Finding muscle activity generating a given motion is a redundant problem, since there are many more muscles than degrees of freedom. The control strategies determining muscle recruitment from a redundant set are still poorly understood. One theory of motor control suggests that motion is produced through activating a small number of muscle synergies, i.e., muscle groups that are activated in a ...

Model-based control strategies for robot manipulators can present numerous performance advantages when an accurate model of the system dynamics is available. In practice, obtaining such a model is a challenging task which involves modeling such physical processes as friction, which may not be well understood and difficult to model. Furthermore, uncertainties in the physical parameters of a syst...

We present new algorithms for inverse optimal control (or inverse reinforcement learning, IRL) within the framework of linearlysolvable MDPs (LMDPs). Unlike most prior IRL algorithms which recover only the control policy of the expert, we recover the policy, the value function and the cost function. This is possible because here the cost and value functions are uniquely defined given the policy...

With extended autonomous capability planetary rovers will be able to carry out more exploration. This paper presents a method of control that provides such autonomy. This controller is based on Inverse Simulation, which considers the path to be taken and the dynamics of the rover. The principle of Inverse Simulation is that the time history for the desired trajectory is used as an input to the ...

Inverse Optimal Control (IOC) has strongly impacted the systems engineering process, enabling automated planner tuning through straightforward and intuitive demonstration. The most successful and established applications, though, have been in lower dimensional problems such as navigation planning where exact optimal planning or control is feasible. In higher dimensional systems, such as humanoi...

In this paper, we present a method for learning the reward function for humanoid locomotion from motioncaptured demonstrations of human running. We show how an approximate, local inverse optimal control algorithm can be used to learn the reward function for this high dimensional domain, and demonstrate how trajectory optimization can then be used to recreate dynamic, naturalistic running behavi...

In this article, we consider the inverse optimal control problem given incomplete observations of an optimal trajectory. We hypothesize that the cost function is constructed as a weighted sum of relevant features (or basis functions). We handle the problem by proposing the recovery matrix, which establishes a relationship between available observations of the trajectory and weights of given can...

We address the inverse problem of Lagrangian identification based on trajectories in the context of nonlinear optimal control. We propose a general formulation of the inverse problem based on occupation measures and complementarity in linear programming. The use of occupation measures in this context offers several advantages from the theoretical, numerical and statistical points of view. We pr...

Progress ha.s been made in comput.ational implementation of speech production based on physiological dat.a. An inverse dynamics model of the speech articulator's l1111sculo-skeletal system. which is the mapping from art.iculator t.rajectories to e\ectromyogl'aphic (EMG) signals, was modeled using the acquired forward dynamics model and temporal (smoot.hness of EMG activation) and range constrai...

The paper deals with the analysis and synthesis of the nonlinear plant by means of the direct inverse neural control. The direct inverse control is one of the simplest control methods at all. But it has some disadvantages which will be briefly discussed: the rate of the input signal is very reduced, problems arise if the mathematical plant description is not known and customary used backpropaga...