No abstract

In electrically actuated robots most energy losses are due to the heating of the actuators. This energy loss can be greatly reduced with parallel elastic actuators, by optimizing the elastic element such that it delivers most of the required torques. Previously used optimization methods relied on parameterizing the spring characteristic, thereby limiting the set of spring characteristics optimized over and with that the loss reduction that can be obtained. This paper shows that such parametrization is not necessary; a method is presented to compute the optimal characteristic as an analytic function of the trajectory. The efficacy of this method is demonstrated using two examples. The first example considers the optimal spring characteristic for a parallel elastic actuator supporting the human ankle during walking. The second example applies the method in combination with trajectory optimization on a single degree of freedom robot performing a specific pick-andplace task. The task at hand has a height difference between the pick and the place location. With the analytical optimal spring, it is shown that the robot can recover enough of the energy released by the package to function without external electric energy supply.

Germline mutations in the mismatch repair genes MLH1, MSH2, MSH6, and PMS2 predispose to Lynch syndrome (also known as hereditary non-polyposis colorectal cancer). Recently, we have shown that the CHEK2 1100delC mutation also is associated with Lynch syndrome/Lynch syndrome-associated families albeit in a polygenic setting. Two of the ten CHEK2 1100delC positive Lynch syndrome families additionally carried a pathogenic MLH1 or MSH6 mutation, suggesting that mutations in mismatch repair genes may be involved in CHEK2 1100delC-associated cancer phenotypes. A phenotype of importance is hereditary breast and colorectal cancer (HBCC), with the CHEK2 1100delC mutation present in almost one-fifth of the families-again in a polygenic setting. In order to evaluate the involvement of MSH6 in polygenic CHEK2 cancer susceptibility, we, here, have analyzed the entire MSH6 coding sequence for genetic alterations in 68 HBCC breast cancer families. Rare MSH6 variants, with population frequencies below 1%, were identified in 11.8% of HBCC breast cancer families, whereas the same variants were identified in only 1.5% of population controls, suggesting that rare MSH6 variants are associated with HBCC breast cancer (P B 0.00001). However, screening of the entire MSH6 coding sequence in 68 non-HBCC breast cancer families showed a similar association (8.8 vs. *1.4% in controls, P B 0.001), suggesting that rare MSH6 variants are not confined to HBCC breast cancer. Together, our data suggest that rare MSH6 variants may predispose to familial breast cancer. However, none of the rare MSH6 variants are obviously pathogenic, suggesting that a more subtle disease mechanism may operate in breast carcinogenesis.

Humans often demonstrate diverse behaviors due to their personal preferences, for instance, related to their individual execution style or personal margin for safety. In this paper, we consider the problem of integrating both path and velocity preferences into trajectory planning for robotic manipulators. We first learn reward functions that represent the user path and velocity preferences from kinesthetic demonstration. We then optimize the trajectory in two steps, first the path and then the velocity, to produce trajectories that adhere to both task requirements and user preferences. We design a set of parameterized features that capture the fundamental preferences in a pick-and-place type of object transportation task, both in the shape and timing of the motion. We demonstrate that our method is capable of generalizing such preferences to new scenarios. We implement our algorithm on a Franka Emika 7-DoF robot arm and validate the functionality and flexibility of our approach in a user study. The results show that non-expert users are able to teach the robot their preferences with just a few iterations of feedback.