Motion Primitives Research Articles

An Approach to Realize Generalized Optimal Motion Primitives Using Physics Informed Neural Networks

Abstract Autonomous manipulation is a challenging problem in field robotics due to uncertainty in object properties, constraints, and coupling phenomenon with robot control systems. Humans learn motion primitives over time to effectively interact with the environment. We postulate that autonomous manipulation can be enabled by basic sets of motion primitives as well, but do not necessitate mimicking human motion primitives. This work presents an approach to generalized optimal motion primitives using physics-informed neural networks. Our simulated and experimental results demonstrate that optimality is notionally maintained where the mean maximum observed final position percent error was 0.564% and the average mean error for all the trajectories was 1.53%. These results indicate that notional generalization is attained using a physics-informed neural network approach that enables near optimal real-time adaptation of primitive motion profiles.

Trajectory Augmentation Method Based on Dynamic Movement Primitives

Trajectory Augmentation Method Based on Dynamic Movement Primitives

Real-Time Trajectory Planning and Effectiveness Analysis of Intercepting Large-Scale Invading UAV Swarms Based on Motion Primitives

Real-Time Trajectory Planning and Effectiveness Analysis of Intercepting Large-Scale Invading UAV Swarms Based on Motion Primitives

Self‐supervised vessel trajectory segmentation via learning spatio‐temporal semantics

AbstractThe study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self‐supervised vessel trajectory segmentation method (SS‐VTS), which segments VTs based on their inherent spatio‐temporal semantics. SS‐VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi‐dimensional feature sequence of the VTs using self‐supervised learning. Finally, spatio‐temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS‐VTS achieves state‐of‐the‐art segmentation results compared to seven baseline methods.

Learning Underwater Intervention Skills Based on Dynamic Movement Primitives

Improving the autonomy of underwater interventions by remotely operated vehicles (ROVs) can help mitigate the impact of communication delays on operational efficiency. Currently, underwater interventions for ROVs usually rely on real-time teleoperation or preprogramming by operators, which is not only time-consuming and increases the cognitive burden on operators but also requires extensive specialized programming. Instead, this paper uses the intuitive learning from demonstrations (LfD) approach that uses operator demonstrations as inputs and models the trajectory characteristics of the task through the dynamic movement primitive (DMP) approach for task reproduction as well as the generalization of knowledge to new environments. Unlike existing applications of DMP-based robot trajectory learning methods, we propose the underwater DMP (UDMP) method to address the problem that the complexity and stochasticity of underwater operational environments (e.g., current perturbations and floating operations) diminish the representativeness of the demonstrated trajectories. First, the Gaussian mixture model (GMM) and Gaussian mixture regression (GMR) are used for feature extraction of multiple demonstration trajectories to obtain typical trajectories as inputs to the DMP method. The UDMP method is more suitable for the LfD of underwater interventions than the method that directly learns the nonlinear terms of the DMP. In addition, we improve the commonly used homomorphic-based teleoperation mode to heteromorphic mode, which allows the operator to focus more on the end-operation task. Finally, the effectiveness of the developed method is verified by simulation experiments.

Motion primitives and sample-based trajectory planning for fixed-wing unmanned aircraft

Navigating fixed-wing unmanned aircraft presents significant challenges, primarily stemming from the intricate dynamics involved and the demand for rapid planning. In this paper, we introduce an efficient approach that concurrently addresses nonlinear control and trajectory planning challenges. To tackle the control problem, we introduce a computationally efficient motion primitive that effectively maps feasible targets to control inputs. As for trajectory planning, we propose an enhanced, rapidly exploring, random tree algorithm capable of generating high-quality trajectories with minimal exploration. Our primary motivation is to seamlessly integrate the introduced motion primitive with a sample-based trajectory-planning technique, providing a comprehensive solution to the intricate challenges posed by nonlinear control and trajectory planning for fixed-wing unmanned aircraft. Experimental results showcase our approach’s superiority, delivering enhanced control precision, faster planning speeds, and improved planning quality.

Kinematic and neuromuscular characterization of cognitive involvement in gait control in healthy young adults.

The signature of cognitive involvement in gait control has rarely been studied using both kinematic and neuromuscular features. The present study aimed to address this gap. Twenty-four healthy young adults walked on an instrumented treadmill in a virtual environment under two optic flow conditions: normal (NOF) and perturbed (POF, continuous mediolateral pseudorandom oscillations). Each condition was performed under single-task and dual-task conditions of increasing difficulty (1-, 2-, 3-back). Subjective mental workload (raw NASA-TLX), cognitive performance (mean reaction time and d-prime), kinematic (steadiness, variability and complexity in the mediolateral and anteroposterior directions) and neuromuscular (duration and variability of motor primitives) control of gait were assessed. The cognitive performance and the number and composition of motor modules were unaffected by simultaneous walking, regardless of the optic flow condition. Kinematic and neuromuscular variability was greater under POF compared to NOF conditions. Young adults sought to counteract POF by rapidly correcting task-relevant gait fluctuations. The depletion of cognitive resources through dual-tasking led to reduced kinematic and neuromuscular variability and this occurred to the same extent regardless of simultaneous working memory (WM) load. Increasing WM load led to a prioritization of gait control in the mediolateral direction over the anteroposterior direction. The impact of POF on kinematic variability (step velocity) was reduced when a cognitive task was performed simultaneously, but this phenomenon was no modulated by WM load. Collectively, these results shed important light on how young adults adjust the processes involved in goal-directed locomotion when exposed to varying levels of task and environmental constraints.

Human–robot collaborative handling of curtain walls using dynamic motion primitives and real-time human intention recognition

Human–robot collaboration fully leverages the strengths of both humans and robots, which is crucial for handling large, heavy objects at construction sites. To address the challenges of human–machine cooperation in handling large-scale, heavy objects — specifically building curtain walls — a human–robot collaboration system was designed based on the concept of “human–centered with machine support”. This system allows the handling of curtain walls according to different human intentions. First, a robot trajectory learning and generalization model based on dynamic motion primitives was developed. The operator’s motion intent was then characterized by their speed, force, and torque, with the force impulse introduced to define the operator’s intentions for acceleration and deceleration. Finally, a collaborative experiment was conducted on an experimental platform to validate the robot’s understanding of human handling intentions and to verify its ability to handle curtain wall. Collaboration between humans and robots ensured a smooth and labor-saving handling process.

Neuromuscular Control Strategies in Basketball Shooting: Distance-Dependent Analysis of Muscle Synergies.

Basketball victory relies on an athlete's skill to make precise shots at different distances. While extensive research has explored the kinematics and dynamics of different shooting distances, the specific neuromuscular control strategies involved remain elusive. This study aimed to compare the differences in muscle synergies during basketball shooting at different distances, offering insights into neuromuscular control strategies and guiding athletes' training. Ten skilled shooting right-handed male basketball players participated as subjects in this experiment. Electromyographic (EMG) data for full-phase shooting were acquired at short (3.2 m), middle (5.0 m), and long (6.8 m) distances. Non-negative matrix decomposition extracted muscle synergies (motor modules and motor primitives) during shooting. The results of this study show that all three distance shooting can be broken down into three synergies and that there were differences in the synergies between short and long distances, with differences in motor primitive 1 and motor primitive 2 at the phase of 45% - 59% (p < 0.001, t* = 4.418), and 78% - 88% (p < 0.01, t* = 4.579), respectively, and differences in the motor module 3 found in the differences in muscle weights for rectus femoris (RF) (p = 0.001, d = -2.094), and gastrocnemius lateral (GL) (p = 0.001, d = -2.083). Shooting distance doesn't affect the number of muscle synergies in basketball shooting but alters synergy patterns. During long distance shooting training, basketball players should place more emphasis on the timing and synergistic activation of upper and lower limbs, as well as core muscles.

Fruit flexible collecting trajectory planning based on manual skill imitation for grape harvesting robot

The flexible collection is vital for the intelligent grape-picking robot. In view of the reliable operation method obtained by long-term training of human prior skill, a fruit flexible collecting trajectory planning method based on manual skill imitation for grape harvesting robot is proposed. The method involves capturing manual teaching trajectory data using a motion capture system, preprocessing the data, extracting features from multiple teaching trajectories, and forming a probability distribution through Gaussian mixture model – Gaussian mixture regression (GMM-GMR). And combined with the key point of manual operation trajectory, the general trajectory generated by GMR is segmented and further imitated by kernelized movement primitives (KMP) to obtain the reference trajectory, respectively. An optimization method for hyperparameter adaptation KMP (O-KMP) was proposed to meet the trajectory fitting effect of multiple key points. Mean square error (MSE) was used to evaluate the deviation of the trajectory from the reference trajectory. The partial optimal trajectory is selected and integrated into a single trajectory. Two experiments were conducted to investigate imitation: For the same starting and ending points task, the MSE of the trajectory generated by O-KMP decreased by 15.274% compared to the original fixed hyperparameter KMP. For different placement tasks, the MSE of the trajectory generated by the O-KMP decreased by 7.296% compared to the original KMP. Finally, the robot arm of the actuator visually presents different task states for the compliant placement of fresh grapes.

Exploring motor skill acquisition in bimanual coordination: insights from navigating a novel maze task

In this study, we introduce a novel maze task designed to investigate naturalistic motor learning in bimanual coordination. We developed and validated an extended set of movement primitives tailored to capture the full spectrum of scenarios encountered in a maze game. Over a 3-day training period, we evaluated participants’ performance using these primitives and a custom-developed software, enabling precise quantification of performance. Our methodology integrated the primitives with in-depth kinematic analyses and thorough thumb pressure assessments, charting the trajectory of participants’ progression from novice to proficient stages. Results demonstrated consistent improvement in maze performance and significant adaptive changes in joint behaviors and strategic recalibrations in thumb pressure distribution. These findings highlight the central nervous system’s adaptability in orchestrating sophisticated motor strategies and the crucial role of tactile feedback in precision tasks. The maze platform and setup emerge as a valuable foundation for future experiments, providing a tool for the exploration of motor learning and coordination dynamics. This research underscores the complexity of bimanual motor learning in naturalistic environments, enhancing our understanding of skill acquisition and task efficiency while emphasizing the necessity for further exploration and deeper investigation into these adaptive mechanisms.

Exo-Glove Shell: A Hybrid Rigid-Soft Wearable Robot for Thumb Opposition with an Under-Actuated Tendon-Driven System.

Usability and functionality are important when designing hand-wearable robots; however, satisfying both indicators remains a challenging issue, even though researchers have made important progress with state-of-the-art robot components. Although hand-wearable robots require sufficient actuators and sensors considering their functionality, these components complicate the robot. Further, robot compliance should be carefully considered because it affects both indicators. For example, a robot's softness makes it compact (improving usability) but also induces inaccurate force transmission (impacting functionality). To address this issue, we present in this paper a tendon-driven, hybrid, hand-wearable robot, named Exo-Glove Shell. The proposed robot assists in three primitive motions (i.e., thumb opposition motion, which is known as one of the most important hand functions, and flexion/extension of the index/middle fingers) while employing only four actuators by using an under-actuation mechanism. The Exo-Glove Shell was designed by combining a soft robotic body with rigid tendon router modules. The use of soft garments enables the robot to be fitted well to users without customization or adjustment of the mechanisms; the metal routers facilitate accurate force transmission. User tests conducted with an individual with a spinal cord injury (SCI) found that the robot could sufficiently and reliably assist in three primitive motions through its four actuators. The research also determined that the robot can assist in various postures with sufficient stability. Based on the grasp stability index proposed in this paper, user stability-when assisted by the proposed robot-was found to be 4.75 times that of an SCI person who did not use the Exo-Glove Shell.

Coupled Conditional Neural Movement Primitives

Learning sensorimotor trajectories through flexible neural representations is fundamental for robots as it facilitates the building of motor skills as well as equipping them with the ability to represent the world as predictable temporal events. Recent advances in deep learning led to the development of powerful learning from demonstration (LfD) systems such as Conditional Neural Movement Primitives (CNMPs). CNMPs can robustly represent skills as movement distributions and allow them to be ‘recalled’ by conditioning the movement on a few observation points. In this study, we focus on improving CNMPs to achieve a higher resource economy by adopting a divide-and-conquer approach. We propose a novel neural architecture called Coupled CNMP (C-CNMP), that couples the latent spaces of a pair of CNMPs that splits a given sensorimotor trajectory into segments whose learning is undertaken by smaller sub-networks. Therefore, each sub-network needs to deal with a less complex trajectory making the learning less resource-hungry. With systematic simulations on a controlled trajectory data set, we show that the overhead brought by the coupling introduced in our model is well offset by the resource and performance gain obtained. To be specific, with CNMP model as the baseline, it is shown that the proposed model is able to learn to generate trajectories in the data set with a lower trajectory error measured as the mean absolute difference between the generated trajectory and the ground truth. Importantly, our model can perform well with relatively limited resources, i.e., with less number of neural network parameters compared to the baseline. To show that the findings from the controlled data set well-transfer to robot data, we use robot joint data in an LfD setting and compare the learning performance of the proposed model with the baseline model at equal complexity levels. The simulation experiments show that with also the robot joint data, the proposed model, C-CNMP, learns to generate the joint trajectories with significantly less error than the baseline model. Overall, our study improves the state of the art in sensorimotor trajectory learning and exemplifies how divide-and-conquer approaches can benefit deep learning architectures for resource economy.

Demonstration-enhanced policy search for space multi-arm robot collaborative skill learning

The increasing complexity of on-orbit tasks imposes great demands on the flexible operation of space robotic arms, prompting the development of space robots from single-arm manipulation to multi-arm collaboration. In this paper, a combined approach of Learning from Demonstration (LfD) and Reinforcement Learning (RL) is proposed for space multi-arm collaborative skill learning. The combination effectively resolves the trade-off between learning efficiency and feasible solution in LfD, as well as the time-consuming pursuit of the optimal solution in RL. With the prior knowledge of LfD, space robotic arms can achieve efficient guided learning in high-dimensional state-action space. Specifically, an LfD approach with Probabilistic Movement Primitives (ProMP) is firstly utilized to encode and reproduce the demonstration actions, generating a distribution as the initialization of policy. Then in the RL stage, a Relative Entropy Policy Search (REPS) algorithm modified in continuous state-action space is employed for further policy improvement. More importantly, the learned behaviors can maintain and reflect the characteristics of demonstrations. In addition, a series of supplementary policy search mechanisms are designed to accelerate the exploration process. The effectiveness of the proposed method has been verified both theoretically and experimentally. Moreover, comparisons with state-of-the-art methods have confirmed the outperformance of the approach.

A behavioral conditional diffusion probabilistic model for human motion modeling in multi-action mixed human-robot collaboration

Collision avoidance is priority for human-robot collaboration (HRC). Human motions have stochastic, making it difficult for robots to recognize humans. To establish accurate human motion models is important to recognize humans. In the collaboration process, the action categories may suddenly and continuously switch to adapt to complex production tasks, namely, the stochastic of human motions, and we call this collaboration process as multi-action mixed HRC. The previous HRC rarely considered the stochastic of human motions. However, the stochastic in multi-action mixed HRC brings great challenges to human motion modeling. On the one hand, the same observed motions may develop into different action categories, the motion distribution need to be predicted. On the other hand, the connected motions between two action categories are missing, and need to be predicted. To address these two problems, this study introduced diffusion probabilistic models to capture the stochastic of human motions. However, the current diffusion probabilistic models cannot realize the high-accuracy human motion prediction. Most of them did not consider the condition distribution in diffusion process, and did not comprehensively capture the spatiotemporal characteristics between human and robot. To solve the above problems, a behavioral conditional probabilistic diffusion model (BCDPM) is proposed. The BCDPM model can capture the stochastic of human motions to model the human motion distribution. A mask mechanism is combined with diffusion prior generated by BCDPM model to predict the missing connected motions. The BCDPM model consists of the designed constrain network, the denoising network, and enhanced memory graph neural network. The constrain network can generate the condition distribution, the denoising network and enhanced memory graph neural network can capture the short term and long term spatiotemporal characteristics, respectively. Moreover, the overlapping motion primitives are explicitly encoded into the implicit BCDPM model to mine commonalities of motions. The results show the proposed method can obtain excellent accuracy in prediction and outstanding F1 score in collision detection experiment.

Learning periodic skills for robotic manipulation: Insights on orientation and impedance

Many daily tasks exhibit a periodic nature, necessitating that robots possess the ability to execute them either alone or in collaboration with humans. A widely used approach to encode and learn such periodic patterns from human demonstrations is through periodic Dynamic Movement Primitives (DMPs). Periodic DMPs encode cyclic data independently across multiple dimensions of multi-degree of freedom systems. This method is effective for simple data, like Cartesian or joint position trajectories. However, it cannot account for various geometric constraints imposed by more complex data, such as orientation and stiffness. To bridge this gap, we propose a novel periodic DMP formulation that enables the encoding of periodic orientation trajectories and varying stiffness matrices while considering their geometric constraints. Our geometry-aware approach exploits the properties of the Riemannian manifold and Lie group to directly encode such periodic data while respecting its inherent geometric constraints. We initially employed simulation to validate the technical aspects of the proposed method thoroughly. Subsequently, we conducted experiments with two different real-world robots performing daily tasks involving periodic changes in orientation and/or stiffness, i.e., operating a drilling machine using a rotary handle and facilitating collaborative human–robot sawing.

Joint synergy and muscle activity in the motion of the ankle-foot complex.

The movement of the ankle-foot complex joints is coupled as a result of various physiological and physical constraints. This study introduces a novel approach to the analysis of joint synergies and their physiological basis by focusing on joint rotational directions and the types of muscle contractions. We developed a biomimetic model of the ankle-foot complex with seven degrees of freedom, considering the skeletal configuration and physiological axis directions. Motion capture experiments were conducted with eight participants performing dorsiflexion and plantarflexion in open-chain states, as well as various walking tasks in closed-chain states, across different ground inclinations (±10, ±5, 0deg) and walking speeds (3 and 4 km h-1). Hierarchical cluster analysis identified joint synergy clusters and motion primitives, revealing that in open-chain movements, plantarflexion of the ankle, tarsometatarsal and metatarsophalangeal joints exhibited synergy with the inversion of the remaining joints in the complex; meanwhile, dorsiflexion was aligned with eversion. During closed-chain movements, the synergies grouping was exchanged in the subtalar, talonavicular and metatarsophalangeal joints. Further analysis showed that in open-chain movements, synergy patterns influenced by multi-joint muscles crossing oblique joint axes contribute to foot motion. In closed-chain movements, these changes in synergistic patterns enhance the propulsion of the center of mass towards the contralateral leg and improve foot arch compliance, facilitating human motion. Our work enhances the understanding of the physiological mechanisms underlying synergistic motion within the ankle-foot complex.

MT-RSL: A multitasking-oriented robot skill learning framework based on continuous dynamic movement primitives for improving efficiency and quality in robot-based intelligent operation

Robot skill learning is one of the international advanced directions in the field of robot-based intelligent manufacturing, which makes it possible for robots to learn and operate autonomously in complex real-world environments. In this paper, we propose a multitasking-oriented robot skill learning framework named MT-RSL to improve the efficiency and robustness of multi-task robot skill learning in complex real-world environments, and present the detailed design steps of three key sub-modules included in MT-RSL, namely, sub-task segmentation module, robot skill learning module, and robot configuration optimization module. Firstly, we design a novel sub-task segmentation module based on a coarse-to-fine sub-task segmentation (CF-STS) strategy, in which the Finite State Machine (FSM) is used to analyze complex robot behaviors to obtain a coarse robot sub-task sequence, and the Beta Process Autoregressive Hidden Markov Model (BP-AR-HMM) is used to establish the connection and dependence between multiple demonstration trajectories and encode these trajectories, so as to obtain a finer robot action sequence. Secondly, we extend the basic DMPs system to a continuous dynamic movement primitives (CDMPs) system to construct a novel robot skill learning module, which improves the efficiency of the robot to learn skills and perform actions by orderly coordinating sub-parts such as the activation signals, motion actuator, DMPs-based learning module, and robot configuration optimization module. Then, we design a novel robot configuration optimization module, which introduces the velocity directional manipulability measure (VDM) as the evaluation index of robot kinematic performance to establish the robot configuration optimization model, and proposes an improved probabilistic adaptive particle swarm optimization (Pro-APSO) algorithm to solve this optimization model, so as to obtain the optimal robot configuration. Finally, we develop an experimental testing platform based on the Robot Operating System (ROS) and conduct a series of prototype experiments in complex real-world scenarios. The experimental results demonstrate that our proposed MT-RSL can significantly improve the effectiveness and robustness of multi-task robot skill learning, and can outperform existing robot skill learning methods in terms of both learning efficiency, VDM, and success rate.

A demonstration trajectory segmentation approach for wheelchair‐mounted assistive robots

AbstractSegmentation of demonstration trajectories and learning the contained motion primitives can effectively enhance the assistive robot's intelligence to flexibly reproduce learnt tasks in an unstructured environment. With the aim to conveniently and accurately segment demonstration trajectories, a novel demonstration trajectory segmentation approach is proposed based on the beta process autoregressive hidden Markov model (BP‐AR‐HMM) algorithm and generalised time warping (GTW) algorithm aiming to enhance the segmentation accuracy utilising acquired demonstration data. This approach first adopts the GTW algorithm to align the multiple demonstration trajectories for the same task. Then, it adopts the BP‐AR‐HMM algorithm to segment the demonstration trajectories, acquire the contained motion primitives, and establish the related task library. This segmentation approach is validated on the 6‐degree‐of‐freedom JACO robotic arm by assisting users to accomplish a holding water glass task and an eating task. The experimental results show that the motion primitives within the trajectories can be correctly segmented with a high segmentation accuracy.

Robot control based on motor primitives: A comparison of two approaches

Motor primitives are fundamental building blocks of a controller which enable dynamic robot behavior with minimal high-level intervention. By treating motor primitives as basic “modules,” different modules can be sequenced or superimposed to generate a rich repertoire of motor behavior. In robotics, two distinct approaches have been proposed: Dynamic Movement Primitives (DMPs) and Elementary Dynamic Actions (EDAs). While both approaches instantiate similar ideas, significant differences also exist. This paper attempts to clarify the distinction and provide a unifying view by delineating the similarities and differences between DMPs and EDAs. We provide nine robot control examples, including sequencing or superimposing movements, managing kinematic redundancy and singularity, control of both position and orientation of the robot’s end-effector, obstacle avoidance, and managing physical interaction. We show that the two approaches clearly diverge in their implementation. We also provide a real-robot demonstration to show how DMPs and EDAs can be combined to get the best of both approaches. With this detailed comparison, we enable researchers to make informed decisions to select the most suitable approach for specific robot tasks and applications.