Motion Primitives Research Articles

MPTree: A Sampling-based Vehicle Motion Planner for Real-time Obstacle Avoidance

This paper presents a novel and modular framework, named MPTree, for real-time vehicle motion planning with dynamic obstacle avoidance. MPTree adopts a sampling-based algorithm, to explore a tree of Motion Primitives (MPs) and return near-optimal trajectories. Specifically, MPTree builds motion primitives to connect the tree nodes (waypoints), sampled in a mesh of waylines on the local planning horizon. The tree exploration is based on a semi-structured RRTa, with an application-specific cost function (e.g., minimum-jerk or minimum-time) and high-level behavioral policy. We show examples of MPTREE’s specialization for urban environments and autonomous racing, using fast-to-evaluate motion primitives to accelerate the tree exploration phase. A prototype implementation is tested in a closed-loop simulation environment. Our preliminary results show that MPTree provides feasible collision-free trajectories while ensuring low computational times.

Learning Motion Primitives for the Quantification and Diagnosis of Mobility Deficits.

The severity of mobility deficits is one of the most critical parameters in the diagnosis and rehabilitation of Parkinson's disease (PD). The current approach for severity evaluation is clinical scaling that relies on a clinician's subjective observations and experience, and the observation in laboratories or clinics may not suffice to reflect the severity of motion deficits as compared to daily living activities. The paper presents an approach to modeling and quantifying the severity of mobility deficits from motion data by using nonintrusive wearable physio-biological sensors. The approach provides a user-specific metric that measures mobility deficits in terms of the quantities of motion primitives that are learned from motion tracking data. The proposed method achieved 99.84% prediction accuracy on laboratory data and 93.95% prediction accuracy on clinical data. This approach presents the potential to supplant traditional observation-based clinical scaling, providing an avenue for real-time feedback to fortify positive progression throughout the course of rehabilitation.

Biomimetic learning of hand gestures in a humanoid robot.

Hand gestures are a natural and intuitive form of communication, and integrating this communication method into robotic systems presents significant potential to improve human-robot collaboration. Recent advances in motor neuroscience have focused on replicating human hand movements from synergies also known as movement primitives. Synergies, fundamental building blocks of movement, serve as a potential strategy adapted by the central nervous system to generate and control movements. Identifying how synergies contribute to movement can help in dexterous control of robotics, exoskeletons, prosthetics and extend its applications to rehabilitation. In this paper, 33 static hand gestures were recorded through a single RGB camera and identified in real-time through the MediaPipe framework as participants made various postures with their dominant hand. Assuming an open palm as initial posture, uniform joint angular velocities were obtained from all these gestures. By applying a dimensionality reduction method, kinematic synergies were obtained from these joint angular velocities. Kinematic synergies that explain 98% of variance of movements were utilized to reconstruct new hand gestures using convex optimization. Reconstructed hand gestures and selected kinematic synergies were translated onto a humanoid robot, Mitra, in real-time, as the participants demonstrated various hand gestures. The results showed that by using only few kinematic synergies it is possible to generate various hand gestures, with 95.7% accuracy. Furthermore, utilizing low-dimensional synergies in control of high dimensional end effectors holds promise to enable near-natural human-robot collaboration.

Contextualized Trajectory Parsing with Spatio-Temporal Graph

This work investigates how to automatically parse object trajectories in surveillance videos, which aims at jointly solving three subproblems: 1) spatial segmentation, 2) temporal tracking, and 3) object categorization. We present a novel representation spatiotemporal graph (ST-Graph) in which: 1) Graph nodes express the motion primitives, each representing a short sequence of small-size patches over consecutive images, and 2) every two neighbor nodes are linked with either a positive edge or a negative edge to describe their collaborative or exclusive relationship of belonging to the same object trajectory. Phrasing the trajectory parsing as a graph multicoloring problem, we propose a unified probabilistic formulation to integrate various types of context knowledge as informative priors. An efficient composite cluster sampling algorithm is employed in search of the optimal solution by exploiting both the collaborative and the exclusive relationships between nodes. The proposed framework is evaluated over challenging videos from public datasets, and results show that it can achieve state-of-the-art tracking accuracy.

PRIMP: PRobabilistically-Informed Motion Primitives for Efficient Affordance Learning From Demonstration

PRIMP: PRobabilistically-Informed Motion Primitives for Efficient Affordance Learning From Demonstration

Computationally Efficient Minimum-Time Motion Primitives for Vehicle Trajectory Planning

Computationally Efficient Minimum-Time Motion Primitives for Vehicle Trajectory Planning

Kinodynamic Motion Planning for a System with Squid Dynamics

This paper introduces a path planning algorithm for a system with squid dynamics in a cluttered environment. We capture the complex interactions of fin, arms, and body patterning by analyzing experimental data collected from observing squid motion. We extract nine motion primitives to build the control sequence for a time-optimal trajectory. This task is formulated as a mixed-integer program, and we generate the minimum-time trajectory using a sample-based approach. Numerical simulations illustrate the efficacy of this strategy and motivate ongoing and future efforts to exploration of squid motion features, improvement of the modeling, and experimental demonstrations of the motion planning strategy.

Hierarchical Trajectory Planning Based on Adaptive Motion Primitives and Bilevel Corridor

Hierarchical Trajectory Planning Based on Adaptive Motion Primitives and Bilevel Corridor

A Comparison of Approaches for Segmenting the Reaching and Targeting Motion Primitives in Functional Upper Extremity Reaching Tasks.

There is growing interest in the kinematic analysis of human functional upper extremity movement (FUEM) for applications such as health monitoring and rehabilitation. Deconstructing functional movements into activities, actions, and primitives is a necessary procedure for many of these kinematic analyses. Advances in machine learning have led to progress in human activity and action recognition. However, their utility for analyzing the FUEM primitives of reaching and targeting during reach-to-grasp and reach-to-point tasks remains limited. Domain experts use a variety of methods for segmenting the reaching and targeting motion primitives, such as kinematic thresholds, with no consensus on what methods are best to use. Additionally, current studies are small enough that segmentation results can be manually inspected for correctness. As interest in FUEM kinematic analysis expands, such as in the clinic, the amount of data needing segmentation will likely exceed the capacity of existing segmentation workflows used in research laboratories, requiring new methods and workflows for making segmentation less cumbersome. This paper investigates five reaching and targeting motion primitive segmentation methods in two different domains (haptics simulation and real world) and how to evaluate these methods. This work finds that most of the segmentation methods evaluated perform reasonably well given current limitations in our ability to evaluate segmentation results. Furthermore, we propose a method to automatically identify potentially incorrect segmentation results for further review by the human evaluator. Clinical impact: This work supports efforts to automate aspects of processing upper extremity kinematic data used to evaluate reaching and grasping, which will be necessary for more widespread usage in clinical settings.

Trajectory Deformation-Based Multi-Modal Adaptive Compliance Control for a Wearable Lower Limb Rehabilitation Robot.

Adaptive compliance control is critical for rehabilitation robots to cope with the varying rehabilitation needs and enhance training safety. This article presents a trajectory deformation-based multi-modal adaptive compliance control strategy (TD-MACCS) for a wearable lower limb rehabilitation robot (WLLRR), which includes a high-level trajectory planner and a low-level position controller. Dynamic motion primitives (DMPs) and a trajectory deformation algorithm (TDA) are integrated into the high-level trajectory planner, generating multi-joint synchronized desired trajectories through physical human-robot interaction (pHRI). In particular, the amplitude modulation factor of DMPs and the deformation factor of TDA are adapted by a multi-modal adaptive regulator, achieving smooth switching of human-dominant mode, robot-dominant mode, and soft-stop mode. Besides, a linear active disturbance rejection controller is designed as the low-level position controller. Four healthy participants and two stroke survivors are recruited to conduct robot-assisted walking experiments using the TD-MACCS. The results show that the TD-MACCS can smoothly switch three control modes while guaranteeing trajectory tracking accuracy. Moreover, we find that appropriately increasing the upper bound of the deformation factor can enhance the average walking speed (AWS) and root mean square of trajectory deviation (RMSTD).

Cooperative Control for Dual-Arm Robots Based on Improved Dynamic Movement Primitives

Cooperative Control for Dual-Arm Robots Based on Improved Dynamic Movement Primitives

Probabilized Dynamic Movement Primitives and Model Predictive Planning for Enhanced Trajectory Imitation Learning

Probabilized Dynamic Movement Primitives and Model Predictive Planning for Enhanced Trajectory Imitation Learning

Robots Adapting to the Environment: A Review on the Fusion of Dynamic Movement Primitives and Artificial Potential Fields

Robots Adapting to the Environment: A Review on the Fusion of Dynamic Movement Primitives and Artificial Potential Fields

Imitation Learning and Teleoperation Shared Control With Unit Tangent Fuzzy Movement Primitives

Imitation Learning and Teleoperation Shared Control With Unit Tangent Fuzzy Movement Primitives

A Variable Impedance Skill Learning Algorithm Based on Kernelized Movement Primitives

This paper proposes a novel learning from demonstrations (LfD) method based on kernelized movement primitives (KMP). The original KMP algorithm is excellent at generalizing and handling high-dimensional inputs, but it is slightly inadequate in reproduction accuracy. To address this issue, we make two improvements to the KMP algorithm. Firstly, a multivariate Gaussian process (MV-GP) is employed to model the reference trajectory, which preliminarily improves the reproduction accuracy of KMP. Secondly, an optimization problem is formulated to learn the hyperparameters of the kernel function in KMP, which reduces the dependence on experience. We also propose a novel variable impedance control (VIC) approach to trade off contact compliance against tracking accuracy by utilizing the probabilistic properties of the KMP. Comparative simulations and experiments are conducted to validate the proposed LfD algorithm.

Neural Optimal Control for Constrained Visual Servoing via Learning From Demonstration

This paper proposes a novel optimal control scheme for constrained image based visual servoing of a robot manipulator. For a robot manipulator with an eye-on-hand configuration, visibility constraint is an essential requirement to avoid servo failure, while robot’s actuator limits must also be satisfied. To ensure this, the constraints are modelled implicitly via learning the task and defining safe regions using expert human demonstrations via mixture of Dynamic Movement Primitives (DMPs). The visual servoing problem is then formulated as a closed-loop optimal control problem using these constraint model where a desired target (possibly time-varying) is obtained by acting upon the feedback from the real-time visual sensors. The visual servo control loop consists of a single network adaptive critic optimal tracking control scheme whose weights are tuned using Lyapunov stability criteria. The stability and the performance of the proposed control scheme is shown theoretically via Lyapunov approach and also verified experimentally using a seven degree of freedom (DOF) Franka Emika and six DOF Universal Robot (UR) 10 manipulator. The approach is also demonstrated on a use case scenarios in mock-up convenience store and warehouse setup. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The applications of robots in busy warehouses, healthcare sectors and convenience store, has a big societal impact. The scenarios in these real-world problems often consist of a dynamic environment. Therefore, sensor-based localization and planning, along with satisfying robot and task constraints are needed. These naturally adds up to the programming costs in addition to the robot platform and actuation. However, modern robots need intuitive and easy programming for more pervasive in a practical societal application. In our proposed method, this sensor-based planning with environmental constraints is modelled implicitly using the Programming by Demonstration framework. The user needs to catch the robot arm by his hand and teach the task at hand, and the framework captures the task and robot constraints while generalizing to new goals. This modelling, along with cost optimal controller, generates real-time constraint aware robot manipulation trajectories for the demonstrated task in dynamic environments.

Dynamic Movement Primitives Modulation-Based Compliance Control for a New Sitting/Lying Lower Limb Rehabilitation Robot

Dynamic Movement Primitives Modulation-Based Compliance Control for a New Sitting/Lying Lower Limb Rehabilitation Robot

Improved Generalization of Probabilistic Movement Primitives for Manipulation Trajectories

Improved Generalization of Probabilistic Movement Primitives for Manipulation Trajectories

Probabilistic Motion Prediction and Skill Learning for Human-to-Cobot Dual-Arm Handover Control.

In this article, we focus on human-to-cobot dual-arm handover operations for large box-type objects. The efficiency of handover operations should be ensured and the naturalness as if the handover is going on between two humans. First of all, we study the human-human dual-arm large box-type object natural handover process to guide this research. Then, for efficiency, we combine the probabilistic approach with the online learning algorithm to predict the beginning of the handover task and handover positions. The online updating probabilistic models can deal with not only human givers' regular motion patterns but also their irregular motion patterns. Then, to guarantee that human givers can perform handover operations naturally, we apply the probabilistic robot skill learning method kernelized movement primitives (KMPs) to adapt the learned receiving skills and fulfill some constraints for safety based on online predicted results. Furthermore, we give special attention to the dual-arm grasp strategy and control design to guarantee a stable grasp. In addition, we equip this handover system on a Baxter cobot and extend its grippers to make it more suitable for dual-arm handover operations. The experimental results show that the proposed handover system can solve human-to-cobot dual-arm handover operations for large box-type objects naturally and efficiently.

Velocity obstacle guided motion planning method in dynamic environments

The ability of a robot to navigate through a dynamic environment, avoid obstacles, and reach its destination is crucial for safety and a major technological challenge in autonomous navigation, especially in crowded environments involving hospitals, hotels, and restaurants. Most prominent methods use distance metrics as a safety constraints in their planning algorithms, which leads to overly cautious navigation. Thus, a velocity obstacle guided motion planning method for mobile robots in moving obstacle environments is proposed. The approach automatically negotiates dynamic obstacles combining velocity obstacle (VO) and trajectory generating. First, a dynamic perception algorithm is developed to track and predict obstacles using only point cloud input. Subsequently, to obtain an initial trajectory that satisfies kinodynamic and the VO-based safety metric (VOSM), VO is applied to search the kinodynamic path to verify the safety of the extended motion primitives. Finally, the smoothness and safety of the robot trajectory are enhanced by nonlinear optimization, which incorporates the proposed safety constraints based on VO. Extensive simulations in challenging environments demonstrate a 40.0% success rate increase and a 44.8% trajectory smoothness improvement over obstacle distance-based methods. Practical testing prove our technology is reliable and can safely avoid dynamic obstacles.