The growing demand for robots to operate effectively in diverse environments necessitates the need for robust real-time anomaly detection techniques during robotic operations. However, deep learning-based models in robotics face significant challenges due to limited training data and highly noisy signal features. In this paper, we present Sparse Masked Autoregressive Flow-based Adversarial AutoEncoder model to address these problems. This approach integrates Masked Autoregressive Flow model into Adversarial AutoEncoders to construct a flexible latent space and utilizes Sparse autoencoder to efficiently focus on important features, even in scenarios with limited feature space. Our experiments demonstrate that the proposed model achieves a 4.96% to 9.75% higher area under the receiver operating characteristic curve for pick-and-place robotic operations with randomly placed cans, compared to existing state-of-the-art methods. Notably, it showed up to 19.67% better performance in scenarios involving collisions with lightweight objects. Additionally, unlike the existing state-of-the-art model, our model performs inferences within 1 millisecond, ensuring real-time anomaly detection. These capabilities make our model highly applicable to machine learning-based robotic safety systems in dynamic environments. The code is available at https://github.com/twkang43/sparse-maf-aae .

The COVID-19 pandemic has caused a global public health crisis, leading to increased costs for operating essential quarantine facilities and risks of infection for medical staff. To address these issues, we have developed a non-contact delivery robot, UTD-pro, that can deliver food and supplies to patients in an isolated residential treatment center. In case of emergency situations such as communication failure with the control system or malfunction of specific modules, the noncontact delivery robot should be able to perform commands robustly or return home. Otherwise, as before, medical staffs may have to enter the high-risk environment again. In this paper, we propose a flexible and robust remote control system and a robot task management system that enables the robot to execute commands correctly. Our robot task manager allows for autonomous task execution with a single command from the remote control system. Each robot task operates independently, allowing users to change task plans flexibly during robot operation. The sensor data communication and task execution communication processes are also independent, preventing any issue in one process from affecting the other. By applying our system to the robot, we conducted a long-term delivery experiment for a total of 7.723km for 79 days, and achieved 125 successes out of 149 trials. This experiment proves that our system can lead to stable delivery processes and contribute to the high reliability of the control system and the robot task manager.

Reactivity to unforeseen disturbances is one of the most crucial characteristics for biped robots to walk robustly in the real world. Nevertheless, conventional walking methods generally have limited capability for generating rapid reactions to disturbances, because in these methods it is necessary to wait until the end of the preplanned time period to proceed to the next phase. In this study, to improve reactivity, we develop an event-based finite-state machine (E-FSM) for walking pattern generation. Reactivity is enhanced by determining the state transition conditions of the E-FSM only with time-independent events based on the present robot state. Moreover, in the E-FSM, the robot can walk robustly even when the center of mass and the swing foot motion are disturbed, by employing the capture point concept combined with a new swing foot position constraint. Finally, we propose to control the walking robot by incorporating the E-FSM with an inverse dynamics-based motion/force controller to achieve compliant behavior. This can provide safe responses to external disturbances. The developed method is verified by experiments on a 12-degrees-of-freedom torque-controlled biped robot while it locomotes under irregular external disturbances applied to the upper body or swing leg.

This paper proposes a novel and practical approach to enhance the computational efficiency of the hierarchical quadratic programming (HQP)-based whole-body control. The HQP method is known to offer control solutions satisfying strict priority with various constraints for multiple-tasks execution. However, it inherently comes at the price of high computation time to solve QP optimization problems in each hierarchical level which limits practicability in a real-time control system with fast sampling time. To mitigate this issue, we propose that the operational space formulation is incorporated into the HQP method, where the decision variables are intuitively defined at the task level and possess smaller dimensions. Indeed, it serves faster whole-body control solution for multiple tasks under equality and inequality constraints yet strictly fulfilling the task priority. The performance of the pro-posed method is experimentally verified on the actual floating-based humanoid, named TOCABI with 33 degrees-of-freedom. In addition, computation time is analyzed by comparison with conventional HQP and other advanced implementation forms.

This paper presents a dynamic whole-body control method for humanoids to render agile and stable standing-up motion based on energy concepts. First, to cope with the standing-up problem with multiple contacts in hierarchical tasks, an enhanced operational-space based whole-body control (WBC) framework is proposed, which offers optimal torque resolutions guaranteeing strict dynamic consistency with inequality constraints formulated by quadratic programming. Second, agile standing-up control strategy with dynamic push and rise actions is newly developed based on the notion of the total energy. The optimal pushing wrenches at contacts are computed to obtain sufficient energy to accelerate the center-of-mass (CoM) of the robot as quick as possible, and the total energy is then controlled to attain rapid rise-up motion and to stabilize the body of the robot. Consequently, the robot can effectively and actively stand up to recover from a certain pose in which cannot be accomplished by any quasi-static motion. The proposed method is numerically experimented and validated with dynamic parameters from the real humanoid COMAN+, fulfilling different types of standing-up actions.

Reactivity to unexpected situations is one of the most important characteristics of walking for real world applications. In this study, we introduce a reactive biped robot walking method that reflects only the current state of the robot. Therefore, time plan and trajectory tracking control are not required for robot walking, and this enables reactive behavior to unexpected contact or disturbance. The walking algorithm is realized through a whole-body control algorithm based on the operational space control framework, that possesses the capability to command the required force for tasks and also implement compliant task behavior by adjusting corresponding task gains. The performance of the proposed method is verified by experiments with a 12-DoF torque controlled biped robot. Robust walking is demonstrated when the foot is stopped by an unexpected obstacle or when the lateral motion is unexpectedly blocked and released by a human.