Sequences Of Motion Primitives Research Articles

Modeling and Control Design of a Contact-Based, Electrostatically Actuated Rotating Sphere

The performance of micromirrors in terms of their maximum deflection is often limited due to mechanical constraints in the design. To increase the range of achievable deflection angles, we present a novel concept in which a free-lying sphere with a flat side as reflector can be rotated. Due to the large forces needed to move the sphere, multiple electrostatic actuators are used to cooperatively rotate the sphere in iterative steps by impacts and friction. A parameterized system-level model of the configuration is derived, which considers arbitrary multi-contact scenarios and can be used for simulation, analysis, and control design purposes. Due to the complex, indirect relation between the actuator voltages and the sphere motion, model-based numerical optimization is applied to obtain suitable system inputs. This results in rotation sequences, which can be understood as a sequence of motion primitives, thus transforming the continuous time model into an abstract discrete time model. Based on this, we propose a feedback control strategy for trajectory following, considering model uncertainties by a learning scheme. High precision is achieved by an extension controlling the angular change of each rotation step. The suitability of the overall approach is demonstrated in simulation for maximum angles of 40°, achieving angular velocities of approximately 10°/s.

Formation and reconfiguration of tight multi-lane platoons

Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons traveling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan based on real-time information about the surrounding traffic. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: (1) formation reconfiguration (2) obstacle avoidance, and (3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives. Videos and software can be found online here https://github.com/RoyaFiroozi/Centralized-Planning.

A Modular Framework for Motion Planning Using Safe-by-Design Motion Primitives

In this paper, we present a modular framework for solving a motion planning problem among a group of robots. The proposed framework utilizes a finite set of low-level motion primitives to generate motions in a gridded workspace. The constraints on allowable sequences of motion primitives are formalized through a maneuver automaton . At the high level, a control policy determines which motion primitive is executed in each box of the gridded workspace. We state general conditions on motion primitives to obtain provably correct behavior so that a library of safe-by-design motion primitives can be designed. The overall framework yields a highly robust design by utilizing feedback strategies at both the low and high levels. We provide specific designs for motion primitives and control policies suitable for multirobot motion planning; the modularity of our approach enables one to independently customize the designs of each of these components. Our approach is experimentally validated on a group of quadrocopters.

Cooperative Shared Control Driver Assistance Systems Based on Motion Primitives and Differential Games

In this paper, a cooperative shared control driver assistance system that supports the driver in the steering task is proposed. The aim behind this concept is a cooperation between the driver and the assistance system. Thereby, both, driver and assistance system, can apply a torque on the steering wheel. Mathematically, this structure is described as a differential game. As a primary condition to facilitate cooperation, it is essential to explicitly regard the aims and steering actions of the driver for the calculation of the optimal torque, which the assistance system should apply. This requires an appropriate model of the human driver. A model that describes the driver steering motion as a sequence of motion primitives, which can be identified, is proposed for this task. Next, a real-time capable implementation of this concept is proposed. The concept is validated in a driving study. The study indicates that the system improves the lane keeping performance of the participants and leads to a higher user rating compared with noncooperative driver assistance systems.

Extraction and Deployment of Human Guidance Policies

Robust and adaptive human motion performance depends on learning, planning, and deploying primitive elements of behavior. Previous work has shown how human motion behavior can be partitioned at subgoal points, and primitive elements extracted as trajectory segments between subgoals. An aggregate set of trajectory segments are described by a spatial cost function and guidance policy. In this paper, Gaussian process regression is used to approximate cost and policy functions extracted from human-generated trajectories. Patterns are identifying in the policy function to further decompose guidance behavior into a sequence of motion primitives. A maneuver automaton model is introduced, simplifying the guidance task over a larger spatial domain. The maneuver automaton and approximated policy functions are then used to generate new trajectories, replicating original human behavior examples.

Real-time Unsupervised Segmentation of human whole-body motion and its application to humanoid robot acquisition of motion symbols

An interactive loop between motion recognition and motion generation is a fundamental mechanism for humans and humanoid robots. We have been developing an intelligent framework for motion recognition and generation based on symbolizing motion primitives. The motion primitives are encoded into Hidden Markov Models (HMMs), which we call “motion symbols”. However, to determine the motion primitives to use as training data for the HMMs, this framework requires a manual segmentation of human motions. Essentially, a humanoid robot is expected to participate in daily life and must learn many motion symbols to adapt to various situations. For this use, manual segmentation is cumbersome and impractical for humanoid robots. In this study, we propose a novel approach to segmentation, the Real-time Unsupervised Segmentation (RUS) method, which comprises three phases. In the first phase, short human movements are encoded into feature HMMs. Seamless human motion can be converted to a sequence of these feature HMMs. In the second phase, the causality between the feature HMMs is extracted. The causality data make it possible to predict movement from observation. In the third phase, movements having a large prediction uncertainty are designated as the boundaries of motion primitives. In this way, human whole-body motion can be segmented into a sequence of motion primitives. This paper also describes an application of RUS to AUtonomous Symbolization of motion primitives (AUS). Each derived motion primitive is classified into an HMM for a motion symbol, and parameters of the HMMs are optimized by using the motion primitives as training data in competitive learning. The HMMs are gradually optimized in such a way that the HMMs can abstract similar motion primitives. We tested the RUS and AUS frameworks on captured human whole-body motions and demonstrated the validity of the proposed framework.

Spatio-temporal structure of human motion primitives and its application to motion prediction

This paper proposes a novel approach to structuring behavioral knowledge based on symbolization of human whole body motions, hierarchical classification of the motions, and extraction of the causality among the motions. The motion patterns are encoded into parameters of corresponding Hidden Markov Models (HMMs), where each HMM abstracts the dynamics of motion pattern, and hereafter is referred to as “motion symbol”. The motion symbols allow motion recognition and synthesis. The motion symbols are organized into a hierarchical tree structure representing the property of spatial similarity among the motion patterns, and this tree is referred to as “motion symbol tree”. Seamless motion is segmented into a sequence of motion primitives, each of which is classified as a motion symbol based on the motion symbol tree. The seamless motion results in a sequence of the motion symbols, which is stochastically represented as transitions between the motion symbols by an N-gram model. The motion symbol N-gram model is referred to as “motion symbol graph”. The motion symbol graph extracts the temporal causality among the human behaviors. The integration of the motion symbol tree and the motion symbol graph makes it possible to recognize motion patterns fast and predict human behavior during observation. The experiments on a motion dataset of radio calisthenics and on a large motion dataset provided by CMU motion database validate the proposed framework.

Learning Trajectories for Robot Programing by Demonstration Using a Coordinated Mixture of Factor Analyzers.

This paper presents an approach for learning robust models of humanoid robot trajectories from demonstration. In this formulation, a model of the joint space trajectory is represented as a sequence of motion primitives where a nonlinear dynamical system is learned by constructing a hidden Markov model (HMM) predicting the probability of residing in each motion primitive. With a coordinated mixture of factor analyzers as the emission probability density of the HMM, we are able to synthesize motion from a dynamic system acting along a manifold shared by both demonstrator and robot. This provides significant advantages in model complexity for kinematically redundant robots and can reduce the number of corresponding observations required for further learning. A stability analysis shows that the system is robust to deviations from the expected trajectory as well as transitional motion between manifolds. This approach is demonstrated experimentally by recording human motion with inertial sensors, learning a motion primitive model and correspondence map between the human and robot, and synthesizing motion from the manifold to control a 19 degree-of-freedom humanoid robot.

Path-Parameterization Approach Using Trajectory Primitives for Three-Dimensional Motion Planning

Motion planning determines trajectories for vehicles that link an initial location and heading with a final location and heading. Techniques for motion planning have been developed for two-dimensional maneuvering; however, they are less mature for three-dimensional maneuvering. The concept of motion primitives is particularly attractive for motion planning that determines trajectories as a set of maneuvers that satisfy differential constraints. This paper furthers work on a higher-level abstraction of trajectory primitives that consider sequences of motion primitives. In this paper, trajectory primitives are developed that deal with airspace constraints of an environment. The motion planning is shown to be an optimization involving a pair of trajectory primitives that is related by an intermediate waypoint. The resulting path is completely parameterized by the waypoint location.

Action recognition and understanding through motor primitives

In robotics, recognition of human activity has been used extensively for robot task learning through imitation and demonstration. However, there has not been much work performed on modeling and recognition of activities that involve object manipulation and grasping. In this work, we deal with single arm/hand actions which are very similar to each other in terms of arm/hand motions. The approach is based on the hypothesis that actions can be represented as sequences of motion primitives. Given this, a set of five different manipulation actions of different levels of complexity are investigated. To model the process, we use a combination of discriminative support vector machines and generative hidden Markov models. The experimental evaluation, performed with 10 people, investigates both the definition and structure of primitive motions, as well as the validity of the modeling approach taken.