Learning Contact Dynamics using Physically Structured Neural Networks

Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suffer from pathological behaviour when forecasting for longer time horizons. In this work, we use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations in settings that are traditionally difficult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback—which is heavily relied upon by biological systems—is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations

Learning Contact Dynamics using Physically Structured Neural Networks

Learning physically structured representations of dynamical systems that include contact between different objects is an important problem for learning-based approaches in robotics. Black-box neural networks can learn to approximately represent discontinuous dynamics, but they typically require large quantities of data and often suffer from pathological behaviour when forecasting for longer time horizons. In this work, we use connections between deep neural networks and differential equations to design a family of deep network architectures for representing contact dynamics between objects. We show that these networks can learn discontinuous contact events in a data-efficient manner from noisy observations in settings that are traditionally difficult for black-box approaches and recent physics inspired neural networks. Our results indicate that an idealised form of touch feedback -- which is heavily relied upon by biological systems -- is a key component of making this learning problem tractable. Together with the inductive biases introduced through the network architectures, our techniques enable accurate learning of contact dynamics from observations

Does Medical Students' Preference of Test Format (Computer-based vs. Paper-based) have an Influence on Performance?

<p>Abstract</p> <p>Background</p> <p>Computer-based examinations (CBE) ensure higher efficiency with respect to producibility and assessment compared to paper-based examinations (PBE). However, students often have objections against CBE and are afraid of getting poorer results in a CBE.</p> <p>The aims of this study were (1) to assess the readiness and the objections of students to a CBE vs. PBE (2) to examine the acceptance and satisfaction with the CBE on a voluntary basis, and (3) to compare the results of the examinations, which were conducted in different formats.</p> <p>Methods</p> <p>Fifth year medical students were introduced to an examination-player and were free to choose their format for the test. The reason behind the choice of the format as well as the satisfaction with the choice was evaluated after the test with a questionnaire. Additionally, the expected and achieved examination results were measured.</p> <p>Results</p> <p>Out of 98 students, 36 voluntarily chose a CBE (37%), 62 students chose a PBE (63%). Both groups did not differ concerning sex, computer-experience, their achieved examination results of the test, and their satisfaction with the chosen format. Reasons for the students' objections against CBE include the possibility for outlines or written notices, a better overview, additional noise from the keyboard or missing habits normally present in a paper based exam. The students with the CBE tended to judge their examination to be more clear and understandable. Moreover, they saw their results to be independent of the format.</p> <p>Conclusions</p> <p>Voluntary computer-based examinations lead to equal test scores compared to a paper-based format.</p