The inverse dynamics of a robotic manipulator is instrumental in precise robot control and manipulation. However, acquiring such a model is challenging, not only due to unmodelled non-linearities such as joint friction, but also from a machine learning perspective (e.g., input space dimension, amount of data needed). The accuracy of such models, regardless of the learning techniques, relies on proper excitation and exploration of the robot’s configuration space, in order to collect a rich dataset. This study aims to provide rich data in learning the inverse dynamics of a serial robotic manipulator using supervised machine learning techniques. We propose a method, called Max-Information Configuration Exploration (MICE), to incrementally explore and generate information-rich data via computing parameters of a trajectory set. We also introduce a new set of excitation trajectories that explores robot’s configuration through imposed stable limit cycles in robot joints’ phase space while satisfying feasibility constraints and physical bounds. We benchmark MICE against state-of-the-art in terms of data quality and learning accuracy. The proposed methodology for data collection, model learning, and evaluation, is validated with a KUKA IIWA14 robotic arm where the results prove significant improvement over traditional approaches.

Foot devices have been ubiquitously used in surgery to control surgical equipment. Most common applications are foot switches for electro-surgery, endoscope positioning and tele-robotic consoles. Switches fall short of providing continuous control as required for precise use of instruments. We developed a haptic foot interface to provide continuous assistance in surgical procedures. This paper concerns the foot control of simultaneous five degrees of freedom (DoF) of a surgical laparoscopic gripper. We assess systematically precision at controlling position and orientation at the target and closing of the forceps. Our controller provides position:position mapping between the foot and the robotic tool, as well as haptic feedback, compensating for gravity of the lower limb of the operator so as to alleviate fatigue. A dynamic model compensation and closed loop force feedback is used to achieve high transparency and backdrivability. The assistance is based on a novel type of haptic fixtures combining spring-damper with selective dynamic compensation in the direction aligned with the task of grasping, so as to simplify control of certain poses, made difficult due to the coupling between human lower limbs’ DoF’s. We experimentally evaluated the control strategy with six users on a position control surgical task in simulation. Results show the proposed assistance greatly eases the foot grasping task leading to higher completeness, efficiency, and lower mental and physical load.

In many tasks such as finishing operations, achieving accurate force tracking is essential. However, uncertainties in the robot dynamics and the environment limit the force tracking accuracy. Learning a compensation model for these uncertainties to reduce the force error is an effective approach to overcome this limitation. However, this approach requires an adaptive and robust framework for motion and force generation. In this paper, we use the time-invariant Dynamical System (DS) framework for force adaptation in contact tasks. We propose to improve force tracking accuracy through online adaptation of a state-dependent force correction model encoded with Radial Basis Functions (RBFs). We evaluate our method with a KUKA LWR IV+ robotic arm. We show its efficiency to reduce the force error to a negligible amount with different target forces and robot velocities. Furthermore, we study the effect of the hyper-parameters and provide a guideline for their selection. We showcase a collaborative cleaning task with a human by integrating our method to previous works to achieve force, motion, and task adaptation at the same time. Thereby, we highlight the benefits of using adaptive force control in real-world environments where we need reactive and adaptive behaviours in response to interactions with the environment.