-- Although robots with flexible bodies are superior in terms of the contact and adaptability, it is difficult to control them precisely. On the other hand, human beings make use of the surrounding environments to stabilize their bodies and control their movements. In this study, we propose a method for the bracing motion and extension of the range of motion using the environment for the musculoskeletal humanoid. Here, it is necessary to recognize the stability of the body when contacting the environment, and we develop a method to measure it by using the change in sensor values of the body when actively vibrating a part of the body. Experiments are conducted using the musculoskeletal humanoid Musashi, and the effectiveness of this method is confirmed. I. INTRODUCTION The flexible body is excellent from the point of view of the soft contact, impact mitigation, adaptability, etc. [1], [2], and a shift from rigid robots [3], [4] to soft robots [5], [6] is underway. In [5], a robot that jumps and runs using pneumatic artificial muscles is developed. In [6], a robot that mitigates impact and softly interacts with the environment using variable stiffness control with nonlinear elastic elements has been developed.

I. INTRODUCTION State estimation for continuum robotics is a comparatively underdeveloped area. Several dynamic estimators have been proposed. In [3], [4] the authors make use of Kalman filtering techniques. A typical assumption these dynamic estimators make is that of a constant curvature shape, further limiting the accuracy of the method in pursuit of model simplicity. In mobile robotics, [5] introduces the use of Gaussian-process (GP) regression for Figure 1: A continuum robot is shown at multiple timesteps performing continuous-time state estimation of a mobile with increasing opacity.

Recently, fiber optic sensors such as fiber Bragg gratings (FBGs) have been widely investigated for shape reconstruction and force estimation of flexible surgical robots. However, most existing approaches need precise model parameters of FBGs inside the fiber and their alignments with the flexible robots for accurate sensing results. Another challenge lies in online acquiring external forces at arbitrary locations along the flexible robots, which is highly required when with large deflections in robotic surgery. In this paper, we propose a novel data-driven paradigm for simultaneous estimation of shape and force along highly deformable flexible robots by using sparse strain measurement from a single-core FBG fiber. A thin-walled soft sensing tube helically embedded with FBG sensors is designed for a robotic-assisted flexible ureteroscope with large deflection up to 270 degrees and a bend radius under 10 mm. We introduce and study three learning models by incorporating spatial strain encoders, and compare their performances in both free space and constrained environments with contact forces at different locations. The experimental results in terms of dynamic shape-force sensing accuracy demonstrate the effectiveness and superiority of the proposed methods.

Abstract-- Traditional rigid endoscopes have challenges in flexibly treating tumors located deep in the brain, and low operability and fixed viewing angles limit its development. This study introduces a novel dual-segment flexible robotic endoscope MicroNeuro, designed to perform biopsies with dexterous surgical manipulation deep in the brain. Taking into account the uncertainty of the control model, an image-based visual servoing with online robot Jacobian estimation has been implemented to enhance motion accuracy. Furthermore, the application of model predictive control with constraints significantly bolsters the flexible robot's ability to adaptively track mobile objects and resist external interference. The rigid structure limited maneuverability [2] within the Tumors located within the brain's ventricular system pose complex anatomy of the brain, slight movement abruptly or significant health risks and present considerable treatment incorrectly may lead to potential brain trauma and complications; challenges due to their difficult-to-reach locations and proximity and (ii) the limitation of fixed viewing angles of rigid to critical neurological structures [1].

Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher load-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional state space. NMPC offers an effective means to control such robots, but its extensive computational demands often limit its application in real-time scenarios. To enable fast control of flexible robots, we propose a framework for a safe approximation of NMPC using imitation learning and a predictive safety filter. Our framework significantly reduces computation time while incurring a slight loss in performance. Compared to NMPC, our framework shows more than a eightfold improvement in computation time when controlling a three-dimensional flexible robot arm in simulation, all while guaranteeing safety constraints. Notably, our approach outperforms conventional reinforcement learning methods. The development of fast and safe approximate NMPC holds the potential to accelerate the adoption of flexible robots in industry.

Flexible robots have advantages over rigid robots in their ability to conform physically to their environment and to form a wide variety of shapes. Sensing the force applied by or to flexible robots is useful for both navigation and manipulation tasks, but it is challenging due to the need for the sensors to withstand the robots' shape change without encumbering their functionality. Also, for robots with long or large bodies, the number of sensors required to cover the entire surface area of the robot body can be prohibitive due to high cost and complexity. We present a novel soft air pocket force sensor that is highly flexible, lightweight, relatively inexpensive, and easily scalable to various sizes. Our sensor produces a change in internal pressure that is linear with the applied force. We present results of experimental testing of how uncontrollable factors (contact location and contact area) and controllable factors (initial internal pressure, thickness, size, and number of interior seals) affect the sensitivity. We demonstrate our sensor applied to a vine robot-a soft inflatable robot that "grows" from the tip via eversion-and we show that the robot can successfully grow and steer towards an object with which it senses contact.

Present-day robots are made for the purpose of repeating several tasks thousands of times. To make robots more widely applicable, future robots need to be able to do thousands of tasks just a few times. Programming a robot to solve just one complex motor task has remained a challenging, costly and time-consuming task - artificial intelligence is rarely employed. In fact, manual programming has become the key bottleneck in robot use. Empowering robots with an artificially intelligence approach to autonomously learn such tasks is at the center of the new ERC Proof of Concept project "AssemblySkills", which aims to validate that an autonomous, intelligent skill learning system can enable robots to acquire and improve a rich set of motor skills for specific applications.

The use of robotics in controlled environments has flourished over the last several decades and training robots to perform tasks using control strategies developed from dynamical models of their hardware have proven very effective. However, in many real-world settings, the uncertainties of the environment, the safety requirements and generalized capabilities that are expected of robots make rigid industrial robots unsuitable. This created great research interest into developing control strategies for flexible robot hardware for which building dynamical models are challenging. In this paper, inspired by the success of deep reinforcement learning (DRL) in other areas, we systematically study the efficacy of policy search methods using DRL in training flexible robots. Our results indicate that DRL is successfully able to learn efficient and robust policies for complex tasks at various degrees of flexibility. We also note that DRL using Deep Deterministic Policy Gradients can be sensitive to the choice of sensors and adding more informative sensors does not necessarily make the task easier to learn.

Soldiers might end up using flexible robots inspired by invertebrates to go where humans can't. Ed Habtour works in the US Army Research Laboratory's Vehicle Technology Directorate, where he specializes in nonlinear dynamical systems. The US Army Research Laboratory and University of Minnesota have joined forces to develop extra pliable materials that can be 3D-printed on the battlefield and used to build robots that can move easily within confined spaces, the way biological organisms like a squid might maneuver through small holes in underwater rocks. Current military robots can't move freely in highly populated environments because they're made with rigid mechanical parts. However, that situation may change now that researchers have recently created a prototype of soft 3D-printed dielectric elastomer actuator -- an electroactive polymer that changes shape when hit with an electrical charge.

LaserSnake2 is a highly flexible robot that's ideal for working in confined and hazardous spaces like aircraft assembly, nuclear power stations or the inspection of sewage systems.