Timber Wars and Aftermath in Northwest Coastal California

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Implementation of Word Solving Strategies During First Grade Guided Reading

Effective reading instruction in the primary grades is essential to students’ future academic success. Research has shown that there are different methods in which students can be taught to figure out unknown words as they are reading. The study took place during small group, guided reading time. The study included five first grade students in the researcher’s classroom that were reading below grade level. The purpose of this purposive study was to determine the effectiveness of two different word-solving strategies. The first research question that guided this study was, “What impact does utilizing the cueing system during guided reading have on students’ reading ability that are reading below grade level?” The second research question that guided this study was, “What impact does utilizing decoding and other word solving strategies have on students that are reading below grade level?” The researcher conducted Running Record assessments weekly to determine her students’ reading progress. Data were analyzed to determine which word-solving strategy was most effective at helping students progress with their reading and word-solving skills. It was determined that neither strategy was most effective at helping students progress with their reading and word-solving skills, rather the needs of the students dictated the most effective and appropriate strategy

The Effect of Ankle Weights on the Jumping Performance of a Selected Group of High School Basketball Players

The purpose of this study was to ascertain what effects the use of ankle weights would have on standing broad jumping performance of selected high school basketball players. Three groups of twelve subjects each were used in this study. Each group was tested at the start of the experimental period, after six v/eeks of training and at the end of the twelfth training week. The test involved standing broad jumping performance measured to the nearest one-fourth inch. Comparisons were made between the means within each group on the pre-, mid-, and final tests. Comparisons were also made between the three groups by testing the significance of the differences between the group means on the pre-,_ mid-, and final tests. The null hypothesis was assumed in making comparisons with rejection at the .01 level. This hypothesis was tested with the t technique for the significance of the difference between means. The results of the comparisons showed that the use of ankle weights can result in a significant amount of improvement in jumping performance at the .01 level during the first six weeks of training, and that there may be little real value in continuing to use them after this time

Predictive and Robust Robot Assistance for Sequential Manipulation

This paper presents a novel concept to support physically impaired humans in daily object manipulation tasks with a robot. Given a user's manipulation sequence, we propose a predictive model that uniquely casts the user's sequential behavior as well as a robot support intervention into a hierarchical multi-objective optimization problem. A major contribution is the prediction formulation, which allows to consider several different future paths concurrently. The second contribution is the encoding of a general notion of constancy constraints, which allows to consider dependencies between consecutive or far apart keyframes (in time or space) of a sequential task. We perform numerical studies, simulations and robot experiments to analyse and evaluate the proposed method in several table top tasks where a robot supports impaired users by predicting their posture and proactively re-arranging objects

Assessing Transferability from Simulation to Reality for Reinforcement Learning

Learning robot control policies from physics simulations is of great interest to the robotics community as it may render the learning process faster, cheaper, and safer by alleviating the need for expensive real-world experiments. However, the direct transfer of learned behavior from simulation to reality is a major challenge. Optimizing a policy on a slightly faulty simulator can easily lead to the maximization of the `Simulation Optimization Bias` (SOB). In this case, the optimizer exploits modeling errors of the simulator such that the resulting behavior can potentially damage the robot. We tackle this challenge by applying domain randomization, i.e., randomizing the parameters of the physics simulations during learning. We propose an algorithm called Simulation-based Policy Optimization with Transferability Assessment (SPOTA) which uses an estimator of the SOB to formulate a stopping criterion for training. The introduced estimator quantifies the over-fitting to the set of domains experienced while training. Our experimental results on two different second order nonlinear systems show that the new simulation-based policy search algorithm is able to learn a control policy exclusively from a randomized simulator, which can be applied directly to real systems without any additional training

Learning from Few Demonstrations with Frame-Weighted Motion Generation

Learning from Demonstration (LfD) enables robots to acquire versatile skills by learning motion policies from human demonstrations. It endows users with an intuitive interface to transfer new skills to robots without the need for time-consuming robot programming and inefficient solution exploration. During task executions, the robot motion is usually influenced by constraints imposed by environments. In light of this, task-parameterized LfD (TP-LfD) encodes relevant contextual information into reference frames, enabling better skill generalization to new situations. However, most TP-LfD algorithms typically require multiple demonstrations across various environmental conditions to ensure sufficient statistics for a meaningful model. It is not a trivial task for robot users to create different situations and perform demonstrations under all of them. Therefore, this paper presents a novel algorithm to learn skills from few demonstrations. By leveraging the reference frame weights that capture the frame importance or relevance during task executions, our method demonstrates excellent skill acquisition performance, which is validated in real robotic environments.Comment: Accepted by ISER. For the experiment video, see https://youtu.be/JpGjk4eKC3

Learning Utility Surfaces for Movement Selection

Humanoid robots are highly redundant systems with respect to the tasks they are asked to perform. This redundancy manifests itself in the number of degrees of freedom of the robot exceeding the dimensionality of the task. Traditionally this redundancy has been utilised through optimal control in the null-space. Some cost function is defined that encodes secondary movement goals and movements are optimised with respect to this functio