Aloysius K. Mok

Xi Zheng, Aloysius K. Mok, Ruzica Piskac et al.

The integration of machine learning (ML) into cyber-physical systems (CPS) offers significant benefits, including enhanced efficiency, predictive capabilities, real-time responsiveness, and the enabling of autonomous operations. This convergence has accelerated the development and deployment of a range of real-world applications, such as autonomous vehicles, delivery drones, service robots, and telemedicine procedures. However, the software development life cycle (SDLC) for AI-infused CPS diverges significantly from traditional approaches, featuring data and learning as two critical components. Existing verification and validation techniques are often inadequate for these new paradigms. In this study, we pinpoint the main challenges in ensuring formal safety for learningenabled CPS.We begin by examining testing as the most pragmatic method for verification and validation, summarizing the current state-of-the-art methodologies. Recognizing the limitations in current testing approaches to provide formal safety guarantees, we propose a roadmap to transition from foundational probabilistic testing to a more rigorous approach capable of delivering formal assurance.

C. -L. Fok, G. Johnson, J. D. Yamokoski et al.

Whole Body Operational Space Control (WBOSC) is a pioneering algorithm in the field of human-centered Whole-Body Control (WBC). It enables floating-base highly-redundant robots to achieve unified motion/force control of one or more operational space objectives while adhering to physical constraints. Limited studies exist on the software architecture and APIs that enable WBOSC to perform and be integrated into a larger system. In this paper we address this by presenting ControlIt!, a new open-source software framework for WBOSC. Unlike previous implementations, ControlIt! is multi-threaded to increase servo frequencies on standard PC hardware. A new parameter binding mechanism enables tight integration between ControlIt! and external processes via an extensible set of transport protocols. To support a new robot, only two plugins and a URDF model needs to be provided --- the rest of ControlIt! remains unchanged. New WBC primitives can be added by writing a Task or Constraint plugin. ControlIt!'s capabilities are demonstrated on Dreamer, a 16-DOF torque controlled humanoid upper body robot containing both series elastic and co-actuated joints, and using it to perform a product disassembly task. Using this testbed, we show that ControlIt! can achieve average servo latencies of about 0.5ms when configured with two Cartesian position tasks, two orientation tasks, and a lower priority posture task. This is significantly higher than the 5ms that was achieved using UTA-WBC, the prototype implementation of WBOSC that is both application and platform-specific. Variations in the product's position is handled by updating the goal of the Cartesian position task. ControlIt!'s source code is released under an LGPL license and we hope it will be adopted and maintained by the WBC community for the long term as a platform for WBC development and integration.

Yi-Hsuan Hsieh, Pei-Chi Huang, Aloysius K Mok

Robot programming typically makes use of a set of mechanical skills that is acquired by machine learning. Because there is in general no guarantee that machine learning produces robot programs that are free of surprising behavior, the safe execution of a robot program must utilize monitoring modules that take sensor data as inputs in real time to ensure the correctness of the skill execution. Owing to the fact that sensors and monitoring algorithms are usually subject to physical restrictions and that effective robot programming is sensitive to the selection of skill parameters, these considerations may lead to different sensor input qualities such as the view coverage of a vision system that determines whether a skill can be successfully deployed in performing a task. Choosing improper skill parameters may cause the monitoring modules to delay or miss the detection of important events such as a mechanical failure. These failures may reduce the throughput in robotic manufacturing and could even cause a destructive system crash. To address above issues, we propose a sensing quality-aware robot programming system that automatically computes the sensing qualities as a function of the robot's environment and uses the information to guide non-expert users to select proper skill parameters in the programming phase. We demonstrate our system framework on a 6DOF robot arm for an object pick-up task.

Chien Liang Fok, Fei Sun, Matt Mangum et al.

The Cloud-based Advanced Robotics Laboratory (CARL) integrates a whole body controller and web-based teleoperation to enable any device with a web browser to access and control a humanoid robot. By integrating humanoid robots with the cloud, they are accessible from any Internet-connected device. Increased accessibility is important because few people have access to state-of-the-art humanoid robots limiting their rate of development. CARL's implementation is based on modern software libraries, frameworks, and middleware including Node.js, Socket.IO, ZMQ, ROS, Robot Web Tools, and ControlIt! Feasibility is demonstrated by having inexperienced human operators use a smartphone's web-browser to control Dreamer, a torque-controlled humanoid robot based on series elastic actuators, and make it perform a dual-arm manipulation task. The implementation serves as a proof-of-concept and foundation upon which many advanced humanoid robot technologies can be researched and developed.