Calibration method to improve transfer from simulation to quadruped robots

Using passive compliance in robotic locomotion has been seen as a cheap and straightforward way of increasing the performance in energy consumption and robustness. However, the control for such systems remains quite challenging when using traditional robotic techniques. The progress in machine learning opens a horizon of new possibilities in this direction but the training methods are generally too long and laborious to be conducted on a real robot platform. On the other hand, learning a control policy in simulation also raises a lot of complication in the transfer. In this paper, we designed a cheap quadruped robot and detail a calibration method to optimize a simulation model in order to facilitate the transfer of parametric motor primitives. We present results validating the transfer of Central Pattern Generators (CPG) learned in simulation to the robot which already give positive insights on the validity of this method

A randomized comparison of a sirolimus-eluting stent with a standard stent for coronary revascularization

BACKGROUND: The need for repeated treatment of restenosis of a treated vessel remains the main limitation of percutaneous coronary revascularization. Because sirolimus (rapamycin) inhibits the proliferation of lymphocytes and smooth-muscle cells, we compared a sirolimus-eluting stent with a standard uncoated stent in patients with angina pectoris. METHODS: We performed a randomized, double-blind trial to compare the two types of stents for revascularization of single, primary lesions in native coronary arteries. The trial included 238 patients at 19 medical centers. The primary end point was in-stent late luminal loss (the difference between the minimal luminal diameter immediately after the procedure and the diameter at six months). Secondary end points included the percentage of in-stent stenosis of the luminal diameter and the rate of restenosis (luminal narrowing of 50 percent or more). We also analyzed a composite clinical end point consisting of death, myocardial infarction, and percutaneous or surgical revascularization at 1, 6, and 12 months. RESULTS: At six months, the degree of neointimal proliferation, manifested as the mean (+/-SD) late luminal loss, was significantly lower in the sirolimus-stent group (-0.01+/-0.33 mm) than in the standard-stent group (0.80+/-0.53 mm, P<0.001). None of the patients in the sirolimus-stent group, as compared with 26.6 percent of those in the standard-stent group, had restenosis of 50 percent or more of the luminal diameter (P<0.001). There were no episodes of stent thrombosis. During a follow-up period of up to one year, the overall rate of major cardiac events was 5.8 percent in the sirolimus-stent group and 28.8 percent in the standard-stent group (P<0.001). The difference was due entirely to a higher rate of revascularization of the target vessel in the standard-stent group. CONCLUSIONS: As compared with a standard coronary stent, a sirolimus-eluting stent shows considerable promise for the prevention of neointimal proliferation, restenosis, and associated clinical events

Structured motor exploration for adaptive learning-based tracking in soft robotic manipulators

Kinematic control of soft robotic manipulators is a challenging problem particularly for systems that are both globally and locally redundant. This article presents a learning-based task-space kinematic controller that enables tracking in such soft robotic manipulators. The novelty of the work is a bioinspired structured sampling mechanism that actively regulates the variance in motor movements during motor exploration. It generates a database that is applied to a direct learning architecture, thereby, formulating an inverse model at the position-level. The controller is validated in simulation on a 12 degrees-of-freedom modular manipulator comprised of elemental modules with three longitudinal actuators and one radial actuator. Experiments demonstrate consistency in performance across multiple unseen trajectories and repeatability of each task. Furthermore, the performance remains uncompromised in altered motor conditions, provided task-relevant motor variance. The results exhibit accurate, repeatable, and adaptive tracking behavior of the system and are promising for the advancement of these systems

Learning dynamic models for open loop predictive control of soft robotic manipulators

The soft capabilities of biological appendages like the arms of Octopus vulgaris and elephants' trunks have inspired roboticists to develop their robotic equivalents. Although there have been considerable efforts to replicate their morphology and behavior patterns, we are still lagging behind in replicating the dexterity and efficiency of these biological systems. This is mostly due to the lack of development and application of dynamic controllers on these robots which could exploit the morphological properties that a soft-bodied manipulator possesses. The complexity of these high-dimensional nonlinear systems has deterred the application of traditional model-based approaches. This paper provides a machine learning-based approach for the development of dynamic models for a soft robotic manipulator and a trajectory optimization method for predictive control of the manipulator in task space. To the best of our knowledge this is the first demonstration of a learned dynamic model and a derived task space controller for a soft robotic manipulator. The validation of the controller is carried out on an octopus-inspired soft manipulator simulation derived from a piecewise constant strain approximation and then experimentally on a pneumatically actuated soft manipulator. The results indicate that such an approach is promising for developing fast and accurate dynamic models for soft robotic manipulators while being applicable on a wide range of soft manipulators

Model-Based Reinforcement Learning for Closed-Loop Dynamic Control of Soft Robotic Manipulators

Dynamic control of soft robotic manipulators is an open problem yet to be well explored and analyzed. Most of the current applications of soft robotic manipulators utilize static or quasi-dynamic controllers based on kinematic models or linearity in the joint space. However, such approaches are not truly exploiting the rich dynamics of a soft-bodied system. In this paper, we present a model-based policy learning algorithm for closed-loop predictive control of a soft robotic manipulator. The forward dynamic model is represented using a recurrent neural network. The closed-loop policy is derived using trajectory optimization and supervised learning. The approach is verified first on a simulated piecewise constant strain model of a cable driven under-Actuated soft manipulator. Furthermore, we experimentally demonstrate on a soft pneumatically actuated manipulator how closed-loop control policies can be derived that can accommodate variable frequency control and unmodeled external loads

Learning Global Inverse Kinematics Solutions for a Continuum Robot

This paper presents a learning based approach for obtaining the inverse kinematics (IK) solution for continuum robots. The proposed model learns a particular global solution for IK problem by supervised learning without any prior knowledge about the system. We have developed an approach that solely relies on the sampling method and a unique IK formulation. The convergence of the solution, practically feasible sample data requirements and adaptability of the model is shown with simulations of a redundant continuum robot