Direct teaching is a straightforward way of teaching new motion to robots. Active methods with torque sensors, for example, can be used so that the robot can follow the movements of the human, but such methods introduce delays. Alternatively, series clutch actuators are easily backdrivable without delay. However, vertical joints are subject to gravity torques, which need to be compensated when disengaging the clutch. We implemented passive gravity compensation to counteract the robot’s weight, but this mechanism cannot compensate for varying payloads, as adjustable passive gravity compensation is relatively slow and mechanically complex. The varying payload causes an unintended joint movement, i.e. the arm falls down on its own, which is unacceptable during direct teaching. Therefore, this paper demonstrates how the torque output controlled with series clutch actuators can be used to compensate for varying payloads while maintaining high backdrivability. The proposed method is evaluated on a collaborative robot with a clutch in series for each actuator. Real-world experiments with payloads from 0 to 3 kg are conducted. During the experiments, the operator force is measured to evaluate the proposed method.

Object picking with two-fingered grippers is widely used in practice. However, the deformability and slipperiness of the target object still remain a challenge, and not resolving them might lead to breaking or dropping of the grasped objects. To prevent such instances, tactile sensing plays an important role because it can directly detect even the subtle changes that occur during grasping. Mechanoreceptors in the human skin detect such events by the change in the skin shape and/or vibration. Using a similar approach, a combined deformation and slip detection system using a distributed 3axis tactile information with various time-scales is proposed. Specifically, the tactile information includes the z-axis data, which denotes the deformation of the skin perpendicular to the finger’s surface and the x- and y-axes, which measure deformations tangential to the surface. The perpendicular and tangential tactile information are used to determine the deformation and slip, respectively. The system is based on a multilayer perceptron (MLP) that outputs detection results from a 3-axis tactile information. Results showed that, the perpendicular and tangential tactile information with an appropriate timescale were effective for deformation and slip detection with over 89% and 95% recognition rates, respectively, measured for 40 different objects. Moreover, 195 out of 200 real-time untrained grasping states were successful detected. Finally, 10 untrained objects were successfully picked.