Direct teaching in collaborative manipulators, an essential method for intuitive trajectory control, faces significant challenges due to friction in robot joints. To address this, we present a novel friction compensation framework to improve direct teaching methods for robots. Our approach focuses on mitigating friction in the joints most susceptible to frictional effects, ensuring smoother and more precise motion. The proposed framework uses deep neural networks (DNN) to model the complex friction behavior. This approach circumvents the difficulties associated with traditional friction compensation model selection. We develop specific data input preprocessing algorithms that optimize friction estimation when paired with standard encoders commonly used in collaborative robots. In addition, our custom loss function is specifically designed to improve DNN training in these low-velocity regions. To evaluate the effectiveness of our framework, we conduct comprehensive ablation studies assessing the impact of two critical components: the preprocessing algorithms and the custom loss function. These studies provide insight into the contributions of each element to overall performance. Experimental validation using two 6-DoF collaborative robots demonstrates the practical applicability and effectiveness of our approach.

Impedance control is a widely adopted approach that ensures the compliant behavior of robot manipulators as they interact with their environment according to specifically designed dynamics. For tasks involving six degrees of freedom (DoF), it is crucial to appropriately manage the position and orientation of the end-effector by controlling dynamic behavior. However, describing orientational displacement and designing the corresponding rotational impedance can be challenging, especially when we use a minimal representation. The well-known minimal representation for orientation, the Euler angle, suffers from representation singularity. As a remedy, the quaternion or dual quaternion can be an alternative, but with non-minimal representations. This lack of minimal representation, which does not suffer from the representation singularity, often leads to handling the impedance design by directly defining the potential energy function in the matrix Lie group. This paper proposes a framework for the six-DoF impedance control design that takes advantage of Lie group theory with minimal representation, known as the exponential coordinate. Since the exponential coordinate can be treated as the Euclidean variable within the injectivity radius, it allows for the formulation of the impedance control more systematically and familiarly. In our framework, a detour strategy is utilized; the impedance is designed in the Lie group$SE(3)$, and the control is designed in the Lie algebra$\mathfrak {se}(3)$, which is isomorphic to the vector space$\mathbb {R}^{6}$. The group structure of$SE(3)$can be maintained using the proposed conversion formula between the Lie group and the Lie algebra, called the differential of the exponential map and its time derivative, with a closed-form expression. Experiments with a 6-DoF robot manipulator verified that the proposed impedance control framework effectively reflects the$SE(3)$group structure and achieves the desired dynamic behavior as the functionality of the impedance control with minimal parameters.

This paper proposes an optimization-based control framework for the torque-controlled robot, which can satisfy the joint position, velocity, and acceleration constraints under a bounded jerk. The optimization filter is incorporated as a module to modify the nominal controller output to ensure joint constraints. To formulate the optimization problem as a QP, the torque optimization problem is converted to the jerk optimization problem using the augmented state, and the constraints are reformulated to be affine in the jerk. Here, the viable constraints are derived using the time-optimal braking policy to guarantee the feasibility of the QP. The proposed method was validated in simulation and with a 6-DOF robot manipulator.

Safety of taking medicine prescribed differently to each patient in hospital relies on the discernment of medical professionals who deals with measuring pill quantity, packaging, and distributing. It is difficult and time consuming to keep track of medication record of each patient. Also, medication safety is prone to be in risk due to the human error. To help patients get accurate medication following their prescription plan with minimizing human labors and mistakes, we developed PillGood, an automated smart pill dispenser system using facial recognition technique. PillGood provides real-time and personalized guidance to take the correct medicine by alarming patients and distributing exact quantity of pills at specific time following each patient's prescription table. The system notify patients through mobile app and speaker when they need to take the medicine, and detect who the patient is through the machine learning based face recognition. Then, based on each patient's prescribing information, the controller distributes pills to each patient. Results show that PillGood enable highly accurate personalized pill dispensation followed by precise face recognition, benefiting both patients and medical professionals. Videos for demonstrating the system can be found on https://youtu.be/Wx7bXxRGjXA