Teaching and learning of performance measurement in OR/MS degrees

A review of existing UK MS/OR undergraduate programmes was completed to assess the extent and nature of performance measurement teaching. In addition, a survey of performance measurement practitioners was undertaken to obtain views on what should be taught in relation to performance measurement. A survey of 23 undergraduate MS/OR degrees in the UK revealed that all the academic respondents supported the inclusion of PM teaching. However, only four distinct PM classes could be found amongst these degrees. The PM techniques taught were broadly similar although the wider context of PM was taught in only 2 of the classes. A survey of a small number of PM practitioners revealed that the Balanced Scorecard and Benchmarking were the two most commonly applied PM techniques with the majority of respondents learning about PM from personal experience and reading rather than through formal education. It appears that there is an opportunity for MS/OR teaching to make a major contribution to the development of PM as a discipline. However, academic respondents whose MS/OR degree course did not teach PM indicated that lack of staff expertise in PM combined with an already full syllabus were the main barriers to introducing a PM class

Dot-to-Dot: Explainable Hierarchical Reinforcement Learning for Robotic Manipulation

Robotic systems are ever more capable of automation and fulfilment of complex tasks, particularly with reliance on recent advances in intelligent systems, deep learning and artificial intelligence. However, as robots and humans come closer in their interactions, the matter of interpretability, or explainability of robot decision-making processes for the human grows in importance. A successful interaction and collaboration will only take place through mutual understanding of underlying representations of the environment and the task at hand. This is currently a challenge in deep learning systems. We present a hierarchical deep reinforcement learning system, consisting of a low-level agent handling the large actions/states space of a robotic system efficiently, by following the directives of a high-level agent which is learning the high-level dynamics of the environment and task. This high-level agent forms a representation of the world and task at hand that is interpretable for a human operator. The method, which we call Dot-to-Dot, is tested on a MuJoCo-based model of the Fetch Robotics Manipulator, as well as a Shadow Hand, to test its performance. Results show efficient learning of complex actions/states spaces by the low-level agent, and an interpretable representation of the task and decision-making process learned by the high-level agent

Gaze-based, context-aware robotic system for assisted reaching and grasping

Assistive robotic systems endeavour to support those with movement disabilities, enabling them to move again and regain functionality. Main issue with these systems is the complexity of their low-level control, and how to translate this to simpler, higher level commands that are easy and intuitive for a human user to interact with. We have created a multi-modal system, consisting of different sensing, decision making and actuating modalities, leading to intuitive, human-in-the-loop assistive robotics. The system takes its cue from the user's gaze, to decode their intentions and implement low-level motion actions to achieve high-level tasks. This results in the user simply having to look at the objects of interest, for the robotic system to assist them in reaching for those objects, grasping them, and using them to interact with other objects. We present our method for 3D gaze estimation, and grammars-based implementation of sequences of action with the robotic system. The 3D gaze estimation is evaluated with 8 subjects, showing an overall accuracy of $4.68\pm0.14cm$. The full system is tested with 5 subjects, showing successful implementation of $100\%$ of reach to gaze point actions and full implementation of pick and place tasks in 96\%, and pick and pour tasks in $76\%$ of cases. Finally we present a discussion on our results and what future work is needed to improve the system

FastOrient: Lightweight Computer Vision for Wrist Control in Assistive Robotic Grasping

Wearable and Assistive robotics for human grasp support are broadly either tele-operated robotic arms or act through orthotic control of a paralyzed user's hand. Such devices require correct orientation for successful and efficient grasping. In many human-robot assistive settings, the end-user is required to explicitly control the many degrees of freedom making effective or efficient control problematic. Here we are demonstrating the off-loading of low-level control of assistive robotics and active orthotics, through automatic end-effector orientation control for grasping. This paper describes a compact algorithm implementing fast computer vision techniques to obtain the orientation of the target object to be grasped, by segmenting the images acquired with a camera positioned on top of the end-effector of the robotic device. The rotation needed that optimises grasping is directly computed from the object's orientation. The algorithm has been evaluated in 6 different scene backgrounds and end-effector approaches to 26 different objects. 94.8% of the objects were detected in all backgrounds. Grasping of the object was achieved in 91.1% of the cases and has been evaluated with a robot simulator confirming the performance of the algorithm

Stepping Into the Participants’ Shoes:The Trans-Positional Cognition Approach

To conduct a phenomenological study, researchers often follow the principles from either the descriptive or interpretive phenomenological schools of thought. This constrains researchers within the domain and limits the potentials of their data set. This paper introduces the Trans-Positional Cognition Approach (TPCA) as a novel synthesised phenomenological research method for conducting qualitative research to address this challenge. The TPCA synthesises the principles of the descriptive and interpretive phenomenological schools and helps to bridge the divide occasioned by polemical arguments between them. At the heart of TPCA is the process of trans-positional cognition or, in simple words, "stepping into the participants' shoes". TPCA, within the phenomenological tradition, proposes a structured methodological approach as a way to reduce the complexity of the extant methods, which novice researchers associate with phenomenology. The purpose of TPCA is not to pit one phenomenological research approach against another but to elucidate an inclusive approach to phenomenological research that can serve as a methodological alternative. A set of dimensions is used to compare TPCA with extant descriptive and interpretive phenomenological approaches in order to demonstrate its distinctiveness. Furthermore, an implementation study illustrates the use of the TPCA. Hence, the TPCA, by bridging the divide between the phenomenological schools of thought, could potentially help sustain the growing interest of researchers in phenomenological research

Neuromuscular Reinforcement Learning to Actuate Human Limbs through FES

Functional Electrical Stimulation (FES) is a technique to evoke muscle contraction through low-energy electrical signals. FES can animate paralysed limbs. Yet, an open challenge remains on how to apply FES to achieve desired movements. This challenge is accentuated by the complexities of human bodies and the non-stationarities of the muscles' responses. The former causes difficulties in performing inverse dynamics, and the latter causes control performance to degrade over extended periods of use. Here, we engage the challenge via a data-driven approach. Specifically, we learn to control FES through Reinforcement Learning (RL) which can automatically customise the stimulation for the patients. However, RL typically has Markovian assumptions while FES control systems are non-Markovian because of the non-stationarities. To deal with this problem, we use a recurrent neural network to create Markovian state representations. We cast FES controls into RL problems and train RL agents to control FES in different settings in both simulations and the real world. The results show that our RL controllers can maintain control performances over long periods and have better stimulation characteristics than PID controllers.Comment: Accepted manuscript IFESS 2022 (RehabWeek 2022