In this paper, we present a method that allows the conditioning of Neural Fields using Regularization by Denoising (RED). As opposed to learning a joint convolutional neural network to condition the output of a neural field, the RED framework is memory-efficient. It allows us to decouple the conditioning network and neural field optimization entirely. We focus our work on applications for 3D sparse-view X-ray Computed Tomography (CT) and propose a flexible procedure that does not assume coordinate-friendly partitioning of the forward operator. Indeed, our method applies to any CT geometry, particularly Cone-Beam CT, which is the most common setup in industrial inspection. We quantitatively evaluate our approach and show that our method is either better or on par with the state-of-the-art regarding reconstruction quality while being the most memory-efficient. Code is available at https://github.com/romainvo/nef-red.

To overcome the limitations of the so-called Industry 4.0 focusing on mass production and full automation, a novel paradigm was recently introduced, namely Industry 5.0, which aims at an increased collaboration between humans and machines, and particularly robots, instead of replacing the former with the latter. This challenge requires novel interactive intelligent robots able to perform complex tasks easily and efficiently and to collaborate on the fly with humans whenever required, be it for training or working. In this work, the Robot Companion, a novel demonstrator of this paradigm, is introduced. It combines robotics, Artificial Intelligence, software engineering and embedded systems technologies, and targets industrial assembly tasks. First tests show that this robot can efficiently assemble a representative gear system autonomously or in collaboration with human operators.

This paper presents a new image-based visual servoing approach to control a robotic system equipped with an ultrasound (US) imaging device. The presented method allows an automatic positioning of the probe with respect to an object of interest. Moments-based image features are computed from three orthogonal US images to servo in-plane and out-of-plane motions of the system. An efficient segmentation method, based on graph-cut strategy, is proposed to extract the object contour in each image plane. Simulation results demonstrate that this approach improves upon techniques based on a single 2-D US image in terms of probe positioning. Our method was also validated from robotic experiments performed on a US phantom with the use of a motorized 3-D probe that provides the three US images.

This paper presents a three dimensional ultrasound (3DUS)-based visual servoing technique for intraoperative tracking of the motion of both surgical instruments and tissue targets. In the proposed approach, visual servoing techniques are used to control the position of a virtual ultrasound probe so as to keep a target centered within the virtual probe's field-of-view. Multiple virtual probes can be servoed in parallel to provide simultaneous tracking of instruments and tissue. The technique is developed in the context of robotic beating-heart intracardiac surgery in which the goal of tracking is to both provide guidance to the operator as well as to provide the means to automate the surgical procedure. To deal with the low signal-to-noise ratio (SNR) of the 3DUS volumes, an intensity-based method is proposed that requires no primitive extraction or image segmentation since it directly utilizes the image intensity information as a visual feature. This approach is computationally efficient and can be applied to a wide range of tissue types and medical instruments. This paper presents the first validation of these techniques through offline robot and tissue tracking using actual in vivo cardiac volume sequences from a robotic beating-heart surgery.

In this paper, we present an ultrasound (US) visual servoing to control a robotic system equipped with a US probe. To avoid the difficult and time-consuming image segmentation process, we develop a new approach taking as visual input directly the intensity of the image pixels. The analytic form of the interaction matrix that relates the variation of the intensity features to the motion of the probe is established and used to control the six degrees of freedom (dof) of the robotic system. Our approach is applied with a 2-D and a 3-D US probe, and the results that are obtained with both sensors are compared in simulation. The 2-D probe shows good performances for tracking tasks and the 3-D one, which ensures a larger domain of convergence, is more particularly used for positioning tasks. The intensity-based approach is validated through experimental results performed with a realistic abdominal phantom and with animal soft tissue.

This paper presents a new image-based approach to the control of a robotic system equipped with an ultrasound imaging device. For diagnostic applications, the proposed method makes it possible to position an ultrasound probe on a desired organ section and to track it by compensating for rigid motions of the organ. Both in-plane and out-of-plane motions of the probe are controlled by the proposed method. The main contribution of this work is the direct use of the ultrasound image as visual feature which spares any segmentation or image processing time consuming step. Simulation and robotic experiments are performed on a realistic abdominal phantom and validate this ultrasound intensity-based visual servoing approach.

Application of the compliance to increase the robustness of the bipedal walker locomotion is the main target of this paper. The control of bipedal robots with point feet is one of the most challenging problems in the domain of hybrid dynamical systems. In order to make an exponentially stable periodic walking for an Acrobot as a planar biped with only one degree of freedom, a controller is designed based on the hybrid zero dynamics analysis. Then, through appropriate alterations in robot structure, the controller complexities are reduced which result in fewer parameters for tuning. Improvement of the robot structure via compliance insertion not only compensates the lower degree of freedom of the control design process, but also expands the domain of stability of the closed-loop system. In this regard, a nonlinear damper is inserted between two legs of Acrobot to enable it to walk on a wider range of slopes. The main controller is designed for walking on a flat terrain and the compliance of the damper empowers it to make stable walking on slopes up to 17°. The simulation results confirmed the efficiency of the proposed approach.

This paper presents a new image-based approach to control a robotic system equipped with an ultrasound imaging device. Moments based image features are extracted from three orthogonal ultrasound images to servo in-plane and out-of-plane motions of the system. Experimental results demonstrate that this approach improves upon techniques based on a single 2D US image in term of probe positioning. The second contribution of this paper is to use this method to perform a multimodal registration task by formulating it as a virtual visual servoing problem. Multimodal registration experiments performed with an ultrasound phantom containing an egg-shaped object provide a first experimental validation of the proposed method.