Exploiting Robot Redundancy for Online Learning and Control (original) (raw)
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Control of redundant robots using learned models: An operational space control approach
2009
We present an adaptive control approach combining forward kinematics model learning methods with the operational space control approach. This combination endows the robot with the ability to realize hierarchically organised learned tasks in parallel, using tasks null space projectors built upon the learned models. We illustrate the proposed method on a simulated 3 degrees of freedom planar robot. This system is used as a benchmark to compare our method to an alternative approach based on learning an extended Jacobian. We show the better versatility of the retained approach with respect to the latter.
Learning Forward Models for the Operational Space Control of Redundant Robots
2010
We present an adaptive control approach combining model learning methods with the operational space control approach. We learn the forward kinematics model of a robot and use standard algebraic methods to extract pseudo-inverses and projectors from it. This combination endows the robot with the ability to realize hierarchically organised learned tasks in parallel, using tasks null space projectors built upon the learned models. We illustrate the proposed method on a simulated 3 degrees of freedom planar robot. This system is used as a benchmark to compare our method to an alternative approach based on learning an inverse of the extended Jacobian. We show the better versatility of the retained approach with respect to the latter.
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1997 European Control Conference (ECC), 1997
Iterative learning control applied to a simpli ed model of a robot arm is studied. The iterative learning control input signal is used in combination with conventional feedback and feed-forward control, and the aim is to let the learning control signal handle the e ects of unmodeled dynamics and friction. Convergence and robustness aspects of the choice of lters in the updating scheme of the iterative learning control signal are studied.
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10th IFAC Symposium on Robot Control, 2012
This paper proposes an approach for online learning of the dynamic model of a robot manipulator. The dynamic model is formulated as a weighted sum of locally linear models, and Locally Weighted Projection Regression (LWPR) is used to learn the models based on training data obtained during operation. The LWPR model can be initialized with partial knowledge of rigid body parameters to improve the initial performance. The resulting dynamic model is used to implement a model-based controller. Both feedforward and feedback configurations are investigated. The proposed approach is tested on an industrial robot, and shown to outperform independent joint and fixed model-based control.
Learning-based robot control with localized sparse online Gaussian process
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013
In recent years, robots have been increasingly utilized in applications with complex unknown environments, which makes system modeling challenging. In order to meet the demand from such applications, an experience-based learning approach can be used. In this paper, a novel learning algorithm is proposed, which can learn an unknown system model from given data iteratively using a localization approach to manage the computational costs for real time applications. The algorithm segments the data domain by measuring significance of data. As case studies, the proposed algorithm is tested on the control of the mecanum-wheeled robot and in learning the inverse kinematics of a kinematically-redundant manipulator. As the result, the algorithm achieves the on-line system model learning for real time robotics applications.
Learning inverse dynamics for redundant manipulator control
2010 International Conference on Autonomous and Intelligent Systems, AIS 2010, 2010
High performance control of robotic systems, including the new generation of humanoid, assistive and entertainment robots, requires adequate knowledge of the dynamics of the system. This can be problematic in the presence of modeling uncertainties as the performance of classical, modelbased controllers is highly dependant upon accurate knowledge of the system. In addition, future robotic systems such as humanoids are likely to be redundant, requiring a mechanism for redundancy resolution when performing lower degree-of-freedom tasks. In this paper, a learning approach to estimating the inverse dynamic equations is presented. Locally Weighted Projection Regression (LWPR) is used to learn the inverse dynamics of a manipulator in both joint and task space and the resulting controllers are used to drive a 3 and 4 DOF robot in simulation. The performance of the learning controllers is compared to a traditional model based control method and is also shown to be a viable control method for a redundant system.
Learning Control of Robot Manipulators in Task Space
Asian Journal of Control, 2017
Two important properties of industrial tasks performed by robot manipulators, namely, periodicity (i.e., repetitive nature) of the task and the need for the task to be performed by the end-effector, motivated this work. Not being able to utilize the robot manipulator dynamics due to uncertainties complicated the control design. In a seemingly novel departure from the existing works in the literature, the tracking problem is formulated in the task space and the control input torque is aimed to decrease the task space tracking error directly without making use of inverse kinematics at the position level. A repetitive learning controller is designed which "learns" the overall uncertainties in the robot manipulator dynamics. The stability of the closed-loop system and asymptotic end-effector tracking of a periodic desired trajectory are guaranteed via Lyapunov based analysis methods. Experiments performed on an in-house developed robot manipulator are presented to illustrate the performance and viability of the proposed controller.
Lifelong learning for disturbance rejection on mobile robots
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016
No two robots are exactly the same-even for a given model of robot, different units will require slightly different controllers. Furthermore, because robots change and degrade over time, a controller will need to change over time to remain optimal. This paper leverages lifelong learning in order to learn controllers for different robots. In particular, we show that by learning a set of control policies over robots with different (unknown) motion models, we can quickly adapt to changes in the robot, or learn a controller for a new robot with a unique set of disturbances. Further, the approach is completely model-free, allowing us to apply this method to robots that have not, or cannot, be fully modeled. These preliminary results are an initial step towards learning robust fault-tolerant control for arbitrary robots.
2019 18th European Control Conference (ECC), 2019
In this paper, we propose an online learning approach that enables the inverse dynamics model learned for a source robot to be transferred to a target robot (e.g., from one quadrotor to another quadrotor with different mass or aerodynamic properties). The goal is to leverage knowledge from the source robot such that the target robot achieves highaccuracy trajectory tracking on arbitrary trajectories from the first attempt with minimal data recollection and training. Most existing approaches for multi-robot knowledge transfer are based on post-analysis of datasets collected from both robots. In this work, we study the feasibility of impromptu transfer of models across robots by learning an error prediction module online. In particular, we analytically derive the form of the mapping to be learned by the online module for exact tracking, propose an approach for characterizing similarity between robots, and use these results to analyze the stability of the overall system. The proposed approach is illustrated in simulation and verified experimentally on two different quadrotors performing impromptu trajectory tracking tasks, where the quadrotors are required to accurately track arbitrary hand-drawn trajectories from the first attempt.
Local Models for Learning Inverse Kinematics of Redundant Robots: A Performance Comparison
In this paper we report the results of a comprehensive comparative analysis of the performances of six local models applied to the task of learning the inverse kinematics of a redundant robotic arm (Motoman HP6). The evaluated algorithm are the following ones: SOM-based Local Linear Mapping (LLM), Radial Basis Functions Network (RBFN), Local Model Network (LMN), Local Weighted Regression (LWR), Takagi-Sugeno-Kang Fuzzy Model (TSK) and Local Linear Mapping over Kwinners (KSOM). Each algorithm is evaluated with respect to its accuracy in estimating the joint angles given the Cartesian coordinates along end-effector trajectories within the robot workspace. Also, a careful evaluation of the performances of the aforementioned algorithms is carried out based on correlation analysis of the residuals of the best model.