (receiver operating characteristic) curves. The paper proposes and evaluates several strategies to obtain the system configuration with the best performance. These strategies include different padding strategies, interpolation approaches, and data augmentation techniques. The best configuration of a fully automatic system obtains an average DTW distance per frame of 0.1057 and an area under the ROC curve (AUC) higher than 0.94. [ABSTRACT FROM AUTHOR]

Abstract Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks.

This research paper presents a new Latin hypercube sampling method, aimed at enhancing its performance in quantifying uncertainty and reducing computation time. The new Latin hypercube sampling (LHS) method serves as a tool in reliability-based design optimization (RBDO). The quantification technique is termed LHSMDF (LHS with maximum diversity factor). The quantification techniques, such as Latin hypercube sampling (LHS), optimum Latin hypercube sampling (OLHS), and Latin hypercube sampling with maximum diversity factor (LHSMDF), are tested against mechanical components, including a circular shaft housing, a connecting rod, and a cantilever beam, to evaluate its comparative performance. Subsequently, the new method is employed as the basis of RBDO in the synthesis of a six-bar high-lift mechanism (HLM) example to enhance the reliability of the resulting mechanism compared to Monte Carlo simulation (MCS). The design problem of this mechanism is classified as a motion generation problem, incorporating angle and position of the flap as an objective function. The six-bar linkage is first adapted to be a high-lift mechanism (HLM), which is a symmetrical device of the aircraft. Furthermore, a deterministic design, without consideration of uncertainty, may lead to unacceptable performance during the manufacturing step due to link length tolerances. The techniques are combined with an efficient metaheuristic known as teaching–learning-based optimization with a diversity archive (ATLBO-DA) to identify a reliable HLM. Performance testing of the new LHSMDF reveals that it outperforms the original LHS and OLHS. The HLM problem test results demonstrate that achieving optimum HLM with high reliability necessitates precision without sacrificing accuracy in the manufacturing process. Moreover, it is suggested that the six-bar HLM could emerge as a viable option for developing a new high-lift device in aircraft mechanisms for the future.

Automatic gesture synthesis from speech is a topic that has attracted researchers for applications in remote communication, video games and Metaverse. Learning the mapping between speech and 3D full-body gestures is difficult due to the stochastic nature of the problem and the lack of a rich cross-modal dataset that is needed for training. In this paper, we propose a novel transformer-based framework for automatic 3D body gesture synthesis from speech. To learn the stochastic nature of the body gesture during speech, we propose a variational transformer to effectively model a probabilistic distribution over gestures, which can produce diverse gestures during inference. Furthermore, we introduce a mode positional embedding layer to capture the different motion speeds in different speaking modes. To cope with the scarcity of data, we design an intra-modal pre-training scheme that can learn the complex mapping between the speech and the 3D gesture from a limited amount of data. Our system is trained with either the Trinity speech-gesture dataset or the Talking With Hands 16.2M dataset. The results show that our system can produce more realistic, appropriate, and diverse body gestures compared to existing state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Manipulators motion planning faces new challenges as robots are increasingly used in dense, cluttered and dynamic environments. The recently proposed technique called Riemannian motion policies(RMPs) provides an elegant solution with clear mathematical interpretations to such challenging scenarios. It is based on differential geometry policies that generate reactive motions in dynamic environments with real-time performance. However, designing and combining RMPs is still a difficult task involving extensive parameter tuning, and typically seven or more RMPs need to be combined by using RMPflow to realize motions of a robot manipulator with more than 6 degrees-of-freedoms, where the RMPs parameters have to be empirically set each time. In this paper, we take a policy to decompose such complex policies into multiple learning modules based on reinforcement learning. Specifically, we propose a three-layer robot learning framework that consists of the basic-level, middle-level and top-level layers. At the basic layer, only two base RMPs i.e. target and collision avoidance are used to output reactive actions. At the middle-level layer, a hierarchical reinforcement learning approach is used to train an agent that automatically selects those RMPs and their parameters based on environmental changes and will be deployed at each joint. At the top-level layer, a multi-agent reinforcement learning approach trains all the joints with high-level collaborative policies to accomplish actions such as tracking a target and avoiding obstacles. With simulation experiments, we compare the proposed method with the baseline method and find that our method effectively produces superior actions and is better at avoiding obstacles, handling self-collisions, and avoiding singularities in dynamic environments. In addition, the proposed framework possesses higher training efficiency while leveraging the generalization ability of reinforcement learning to dynamic environments and improving safety and interpretability.

Data-driven grasp planning can generate anthropopathic grasps, providing controllers with robust and natural responses to environmental changes or morphological discrepancies. Mocap data, which is the widely used source of motion data, can provide high-fidelity dynamic motions. However, it is challenging for non-professionals to quickly get start and collect sufficient mocap data for grasp training. Furthermore, current grasp planning approaches suffer from limited adaptive abilities, and thus cannot be applied to objects of different shapes and sizes directly. In this paper, we propose the first framework, to the best of our knowledge, for fast and easy design of grasping controller with kinematic algorithms based on monocular 3D hand pose estimation and deep reinforcement learning, leveraging abundant and flexible videos of desired grasps. Specially, we first get original grasping sequences through 3D hand pose estimation from given monocular video fragments. Then, we reconstruct the motion sequences using data smoothing based on the peek clipping filter, and further optimize them using the CMA-ES (Covariance Matrix Adaptation Evolution Strategy). Finally, we integrate the reference motion with the adaptive grasping controller through deep reinforcement learning. Quantitative and qualitative results demonstrate that our framework is able to generate natural and stable grasps easily from monocular video demonstrations, added the adaptive ability to primitive objects of different shapes and sizes in the target object library. [ABSTRACT FROM AUTHOR]

Human motion is required in many simulation models. However, generating such a motion is quite complex and in industrial simulation cases represents an overhead that often cannot be accepted. There are several common file formats that are used nowadays for saving motion data that can be used in gaming engines or 3D editing software. Using such motion sets still requires considerable effort in creating logic for motion playing, blending, and associated object manipulation in the scene. Additionally, every action needs to be described with the motion designed for the target scene environment. This is where the Motion Model Units (MMU) concept was created. Motion Model Units represent a new way of transferring human motion data together with logic and scene manipulation capabilities between motion vendors and simulation platforms. The MMU is a compact software bundle packed in a standardized way, provides machine-readable capabilities and interface description that makes it interchangeable, and is adaptable to the scene. Moreover, it is designed to represent common actions in a task-oriented way, which allows simplifying the scenario creation to a definition of tasks and their timing. The underlying Motion Model Interface (MMI) has become an open standard and is currently usable in MOSIM framework, which provides the implementation of the standard for the Unity gaming engine and works on implementation for the Unreal Engine are under way. This paper presents two implementation examples for the MMU using direct C# programming, and using C# for Unity and MOSIM MMU generator as a helping tool. The key points required to build a working MMU are presented accompanied by an open-source code that is available for download and experimenting.

Inspired by the success of WaveNet in multi-subject speech synthesis, we propose a novel neural network based on causal convolutions for multi-subject motion modeling and generation. The network can capture the intrinsic characteristics of the motion of different subjects, such as the influence of skeleton scale variation on motion style. Moreover, after fine-tuning the network using a small motion dataset for a novel skeleton that is not included in the training dataset, it is able to synthesize high-quality motions with a personalized style for the novel skeleton. The experimental results demonstrate that our network can model the intrinsic characteristics of motions well and can be applied to various motion modeling and synthesis tasks. [ABSTRACT FROM AUTHOR]

Abstract We propose a novel robotic system that combines both a reliable programming-based approach and a highly generalizable learning-based approach. How to design and implement a series of tasks in an atypical environment is a challenging issue. If all tasks are implemented using a programming-based approach, the development costs will be huge. However, if a learning-based approach is used, reliability is an issue. In this paper, we propose novel design guidelines that focus on the respective advantages of programming-based and learning-based approaches and select them so that they complement each other. We use a program-based approach for motions that is rough behavior and a learning-based approach for motion that is required complex interaction between robot and object of robot tasks and are difficult to achieve with a program. Our learning approach can easily and rapidly accomplish a series of tasks consisting of various motions because it does not require a computational model of an object to be designed in advance. We demonstrate a series of tasks in which randomly arranged parts are assembled using an actual robot.

This paper proposes, analyzes, and evaluates a deep learning architecture based on transformers for generating sign language motion from sign phonemes (represented using HamNoSys: a notation system developed at the University of Hamburg). The sign phonemes provide information about sign characteristics like hand configuration, localization, or movements. The use of sign phonemes is crucial for generating sign motion with a high level of details (including finger extensions and flexions). The transformer-based approach also includes a stop detection module for predicting the end of the generation process. Both aspects, motion generation and stop detection, are evaluated in detail. For motion generation, the dynamic time warping distance is used to compute the similarity between two landmarks sequences (ground truth and generated). The stop detection module is evaluated considering detection accuracy and ROC (receiver operating characteristic) curves. The paper proposes and evaluates several strategies to obtain the system configuration with the best performance. These strategies include different padding strategies, interpolation approaches, and data augmentation techniques. The best configuration of a fully automatic system obtains an average DTW distance per frame of 0.1057 and an area under the ROC curve (AUC) higher than 0.94.

Human motion is required in many simulation models. However, generating such a motion is quite complex and in industrial simulation cases represents an overhead that often cannot be accepted. There are several common file formats that are used nowadays for saving motion data that can be used in gaming engines or 3D editing software. Using such motion sets still requires considerable effort in creating logic for motion playing, blending, and associated object manipulation in the scene. Additionally, every action needs to be described with the motion designed for the target scene environment. This is where the Motion Model Units (MMU) concept was created. Motion Model Units represent a new way of transferring human motion data together with logic and scene manipulation capabilities between motion vendors and simulation platforms. The MMU is a compact software bundle packed in a standardized way, provides machine-readable capabilities and interface description that makes it interchangeable, and is adaptable to the scene. Moreover, it is designed to represent common actions in a task-oriented way, which allows simplifying the scenario creation to a definition of tasks and their timing. The underlying Motion Model Interface (MMI) has become an open standard and is currently usable in MOSIM framework, which provides the implementation of the standard for the Unity gaming engine and works on implementation for the Unreal Engine are under way. This paper presents two implementation examples for the MMU using direct C# programming, and using C# for Unity and MOSIM MMU generator as a helping tool. The key points required to build a working MMU are presented accompanied by an open-source code that is available for download and experimenting. [ABSTRACT FROM AUTHOR]

We propose a muscle‐driven motion generation approach to realize virtual human motion with user interaction and higher fidelity, which can address the problem that the joint‐driven fails to reflect the motion process of the human body. First, a simplified virtual human musculoskeletal model is built based on human biomechanics. Then, a hierarchical policy learning framework is constructed including motion tracking layer, SPD controller and muscle control layer. The motion tracking layer is responsible for mimicking reference motion and completing control command, using proximal policy optimization to train the policy; the muscle control layer is aimed to minimize muscle energy consumption and train the policy based on supervised learning; the SPD controller acts as a link between the two layers. At the same time, we integrate the curriculum learning to improve the efficiency and success rate of policy training. Simulation experiments show that the proposed approach can use motion capture data and pose estimation data as reference motions to generate better and more adaptable motions. Furthermore, the virtual human has the ability to respond to the user control command during the motion, and can complete the target task successfully. [ABSTRACT FROM AUTHOR]

The issue of human motion prediction aimed to predict sequences of joint positions or joint rotations of human skeleton has recently grown in importance. The Recurrent Neural Network is widely applied on the sequence prediction problems which has been proved effective. However it is difficult to train the model with human skeleton data of multi-dimensional as input, which would do naive forecasting to produce motionless sequence. To address the problem, it is a consensus that additional information will help to improve the accuracy, thus the angular velocities are extracted from the joint rotations as the input to enhance the prediction. Further more, this work adopts proper strategies on the basis of a stacked Gated Recurrent Unit network and verify them on the human motion prediction task. The experimental results show that our network outperforms the state-of-art on the short-term prediction task, and generates plausible action sequences in a relatively long period of time. [ABSTRACT FROM AUTHOR]

We propose a novel robotic system that combines both a reliable programming-based approach and a highly generalizable learning-based approach. How to design and implement a series of tasks in an atypical environment is a challenging issue. If all tasks are implemented using a programming-based approach, the development costs will be huge. However, if a learning-based approach is used, reliability is an issue. In this paper, we propose novel design guidelines that focus on the respective advantages of programming-based and learning-based approaches and select them so that they complement each other. We use a program-based approach for motions that is rough behavior and a learning-based approach for motion that is required complex interaction between robot and object of robot tasks and are difficult to achieve with a program. Our learning approach can easily and rapidly accomplish a series of tasks consisting of various motions because it does not require a computational model of an object to be designed in advance. We demonstrate a series of tasks in which randomly arranged parts are assembled using an actual robot. [ABSTRACT FROM AUTHOR]

State-dependent dynamical systems (DSs) offer adaptivity, reactivity, and robustness to perturbations in motion planning and physical human–robot interaction tasks. Learning DS-based motion plans from non-linear reference trajectories is an active research area in robotics. Most approaches focus on learning DSs that can (i) accurately mimic the demonstrated motion, while (ii) ensuring convergence to the target, i.e., they are globally asymptotically (or exponentially) stable. When subject to perturbations, a compliant robot guided with a DS will continue following the next integral curves of the DS towards the target. If the task requires the robot to track a specific reference trajectory, this approach will fail. To alleviate this shortcoming, we propose the locally active globally stable DS (LAGS-DS), a novel DS formulation that provides both global convergence and stiffness-like symmetric attraction behaviors around a reference trajectory in regions of the state space where trajectory tracking is important. This allows for a unified approach towards motion and impedance encoding in a single DS-based motion model, i.e., stiffness is embedded in the DS. To learn LAGS-DS from demonstrations we propose a learning strategy based on Bayesian non-parametric Gaussian mixture models, Gaussian processes, and a sequence of constrained optimization problems that ensure estimation of stable DS parameters via Lyapunov theory. We experimentally validated LAGS-DS on writing tasks with a KUKA LWR 4+ arm and on navigation and co-manipulation tasks with iCub humanoid robots. [ABSTRACT FROM AUTHOR]

Exoskeleton robots are electrically, pneumatically, or hydraulically actuated devices that externally support the bones and cartilage of the human body while trying to mimic the human movement capabilities and augment muscle power. The lower extremity exoskeleton device may support specific human joints such as hip, knee, and ankle, or provide support to carry and balance the weight of the full upper body. Their assistive functionality for physically-abled and disabled humans is demanded in medical, industrial, military, safety applications, and other related fields. The vision of humans walking with an exoskeleton without external support is the prospect of the robotics and artificial intelligence working groups. This paper presents a survey on the design and control of lower extremity exoskeletons for bipedal walking. First, a historical view on the development of walking exoskeletons is presented and various lower body exoskeleton designs are categorized in different application areas. Then, these designs are studied from design, modeling, and control viewpoints. Finally, a discussion on future research directions is provided. [ABSTRACT FROM AUTHOR]

In four-bar motion generation, linkage dimensions are calculated to achieve prescribed coupler positions. This work investigates the sensitivity of four-bar coupler motion sequences by analyzing position error margins and implementing a genetic algorithm (GA) for fourbar motion generation. As an application, the four-bar hood mechanism in the Plymouth Satellite mid-size automobile is considered. The results of the sensitivity analysis show that the mean average structural error between the prescribed and achieved hood positions is less than 0.015in for any quadratic analysis. In each demonstration, the proposed method consistently produced results that are scalable for 360° within a margin error of 0.06in. [ABSTRACT FROM AUTHOR]

Deep learning-based visuomotor control for manipulation are studied recently, which uses a neural network to learn the mapping from images to robotic actions directly. Most previous methods assume there is only one target object in the image. When multiple target objects are present, they are normally regarded as multi-task problems. One-hot vectors are often used to denote different tasks in the visuomotor framework. Nonetheless, these one-hot vector-based methods are easily disturbed and non-extendable. In this paper, a location instruction is used to guide the robot to generate different trajectories in the situation with multiple targets. The proposed framework mainly consists of three modules: the AutoEncoder (AE) network, the motion generation network and the location detection network. AE tries to process the perception information into small-scale feature maps. The location detection network gives the pixel coordinates of the center of the target object in the image. The motion generation network combines the location instruction and the preprocessed perception information and generates an entire motion trajectory to finish the specified manipulation task. For the object stacking tasks with distractors, the proposed method could obtain a success rate of 98%, while the one-hot vector-based method only has a success rate of 56%.

The objective in planar four-bar motion generation is to calculate the linkage dimensions required to achieve a group of prescribed coupler positions. For a given planar four-bar motion generator however, the rolling motion of its moving centrode over its fixed centrode will replicate its coupler motion. The curvature of the fixed and moving centrodes can be incorporated as contact surfaces in a rolling cam system to replicate the coupler motion of a planar four-bar motion generator. While the advantages that cam-based systems have over four-bar linkage-based systems are application-specific, some of the advantages relate to system workspace (having a more compact design), structural soundness (having a greater load bearing capacity) and design simplicity (utilizing fewer components). While design methods for cam-based systems have been developed for conventional non-adjustable four-bar motion generators, no such method has been presented for the adjustable planar four-bar motion generator. Given the design advantages associated with cam-based systems along with an absence of published work in the design of cam-based systems for adjustable motion generation, an opportunity exists to contribute to the body of knowledge in this area. This work presents for the first time a design method for a dual cam system to replicate the coupler motion of an adjustable planar four-bar motion generator. This design method incorporates an optimization model for defect-free adjustable planar four-bar motion generation. The adjustments considered in the optimization model are adjustable crank and follower moving pivots with adjustable crank and follower link lengths. This design method also incorporates centrode generation equations for the adjustable planar four-bar motion generator. As an example, the centrodes generated for a calculated adjustable motion generator were incorporated into a computer-aided design model to produce a concept dual cam system.