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34
Forward models: Supervised learning with a distal teacher
 Cognitive Science
, 1992
"... Internal models of the environment have an important role to play in adaptive systems in general and are of particular importance for the supervised learning paradigm. In this paper we demonstrate that certain classical problems associated with the notion of the \teacher " in supervised learnin ..."
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Cited by 299 (7 self)
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Internal models of the environment have an important role to play in adaptive systems in general and are of particular importance for the supervised learning paradigm. In this paper we demonstrate that certain classical problems associated with the notion of the \teacher " in supervised learning can be solved by judicious use of learned internal models as components of the adaptive system. In particular, we show how supervised learning algorithms can be utilized in cases in which an unknown dynamical system intervenes between actions and desired outcomes. Our approach applies to any supervised learning algorithm that is capable of learning in multilayer networks.
Policy Search for Motor Primitives in Robotics
"... Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in imitation learning. However, most interesting motor learning problems are highdimensional reinforcement learning problems often beyond the reach of current methods. In this paper, we extend previous ..."
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Cited by 70 (17 self)
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Many motor skills in humanoid robotics can be learned using parametrized motor primitives as done in imitation learning. However, most interesting motor learning problems are highdimensional reinforcement learning problems often beyond the reach of current methods. In this paper, we extend previous work on policy learning from the immediate reward case to episodic reinforcement learning. We show that this results in a general, common framework also connected to policy gradient methods and yielding a novel algorithm for policy learning that is particularly wellsuited for dynamic motor primitives. The resulting algorithm is an EMinspired algorithm applicable to complex motor learning tasks. We compare this algorithm to several wellknown parametrized policy search methods and show that it outperforms them. We apply it in the context of motor learning and show that it can learn a complex BallinaCup task using a real Barrett WAM TM robot arm. 1
Improving the Performance of Stabilizing Controls for Nonlinear Systems
 Control Systems Magazine
, 1996
"... There are a variety of tools for computing stabilizing feedback control laws for nonlinear systems. The difficulty is that these tools usually do not take into account the performance of the control and therefore, systematic improvement of an arbitrary stabilizing control law is extremely difficult ..."
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Cited by 21 (14 self)
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There are a variety of tools for computing stabilizing feedback control laws for nonlinear systems. The difficulty is that these tools usually do not take into account the performance of the control and therefore, systematic improvement of an arbitrary stabilizing control law is extremely difficult and often impossible. The objective of this paper is to present a design algorithm that addresses this problem. The algorithm that we present iteratively computes a sequence of control laws with increasingly improved performance. We also consider implementation issues and discuss some of the successes and difficulty that we have encountered. Finally, we present a number of illustrative examples and compare our algorithm with perturbation methods. Keywords: Nonlinear Control, Suboptimal Design Methodology, Galerkin's Spectral Method, Feedback Synthesis. Correspondence should be sent to Randy Beard, 444 CB BYU, Provo, Utah 84602, beard\Omega ee.byu.edu 1 Introduction If a system is modele...
An Approach to Rough Terrain Autonomous Mobility
 IN INTERNATIONAL CONFERENCE ON MOBILE PLANETARY ROBOTS
, 1998
"... Offroad autonomous navigation is one of the most difficult automation challenges from the point of view of constraints on mobility, speed of motion, lack of environmental structure, density of hazards, and typical lack of prior information. This paper describes an autonomous navigation software sy ..."
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Cited by 17 (0 self)
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Offroad autonomous navigation is one of the most difficult automation challenges from the point of view of constraints on mobility, speed of motion, lack of environmental structure, density of hazards, and typical lack of prior information. This paper describes an autonomous navigation software system for outdoor vehicles which includes perception, mapping, obstacle detection and avoidance, and goal seeking. It has been used on several vehicle testbeds including autonomous HMMWV's and planetary rover prototypes. To date, it has achieved speeds of 15 km/hr and excursions of 15 km. We introduce algorithms for optimal processing and computational stabilization of range imagery for terrain mapping purposes. We formulate the problem of trajectory generation as one of predictive control searching trajectories in command space. We also formulate the problem of goal arbitration in local autonomous mobility as an optimal control problem. We emphasize the modeling of vehicles in state space ...
Robot planning in the space of feasible actions: Two examples
 In Proc. of the IEEE Int. Conf. on Robotics&Automation (ICRA
, 1996
"... Several researchers in robotics and artijicial intelligence have found that the commonly used method ofplanning in a state (conjiguration) space is intractable in certain domains. This may be because the Cspace has very high dimensionality, the “Cspace obstacles ” are too diflcult to compute, or; ..."
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Cited by 11 (0 self)
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Several researchers in robotics and artijicial intelligence have found that the commonly used method ofplanning in a state (conjiguration) space is intractable in certain domains. This may be because the Cspace has very high dimensionality, the “Cspace obstacles ” are too diflcult to compute, or; because a mapping between desired states and actions is not straightforward. Instead of using an inverse model that relates a desired state to an action to be executed by a robot, we have used a methodology that selects between the feasible actions that a robot might execute, in effect, circumventing many of the problems faced by configuration space planners. In this paper we discuss the implications of such a method and present two examples of working systems that employ this methodology. One system drives an autonomous crosscountry vehicle while the other controls a robotic excavator performing a trenching operation. 1
Approximate Solutions to the TimeInvariant HamiltonJacobiBellman Equation
, 1998
"... In this paper we develop a new method to approximate the solution to the HamiltonJacobiBellman (HJB) equation which arises in optimal control when the plant is modeled by nonlinear dynamics. The approximation is comprised of two steps. First, successive approximation is used to reduce the HJB equat ..."
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Cited by 11 (4 self)
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In this paper we develop a new method to approximate the solution to the HamiltonJacobiBellman (HJB) equation which arises in optimal control when the plant is modeled by nonlinear dynamics. The approximation is comprised of two steps. First, successive approximation is used to reduce the HJB equation to a sequence of linear partial differential equations. These equations are then approximated via Galerkin's spectral method. The resulting algorithm has several important advantages over previously reported methods. Namely, the resulting control is in feedback form and its associated region of attraction is well defined. In addition, all computations are performed offline and the control can be made arbitrarily close to optimal. Accordingly this paper presents a new tool for designing nonlinear control systems that adhere to a prescribed integral performance criteria. Key Words: Nonlinear control, optimal control, HamiltonJacobiBellman equation, feedback synthesis, successive approxi...
Designing Motion Guides for Ergonomic Collaborative Manipulation
 In IEEE International Conference on Robotics and Automation
, 2000
"... Manual materials handling of heavy loads is a common cause of low back disorders. The manual manipulation of a heavy load may be made more comfortable by constraining the load to move along a guide. If the load is constrained, the human operator can provide forces in directions that are comfortable ..."
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Cited by 8 (1 self)
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Manual materials handling of heavy loads is a common cause of low back disorders. The manual manipulation of a heavy load may be made more comfortable by constraining the load to move along a guide. If the load is constrained, the human operator can provide forces in directions that are comfortable while the frictionless guide directs the motion of the load to the goal configuration. In this paper we study the design of such motion guides for ergonomic materials handling. We formulate the problem and provide some example guides for planar manipulation. The motion guides may be implemented by fixed rail systems or by programmable constraint machines (cobots). 1 Introduction Manual materials handling exposes the worker to known risk factors for low back disorders, such as lifting, bending, twisting, and maintenance of static postures. Studies have shown that low back disorders account for up to 40% of all work compensation costs (Marras et al. [9]). Recognizing the importance of the pr...
Hyperbolic Optimal Control and Fuzzy Control
 IEEE Trans. Systems, Man and Cybernetics
, 1999
"... In this paper we consider a new approach to fuzzy control which entails the formulation of a novel statespace representation and a new form of optimal control problem. Basically, in this new formulation, linear functions in the conventional state space representation and cost functional are rep ..."
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Cited by 8 (6 self)
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In this paper we consider a new approach to fuzzy control which entails the formulation of a novel statespace representation and a new form of optimal control problem. Basically, in this new formulation, linear functions in the conventional state space representation and cost functional are replaced by hyperbolic functions. We give a solution for this new, in nitetime, optimal control problem, which we call hyperbolic optimal control. Furthermore, we show that the resulting optimal controller is in fact a Mamdanitype fuzzy controller with Gaussian membership functions and center of gravity defuzzi cation.
Control of Virtual Motion Systems
, 1993
"... We introduce a new control problem: the control of motion simulating devices (Virtual Motion Systems, or VMS) for walking and running humans and robots in a fashion that feels most "realistic," that is, like locomoting on ground. After developing simplified dynamical models for the VMS, the human/ro ..."
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Cited by 8 (0 self)
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We introduce a new control problem: the control of motion simulating devices (Virtual Motion Systems, or VMS) for walking and running humans and robots in a fashion that feels most "realistic," that is, like locomoting on ground. After developing simplified dynamical models for the VMS, the human/robot and the resulting coupled system, we cast the problem in terms of a performance index. This approach permits application of standard optimal control theory. We present two solutions and discuss upcoming problems in the task domain of virtual motion control. Figure 1: Legged robot on a treadmill 1 Introduction Sensation in Artificial Reality environments could be considerably enriched if we could provide a realistic simulation of locomotion [6]. People could walk and run in any direction, without limitation, while guided by visual and auditory impressions from their head mounted displays. At the same time, however, they would not go anywhere, because they are moving on a "virtual motion ...
Proper Orthogonal Decomposition in Optimal Control of Fluids
 Int. J. Numer. Meth. Fluids
, 1999
"... In this article, we present a reduced order modeling approach suitable for active control of fluid dynamical systems based on proper orthogonal decomposition (POD). The rationale behind the reduced order modeling is that numerical simulation of NavierStokes equations is still too costly for the pur ..."
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Cited by 8 (0 self)
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In this article, we present a reduced order modeling approach suitable for active control of fluid dynamical systems based on proper orthogonal decomposition (POD). The rationale behind the reduced order modeling is that numerical simulation of NavierStokes equations is still too costly for the purpose of optimization and control of unsteady flows. We examine the possibility of obtaining reduced order models that reduce computational complexity associated with the NavierStokes equations while capturing the essential dynamics by using the POD. The POD allows extraction of certain optimal set of basis functions  perhaps few  from a computational or experimental database through an eigenvalue analysis. The solution is then obtained as a linear combination of these optimal set of basis functions by means of Galerkin projection. This makes it attractive for optimal control and estimation of systems governed by partial differential equations. We here use it in active control of fluid flows ...