Underactuated Gripper with Forearm Roll Estimation for Human Limbs Manipulation in Rescue Robotics The emergence of new robotic technologies such as compliant control and soft robotics, has contributed to safe physical HumanRobot Interaction (pHRI) mainly for assistive applications. However, a robot capable of directly manipulating the human body, which is key for the implementation of autonomous rescue robots, has not been developed so far. In this paper, the development of a gripper and methods for the robotic manipulation of a laying victimxe2x80x99s forearm, initiated by the robot is addressed, and validated based on experimental results. An underactuated gripper with added proprioceptive sensors has been designed, with environment sensing and tactile recognition capabilities. This method provides a stable grasping of a human forearm that lays on a surface and is capable of estimating the roll angle of the grasped arm for precise location and safe manipulation. The rollangle estimation method is based on Machine Learning and has been trained with experimental data obtained from experiments with human volunteers. The resulting method provides robust and precise grasping, tolerant to location inaccuracy with inexpensive sensors. This is one of the very first works on the robotic humanbody manipulation.

Predicting Grasp Success with a Soft Sensing Skin and ShapeMemory Actuated Gripper Tactile sensors have been increasingly used to support rigid robot grippers in object grasping and manipulation. However, rigid grippers are often limited in their ability to handle compliant, delicate, or irregularly shaped objects. In recent years, grippers made from soft and flexible materials have become increasingly popular for certain manipulation tasks, e.g., grasping, due to their ability to conform to the object shape without the need for precise control. Although promising, such soft robot grippers currently suffer from the lack of available sensing modalities. In this work, we introduce a soft and stretchable sensing skin and incorporate it into the two fingers of a shapememory actuated soft gripper. The onboard sensing skin includes a 9axis inertial measurement unit (IMU) and five discrete pressure sensors per finger. We use this sensorized soft gripper to study grasp success and stability of over 2585 grasps with various objects using several machine learning methods. Our experiments show that LSTMs were the most accurate predictors of grasp success and stability, compared to SVMs, FFNNs, and STHMP. We also evaluated the effects on performance of each sensoru0027s data, and the success rates for individual objects. The results show that the accelerometer data of the IMUs has the largest contribution to the overall grasp prediction, which we attribute to its ability to detect precise movements of the gripper during grasping.