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Robots based on tensegrity structures have the potential to be robust, efficient and adaptable. While traditionally being difficult to control, recent control strategies for ball-shaped tensegrity robots have successfully enabled punctuated rolling, hill-climbing and obstacle climbing. These gains have been made possible through the use of machine learning and physics simulations that allow controls...
Robotic systems typically have numerous parameters, e.g. the choice of planning algorithm, real-valued parameters of motion and vision modules, and control parameters. We consider the problem of optimizing these parameters for best worst-case performance over a range of environments. To this end we first propose to evaluate system parameters by adversarially optimizing over environment parameters...
We present an algorithm for generating open-loop trajectories that solve the problem of rearrangement planning under uncertainty. We frame this as a selection problem where the goal is to choose the most robust trajectory from a finite set of candidates. We generate each candidate using a kinodynamic state space planner and evaluate it using noisy rollouts. Our key insight is we can formalize the...
Mapping evolving environments requires an update mechanism to efficiently deal with dynamic objects. In this context, we propose a new approach to update maps pertaining to large-scale dynamic environments with semantics. While previous works mainly rely on large amount of observations, the proposed framework is able to build a stable representation with only two observations of the environment. To...
To analyze auditory scenes of robots' surrounding environments, not only speeches but also non-speech sounds are important, which are spatially distributed and have different spectral and temporal characteristics. Thus, this paper investigates Acoustic Event Identification (AEI) which includes problems of localization, detection, and identification of sound sources. To achieve AEI by a robot in a...
This article depicts an algorithm which matches the output of a Lidar with an initial terrain model to estimate the absolute pose of a robot. Initial models do not perfectly fit the reality and the acquired data set can contain an unknown, and potentially large, proportion of outliers. We present an interval based algorithm that copes with such conditions, by matching the Lidar data with the terrain...
In this paper we exploit Iterative Learning Controllers (ILC) schemes in force adaptation tasks. We propose to encode the control signal with Radial Basis Functions (RBF), which enhances the robustness of the ILC scheme and allows to vary the execution speed of the learned motion. For that a novel control scheme is proposed, which updates the feedforward compensation signals based on current iteration...
With the increasing usage of autonomous underwater vehicles in offshore applications, it has been important to have a controller that can satisfy a convincing performance throughout the mission. Due to non-linear and time varying dynamic, controlling this vehicle has some special difficulties. Modeling errors that may exist and Uncertainties which associated with the environment firstly evokes that...
This work focuses on robust nonlinear control design of a robot arm with micro-hand by using operator-based robust right coprirne factorization (RRCF) approach. In detail, to control the precise endpoint position of robot arm and obtain the desired force using micro-hand according to the external environment or task involved, a connected feedback control system based on operator-based RRCF approach...
A robust tracking control problem is considered for an unknown Euler-Lagrange system to track a desired time-varying trajectory. Unknown dynamics including friction effects and impact forces from the environment are considered in the paper. It is challenging to model these two types of uncertain forces for Euler-Lagrange Systems such as robot manipulators and motors. A robust controller with adaptive...
Infants are curious learners who drive their own cognitive development by imposing structure on their learning environments as they explore. Understanding the mechanisms underlying this curiosity is therefore critical to our understanding of development. However, very few studies have examined the role of curiosity in infants' learning, and in particular, their categorization; what structure infants...
In this paper, a parametric adaptive backstepping control is presented to improve the dynamic responses of Electro-hydraulic system under unknown parameters and dynamic external loads. Firstly, 6 unknown parameters is estimated by parametric adaptive estimation law in nonlinear model of EHS. Secondly, the backstepping controller is designed by Lyapunov method to realize the position tracking control...
In this paper, we show that the existence of centrally synergistic potential functions on the n-dimensional sphere, denoted by Sn, is a sufficient condition for the global asymptotic stabilization of a point in Sn. Additionally, if these functions decrease exponentially fast during flows and are bounded from above and from below by some polynomial function of the tracking error, then the reference...
Among different machine learning algorithms AdaBoost is a classification technique, which improves the classification accuracy by increasing the weights of the misclassified data. To overcome the problem of misclassification in Real AdaBoost algorithm, of the already classified samples, concept of margin is employed in the Parameterized AdaBoost algorithm. The new parameter, introduced in Parameterized...
Population based encodings allow to represent probabilistic and fuzzy state estimates. Such a representation will be introduced and applied for the case of a redundant manipulator. Following the Mean of Multiple Computations principle, a neural network model (PbMMC) is presented in which the overall complexity is divided into multiple local relationships. This allows to solve inverse, forward and...
Intention understanding is a basic requirement for human-machine interaction. Action classification and object affordance recognition are two possible ways to understand human intention. In this study, Multiple Timescale Recurrent Neural Network (MTRNN) is adapted to analyze human action. Supervised MTRNN, which is an extension of Continuous Timescale Recurrent Neural Network (CTRNN), is used for...
A control algorithm for finite-time tracking of quadrotor is introduced. The algorithm is based on use of feedback linearization method and finite-time output control presented in [17]. In comparison with commonly used in practice control methods like PD-controller the proposed method allows to estimate the first derivatives of the generalized coordinates, that enable to reduce the number of necessary...
The performance of a tracker can be measured by two often conflicting criteria - robustness and accuracy. Recently researchers have focused on improving robustness, using adaptive appearance models. However updating the appearance model can cause drift and lower the accuracy of motion (state) estimation. These trackers generally compute 2 degree of freedom(DOF) image translation of the object, and...
In the future robotic applications, robot requires the ability not only to recognize human actions but also to learn incrementally and quickly. Therefore, we proposed an incremental action learning system for this future requirement. The proposed system can continuously learn new actions quickly with robust performance and less effort.
Unmanned Aerial Vehicles (UAVs) are employed in surveillance, reconnaissance, and aerial photography. The dynamics of UAVs is highly nonlinear and inherently coupled. It also tends to vary continuously with time and is subjected to severe external disturbances. Due to this, dynamic and parametric uncertainties arise in the mathematical model of the UAVs over different operating conditions. In this...
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