274 research outputs found
Effects of self-modeling on batting performance
The effect of a self-modeling video plus batting practice program on teaching baseball players to hit from the nondominant side was investigated with 9 male subjects.
[This is an excerpt from the abstract. For the complete abstract, please see the document.
The creation and validation of a youth fundamental hitting scale: The assessment of youth baseball and softball hitting fundamentals and the perceived psychological barriers to hitting a pitched ball
The primary purpose of the thesis was to create a hitting scale for youth players that assess the fundamentals of a baseball swing. Secondly, the purpose of this thesis was to determine the interactions between the changes in anxiety, perceived competence and fear variables have with actual hitting competence over a four week sport specific training program
Probabilistic movement primitives
Movement Primitives (MP) are a well-established approach for representing modular
and re-usable robot movement generators. Many state-of-the-art robot learning
successes are based MPs, due to their compact representation of the inherently
continuous and high dimensional robot movements. A major goal in robot learning
is to combine multiple MPs as building blocks in a modular control architecture
to solve complex tasks. To this effect, a MP representation has to allow for
blending between motions, adapting to altered task variables, and co-activating
multiple MPs in parallel. We present a probabilistic formulation of the MP concept
that maintains a distribution over trajectories. Our probabilistic approach
allows for the derivation of new operations which are essential for implementing
all aforementioned properties in one framework. In order to use such a trajectory
distribution for robot movement control, we analytically derive a stochastic feedback
controller which reproduces the given trajectory distribution. We evaluate
and compare our approach to existing methods on several simulated as well as
real robot scenarios
Learning sequential motor tasks
Many real robot applications require the sequential use of multiple distinct motor primitives. This requirement implies the need to learn the individual primitives as well as a strategy to select the primitives sequentially. Such hierarchical learning problems are commonly either treated as one complex monolithic problem which is hard to learn, or as separate tasks learned in isolation. However, there exists a strong link between the robots strategy and its motor primitives. Consequently, a consistent framework is needed that can learn jointly on the level of the individual primitives and the robots strategy. We present a hierarchical learning method which improves individual motor primitives and, simultaneously, learns how to combine these motor primitives sequentially to solve complex motor tasks. We evaluate our method on the game of robot hockey, which is both difficult to learn in terms of the required motor primitives as well as its strategic elements
Merging Position and Orientation Motion Primitives
In this paper, we focus on generating complex robotic trajectories by merging
sequential motion primitives. A robotic trajectory is a time series of
positions and orientations ending at a desired target. Hence, we first discuss
the generation of converging pose trajectories via dynamical systems, providing
a rigorous stability analysis. Then, we present approaches to merge motion
primitives which represent both the position and the orientation part of the
motion. Developed approaches preserve the shape of each learned movement and
allow for continuous transitions among succeeding motion primitives. Presented
methodologies are theoretically described and experimentally evaluated, showing
that it is possible to generate a smooth pose trajectory out of multiple motion
primitives
Movement primitives with multiple phase parameters
Movement primitives are concise movement representations that can be learned from human demonstrations, support generalization to novel situations and modulate the speed of execution of movements. The speed modulation mechanisms proposed so far are limited though, allowing only for uniform speed modulation or coupling changes in speed to local measurements of forces, torques or other quantities. Those approaches are not enough when dealing with general velocity constraints. We present a movement primitive formulation that can be used to non-uniformly adapt the speed of execution of a movement in order to satisfy a given constraint, while maintaining similarity in shape to the original trajectory. We present results using a 4-DoF robot arm in a minigolf setup
- …