Model-based controllers using a linearized model around the system’s equilibrium point is a common approach in the control of a wheeled humanoid due to their less computational load and ease of stability analysis. However, controlling a wheeled humanoid robot while it lifts an unknown object presents significant challenges, primarily due to the lack of knowledge in object dynamics. This paper presents a framework designed for predicting the new equilibrium point explicitly to control a wheeled-legged robot with unknown dynamics. We estimated the total mass and center of mass of the system from its response to initially unknown dynamics, then calculated the new equilibrium point accordingly. To avoid using additional sensors (e.g., force torque sensor) and reduce the effort of obtaining expensive real data, a data-driven approach is utilized with a novel real-to-sim adaptation. A more accurate nonlinear dynamics model, offering a closer representation of real-world physics, is injected into a rigid-body simulation for real-to-sim adaptation. The nonlinear dynamics model parameters were optimized using Particle Swarm Optimization. The efficacy of this framework was validated on a physical wheeled inverted pendulum, a simplified model of a wheeled-legged robot. The experimental results indicate that employing a more precise analytical model with optimized parameters significantly reduces the gap between simulation and reality, thus improving the efficiency of a model-based controller in controlling a wheeled robot with unknown dynamics.

Control of wheeled humanoid locomotion is a challenging problem due to the nonlinear dynamics and under-actuated characteristics of these robots. Traditionally, feedback controllers have been utilized for stabilization and locomotion. However, these methods are often limited by the fidelity of the underlying model used, choice of controller, and environmental variables considered (surface type, ground inclination, etc). Recent advances in reinforcement learning (RL) offer promising methods to tackle some of these conventional feedback controller issues, but require large amounts of interaction data to learn. Here, we propose a hybrid learning and model-based controller Hybrid LMC that combines the strengths of a classical linear quadratic regulator (LQR) and ensemble deep reinforcement learning. Ensemble deep reinforcement learning is composed of multiple Soft Actor-Critic (SAC) and is utilized in reducing the variance of RL networks. By using a feedback controller in tandem the network exhibits stable performance in the early stages of training. As a preliminary step, we explore the viability of Hybrid LMC in controlling wheeled locomotion of a humanoid robot over a set of different physical parameters in MuJoCo simulator. Our results show that Hybrid LMC achieves better performance compared to other existing techniques and has increased sample efficiency.

Robotic systems capable of Dynamic Mobile Manipulation (DMM) tasks combine dynamic manipulation and locomotion and could facilitate dangerous or physically demanding labor. For instance, firefighter humanoid robots could leverage their body by leaning against collapsed building rubble to push it aside. Here we introduce a teleoperation system that targets the realization of these tasks using human's whole-body motor skills. We describe a new wheeled humanoid platform, SATYRR, and a novel hands-free teleoperation architecture using a whole-body Human Machine Interface (HMI). This system enables telelocomotion of the humanoid robot using the operator's body motion, freeing their arms for manipulation tasks. In this study we evaluate the efficacy of the proposed system on hardware, and explore the control of SATYRR using two teleoperation mappings that map the operators body pitch and yaw to the robot's velocity or acceleration. Through experiments and user feedback we showcase our preliminary findings of the pilot-system response. Results suggest that the HMI is capable of effectively telelocomoting SATYRR, that pilot preferences should dictate the appropriate motion mapping and gains, and finally that the pilot can better learn to control the system over time. This study represents a fundamental step towards the realization of combined manipulation and locomotion via teleoperation.

The use of the tendon-sheath mechanism (TSM) is common in flexible surgery robots, because it can flexibly work in limited spaces and provides efficient power transmission. However, hysteresis from nonlinearities such as friction and backlash poses a challenge in controlling precise motion in the surgical instrument. Moreover, this hysteresis is also affected by changes in the various configurations of sheath which limits traditional model-based compensation approaches. Recently, feedback approach using an endoscopic camera is presented, but they use markers which are not appropriate for applying to a real surgical instruments. In this paper, a novel hysteresis compensator with learning-based pose estimation is proposed. Unlike previous studies, the proposed compensator can reduce hysteresis of the surgical instrument in various sheath configurations without using markers. In order to estimate an actual angle of the surgical instrument's joint, we employ the learning-based pose estimation using a siamese convolutional neural network (SCNN). The proposed compensator reduces hysteresis by partially controlling the position command, similar to the instinctive adjustments that physicians make with their visual feedback. To validate the proposed method, a testbed was constructed considering several requirements of flexible surgery robots. As a result, the results show the proposed method reduces hysteresis to less than 10°, for various configurations of sheath. In addition, we confirmed that the learning-based pose estimation is sufficient to apply to the proposed compensator for reducing hysteresis in real-time.

Endoscopes are widely used for not only intraluminal diagnosis but also therapeutic procedures in the gastrointestinal area. However, conventional endoscopes present a few challenges such as nonintuitive manipulation, physical burden on the operator, and lack of dexterity. These challenges limit endoscope usage in complex surgical procedures. Moreover, endoscope operators undergo extensive and lengthy training to attain an adequate skill level. In this paper, we introduce a flexible surgery robot platform K-FLEX that facilitates teleoperation via an intuitive master interface and bimanual manipulation by means of two dexterous surgical robot arms. Its effects on endoscopic procedures, especially in terms of task performance, learning properties, and physical burden on the operator, are validated by conducting a user test. The experimental results demonstrate that the developed robotic assistant increases operation speed, especially for novices; simplifies the learning process; and reduces the workload on the operator compared to conventional endoscopes.