Inverse optimal control (IOC) is a powerful approach for learning robotic controllers from demonstration that estimates a cost function which rationalizes demonstrated control trajectories. Unfortunately, its applicability is difficult in settings where optimal control can only be solved approximately. Local IOC approaches rationalize demonstrated trajectories based on a linear-quadratic approx...

We develop an object trajectory pattern learning method that has two significant advantages over past work. First, we represent trajectories in the HMM parameter space which overcomes the trajectory sampling problems of the existing methods. The proposed features are more expressive and enable detection of trajectory patterns that cannot be detected with the conventional trajectory representati...

We present two different optimal control problem formulations that can be used to solve various trajectory planning scenarios of an Unmanned Ground Vehicle (UGV). It is less a study of trajectory planning than it is an analysis of the optimal control planning method itself. Numerous issues regarding vehicle modeling, obstacle modeling and computational complexity are discussed. The results and ...

Abstract: This paper proposes a robust-optimal trajectory design method for uncertain system to minimize the expected value of objective function. The basic idea is solving Stochastic Differential Dynamic Programming (SDDP), which solve optimal control problem to minimize the expected value of cost-to-go function, with Unscented Transform, which is used to estimate the expected value. Most rece...

We present one of the first algorithms on model based reinforcement learning and trajectory optimization with free final time horizon. Grounded on the optimal control theory and Dynamic Programming, we derive a set of backward differential equations that propagate the value function and provide the optimal control policy and the optimal time horizon. The resulting policy generalizes previous re...

Innovative solar system exploration missions require ever larger velocity increments and thus ever more demanding propulsion capabilities. Using for those high-energy missions the stateof-the-art technique of chemical propulsion in combination with (eventually multiple) gravity assist maneuvers results in long, complicated, and inflexible mission profiles. Low-thrust propulsions systems can sig...

The Space Shuttle’s two-dimensional entry trajectory planning method is extended to three dimensions. Both angle-of-attack and angle-of-bank variations are used to control the entry trajectory. The trajectory planning is done with a third-order system of differential equations using the drag and lateral accelerations as intermediate controls. The reduced-order planning problem is solved first i...

This paper describes a general approach for dynamic control of stochastic networks based on fluid model analysis, where in broad terms, the stochastic network is approximated by its fluid analog, an associated fluid control problem is solved and, finally, a scheduling rule for the original system is defined by interpreting the fluid control policy. The main contribution of this paper is to prop...

In this study, we consider a point-to-point motion of a flexible arm mounted on a flexible base, composed of a slide rail and helical extension springs, and then propose a trajectory planning method for reducing residual vibrations. The Lagrangian approach, in conjunction with the assumed mode method, is applied to derive the equations of motion of the system. In order to accurately construct t...

We consider nonlinear stochastic systems that arise in path planning and control of mobile robots. As is typical of almost all nonlinear stochastic systems, the optimally solving problem is intractable. We provide a design approach which yields a tractable design that is quantifiably nearoptimal. We exhibit a “separation” principle under a small noise assumption consisting of the optimal open-l...