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In the Multi-Policy Decision Making (MPDM) framework, a robot's policy is elected by sampling from the distribution of current states, predicting future outcomes through forward simulation, and selecting the policy with the best expected performance. Electing the best plan depends on sampling initial conditions with influential (very high costs) outcomes. Discovering these configurations through random...
A variety of end-user devices involving keypoint-based mapping systems are about to hit the market e.g. as part of smartphones, cars, robotic platforms, or virtual and augmented reality applications. Thus, the generated map data requires automated evaluation procedures that do not require experienced personnel or ground truth knowledge of the underlying environment. A particularly important question...
In this paper, we design provably-good algorithms for task allocation in multi-robot systems in the presence of payoff uncertainty. We consider a group of robots that has to perform a given set of tasks where each robot performs at most one task. The payoffs of the robots doing the tasks are assumed to be Gaussian random variables with known mean and variances. The total payoff of the robots is a...
There has been a great deal of work on learning new robot skills, but very little consideration of how these newly acquired skills can be integrated into an overall intelligent system. A key aspect of such a system is compositionality: newly learned abilities have to be characterized in a form that will allow them to be flexibly combined with existing abilities, affording a (good!) combinatorial explosion...
The exoskeleton robots because of its potential applications in rehabilitation engineering, assistive robotics, and power augmentation are getting more attention in the field of robotics. Besides kinematics model and dynamic model, three type controllers are applied in position control mode, PID control, computed torque control and time delay control, respectively. For the sake of the difficulty in...
Bayesian Optimization has gained much popularity lately, as a global optimization technique for functions that are expensive to evaluate or unknown a priori. While classical BO focuses on where to gather an observation next, it does not take into account practical constraints for a robotic system such as where it is physically possible to gather samples from, nor the sequential nature of the problem...
For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with one another to avoid collisions, while heading toward their respective destinations. Previous approaches have used handcrafted functions based on proximity to model...
This work studies the design of reliable control laws of robotic systems operating in uncertain environments. We introduce a new approach to stochastic policy optimization based on probably approximately correct (PAC) bounds on the expected performance of control policies. An algorithm is constructed which directly minimizes an upper confidence bound on the expected cost of trajectories instead of...
Parallel manipulators play a key role in robotic rehabilitation. In reality, such systems operate under uncertainty due to the changes in the characteristics of the patients and lack of knowledge about the physical and geometrical properties of the system. In this paper, we present a robust control scheme to control a six-degree-of-freedom Stewart platform. In this application, it is aimed to follow...
Expert based learning algorithms have been used by robots to choose satisfying reactions to human movements. These algorithms often demonstrate random performance that tries to hit a balance between adaptiveness and consistency that matches human's preferences intuitively. This paper provides a rigorous way to quantify the adaptiveness and consistency of the expert based learning algorithms in the...
In many robotics tasks involving impacts (e.g. grasping, hitting, kicking) the existence of complex interactions between the physics of the objects and of the robot makes it hard to create an analytical model of the interactions that can be used for prediction and planning. Exploration learning can enable a robot to autonomously learn such tasks and models simultaneously by trial and error. The Cost-Regularized...
Advanced robotic technology has become a significant component in many medical specialties, including in rehabilitation tasks such as physiotherapy. Rehabilitation programs are a practical approach created to assist patients, such as stroke victims, in retrieving their missing functional capacity, obtaining new skills, and enhancing their quality of life. Nevertheless, rehabilitation treatments need...
Robotic behaviors are mainly described by differential equations. Those mathematical models are usually not precise enough because of inaccurately known parameters or model simplifications. Nevertheless, robots are often used in critical contexts as medical or military fields. So, uncertainties in mathematical models have to be taken into account in order to produce reliable and safe analysis results...
Online model-free reinforcement learning (RL) methods with continuous actions are playing a prominent role when dealing with real-world applications such as Robotics. However, when confronted to non-stationary environments, these methods crucially rely on an exploration-exploitation trade-off which is rarely dynamically and automatically adjusted to changes in the environment. Here we propose an active...
This paper presents a Cartesian adaptive control based on a robust Sliding Mode and Time Delay Estimation (CSMTDE) for controlling a redundant exoskeleton robot called ETS-MARSE subject to uncertain nonlinear dynamics and external forces. The robustness and accuracy are achieved by selecting a sliding Cartesian surface and a sliding joint surface. The combination between them is done by the nonlinear...
This paper proposes a non-singular terminal sliding mode combined with time delay controller for fast tracking trajectory of uncertain second-order nonlinear systems in presence of external disturbances. The motivation for using Non-Singular Terminal Sliding Mode (NSTSM) mainly relies on its appreciable features, such as simplicity of design and implementation, high precision and fast convergence...
This paper considers a tractable simplified problem of model-based Bayesian reinforcement learning (BRL) in terms of real-world samples, computational complexity, and target uncertainties. Robust control and adaptive control are two of the most successful and tractable conventional control design theories against uncertainties in various domain, while they have contrasting ideas. We show that both...
We consider the Chance Constrained Model Predictive Control problem for polynomial systems subject to disturbances. In this problem, we aim at finding optimal control input for given disturbed dynamical system to minimize a given cost function subject to probabilistic constraints, over a finite horizon. The control laws provided have a predefined (low) risk of not reaching the desired target set....
This paper addresses adaptive synchronization control problem of networked robot systems characterized by Lagrangian function, where exact dynamic models are unknown and velocity measurements are unavailable. A class of distributed observers, comprised of multiple dynamic variables and static variables, are established based on not imposing a priori restriction on the boundness of the observer states...
In this paper, we address the tracking control problems of a robotic system with uncertain dynamics. To cope with the problem of the unknown nonlinear function terms in the system and improve the robustness, adaptive fuzzy logic control is proposed for an approximation of uncertain parameters to achieve the control objectives. Furthermore, both non-constraint and output constraint are considered in...
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