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Robots in realistic environments require the capability of adapting themselves to different conditions and task requirements regarding motion tracking performances, interaction forces, and payload conditions. Therefore, an online impedance modulation during the task execution represents a critical prerequisite for the robots operating in real-world environments, remaining an open research topic today. This work presents a novel task-based online impedance modulation with stability online verified. The method allows modulating the robot impedance following the task requirements. The method considers the motion tracking performance requested by the task and the expected task payload or interaction forces to derive the robot impedance. Appropriate impedance levels are also maintained to ensure the stability of the modulated robot impedance. The method is successfully verified on the CENTAURO robot.

Wheeled-legged robots have the ability to navigate in cluttered and irregular environments adapting the locomotion mode to the terrain perceived. To achieve this functionality, a locomotion planner is needed. In this work we present a hybrid search-based planner, which considers a set of modifiable motion primitives and a 2.5D traversability map acquired from the environment to generate navigation plans for the hybrid mobility robot CENTAURO. Our approach was validated in simulation and on the real wheeled-legged robot CENTAURO, demonstrating traversing capabilities in cluttered environments with various obstacles.

Structural Intervention Cardiology (SIC) is a miniinvasive intervention with a catheter based approach for cardiac surgery. Although SIC procedures are becoming increasingly popular, procedures are not ergonomic and technically demanding and, at the same time, high precision and accuracy in reaching target locations inside the human body are necessary for the success these procedures. Thus, there is therefore a need to develop a robust path planner framework to improve the accuracy in target reaching while minimizing interaction with anatomical structures. In this work a pre-operative path-planning method able to guide the catheter from the peripheral access to the desired target position with the needed orientation is proposed. The method exploits an Inverse Reinforcement Learning algorithm based on a combination of Behavioral Cloning (BC) and Generative Adversarial Imitation Learning (GAIL). The method was in-silico tested performing 50 intra-vascular and 70 intra-cardiac paths where the ratio between attempts in which the catheter reaches the target and total number of attempts, computation time, the difference between desired pose and the reached one were considered as validation metrics. Results show that the proposed method computes optimal path enabling the catheter to reach the target with an average error in position below 2 mm in the intra-vascular phase and below 1 mm in position and 6° in orientation in the intra-cardiac phase

Wheeled-legged robots have the potential to navigate in cluttered and irregular scenarios by altering the locomotion modes to adapt to the terrain challenges and effectively reach targeted locations in unstructured spaces. To achieve this functionality, a hybrid locomotion planner is necessary. In this work we present a search-based planner, which explores a set of motion primitives and a 2.5D traversability map extracted from the environment to generate navigation plans for the hybrid mobility robot CENTAURO. The planner explores the map from the current robot position to the goal location requested by the user, considering the most appropriate composition and tuning of locomotion primitives to build up afeasible plan, which is then executed by the robot. The available primitives are prioritized and can be easily modified, addedor removed through a configuration file. Our approach wasevaluated both in simulation and on the real wheeled-leggedrobot CENTAURO, demonstrating traversing capabilities incluttered environments with various obstacles.