The trade-off between morphology and control in the co-optimized design of robots.

Conventionally, robot morphologies are developed through simulations and calculations, and different control methods are applied afterwards. Assuming that simulations and predictions are simplified representations of our reality, how sure can roboticists be that the chosen morphology is the most adequate for the possible control choices in the real-world? Here we study the influence of the design parameters in the creation of a robot with a Bayesian morphology-control (MC) co-optimization process. A robot autonomously creates child robots from a set of possible design parameters and uses Bayesian Optimization (BO) to infer the best locomotion behavior from real world experiments. Then, we systematically change from an MC co-optimization to a control-only (C) optimization, which better represents the traditional way that robots are developed, to explore the trade-off between these two methods. We show that although C processes can greatly improve the behavior of poor morphologies, such agents are still outperformed by MC co-optimization results with as few as 25 iterations. Our findings, on one hand, suggest that BO should be used in the design process of robots for both morphological and control parameters to reach optimal performance, and on the other hand, point to the downfall of current design methods in face of new search techniques.his work was enabled by funding provided by the RoboSoft Coordination Action project (FP7-ICT-2013-C project 619319)

Marker-free surgical navigation of rod bending using a stereo neural network and augmented reality in spinal fusion

The instrumentation of spinal fusion surgeries includes pedicle screw placement and rod implantation. While several surgical navigation approaches have been proposed for pedicle screw placement, less attention has been devoted towards the guidance of patient-specific adaptation of the rod implant. We propose a marker-free and intuitive Augmented Reality (AR) approach to navigate the bending process required for rod implantation. A stereo neural network is trained from the stereo video streams of the Microsoft HoloLens in an end-to-end fashion to determine the location of corresponding pedicle screw heads. From the digitized screw head positions, the optimal rod shape is calculated, translated into a set of bending parameters, and used for guiding the surgeon with a novel navigation approach. In the AR-based navigation, the surgeon is guided step-by-step in the use of the surgical tools to achieve an optimal result. We have evaluated the performance of our method on human cadavers against two benchmark methods, namely conventional freehand bending and marker-based bending navigation in terms of bending time and rebending maneuvers. We achieved an average bending time of 231s with 0.6 rebending maneuvers per rod compared to 476s (3.5 rebendings) and 348s (1.1 rebendings) obtained by our freehand and marker-based benchmarks, respectively

Automatic registration with continuous pose updates for marker-less surgical navigation in spine surgery

Established surgical navigation systems for pedicle screw placement have been proven to be accurate, but still reveal limitations in registration or surgical guidance. Registration of preoperative data to the intraoperative anatomy remains a time-consuming, error-prone task that includes exposure to harmful radiation. Surgical guidance through conventional displays has well-known drawbacks, as information cannot be presented in-situ and from the surgeon's perspective. Consequently, radiation-free and more automatic registration methods with subsequent surgeon-centric navigation feedback are desirable. In this work, we present a marker-less approach that automatically solves the registration problem for lumbar spinal fusion surgery in a radiation-free manner. A deep neural network was trained to segment the lumbar spine and simultaneously predict its orientation, yielding an initial pose for preoperative models, which then is refined for each vertebra individually and updated in real-time with GPU acceleration while handling surgeon occlusions. An intuitive surgical guidance is provided thanks to the integration into an augmented reality based navigation system. The registration method was verified on a public dataset with a median of 100% successful registrations, a median target registration error of 2.7 mm, a median screw trajectory error of 1.6°and a median screw entry point error of 2.3 mm. Additionally, the whole pipeline was validated in an ex-vivo surgery, yielding a 100% screw accuracy and a median target registration error of 1.0 mm. Our results meet clinical demands and emphasize the potential of RGB-D data for fully automatic registration approaches in combination with augmented reality guidance

The trade-off between morphology and control in the co-optimized design of robots.

Conventionally, robot morphologies are developed through simulations and calculations, and different control methods are applied afterwards. Assuming that simulations and predictions are simplified representations of our reality, how sure can roboticists be that the chosen morphology is the most adequate for the possible control choices in the real-world? Here we study the influence of the design parameters in the creation of a robot with a Bayesian morphology-control (MC) co-optimization process. A robot autonomously creates child robots from a set of possible design parameters and uses Bayesian Optimization (BO) to infer the best locomotion behavior from real world experiments. Then, we systematically change from an MC co-optimization to a control-only (C) optimization, which better represents the traditional way that robots are developed, to explore the trade-off between these two methods. We show that although C processes can greatly improve the behavior of poor morphologies, such agents are still outperformed by MC co-optimization results with as few as 25 iterations. Our findings, on one hand, suggest that BO should be used in the design process of robots for both morphological and control parameters to reach optimal performance, and on the other hand, point to the downfall of current design methods in face of new search techniques.his work was enabled by funding provided by the RoboSoft Coordination Action project (FP7-ICT-2013-C project 619319)

Angular amplitude of the cube.

<p>The amplitude, the third parameter from the child robot, determines the deflection from the origin position in either direction. The front and rear cubes can have different values for their origins, defined as parameters 4 and 5, respectively. In the figure, the origin is 90°-15° = 75°, while the amplitude is 20°.</p

Three-dimensional meniscus allograft sizing - a study of 280 healthy menisci

Background: Inaccurate meniscus allograft size is still an important problem of the currently used sizing methods. The purpose of this study was to evaluate a new three-dimensional (3D) meniscus-sizing method to increase the accuracy of the selected allografts. Methods: 3D triangular surface models were generated from 280 menisci based on 50 bilateral and 40 unilateral knee joint magnetic resonance imaging (MRI) scans. These models served as an imaginary meniscus allograft tissue bank. Meniscus sizing and allograft selection was simulated for all 50 bilateral knee joints by (1) the closest mean surface distance (MeSD) (3D-MRI sizing with contralateral meniscus), (2) the smallest meniscal width/length difference in MRI (2D-MRI sizing with contralateral meniscus), and (3) conventional radiography as proposed by Pollard (2D-radiograph (RX) sizing with ipsilateral tibia plateau). 3D shape and meniscal width, length, and height were compared between the original meniscus and the selected meniscus using the three sizing methods. Results: Allograft selection by MeSD (3D MRI) was superior for all measurement parameters. In particular, the 3D shape was significantly improved (p < 0.001), while the mean differences in meniscal width, length, and height were only slightly better than the allograft selected by the other methods. Outliers were reduced by up to 55% (vs. 2D MRI) and 83% (vs. 2D RX) for the medial meniscus and 39% (vs. 2D MRI) and 56% (vs. 2D RX) for the lateral meniscus. Conclusion: 3D-MRI sizing by MeSD using the contralateral meniscus as a reconstruction template can significantly improve meniscus allograft selection. Sizing using conventional radiography should probably not be recommended. Trial registration: Kantonale Ethikkommission Zürich had given the approval for the study (BASEC-No. 2018-00856

Morphological parameters.

<p>The morphological parameters of the child robot can be seen above. While the first parameter determines the orientation of the second cube in respect to the first, the second parameter determines the off-set between these cubes. In the example above, the first parameter is 1 while the second parameter is 3, with a 1 cm gap between the cubes.</p

Bootstrapping C processes from MC.

<p>(I) The C optimization uses the best MC case as initial seed and tries to further improve it. (II) As observed with the MC optimization, the design parameters are initially explored to be later exploited until no further improvement is seen, eventually returning to landscape exploration. (III) The behavior of non-optimal morphologies, observed at ɑ = 0.2 and ɑ = 0.4, are further improved with the C optimization, while the last morphology found at ɑ = 0.6 and afterwards could not have its behavior further improved by C methods. (IV) The best control parameters observed at the end of MC and C processes reveal that small morphological changes, as observed between (1 1) and (1 2), can strongly influence the behavior, even if the control remains unchanged, i.e. (1 20 1).</p