Maximising Coefficiency of Human-Robot Handovers through Reinforcement Learning

Handing objects to humans is an essential capability for collaborative robots. Previous research works on human-robot handovers focus on facilitating the performance of the human partner and possibly minimising the physical effort needed to grasp the object. However, altruistic robot behaviours may result in protracted and awkward robot motions, contributing to unpleasant sensations by the human partner and affecting perceived safety and social acceptance. This paper investigates whether transferring the cognitive science principle that “humans act coefficiently as a group” (i.e. simultaneously maximising the benefits of all agents involved) to human-robot cooperative tasks promotes a more seamless and natural interaction. Human-robot coefficiency is first modelled by identifying implicit indicators of human comfort and discomfort as well as calculating the robot energy consumption in performing the desired trajectory. We then present a reinforcement learning approach that uses the human-robot coefficiency score as reward to adapt and learn online the combination of robot interaction parameters that maximises such coefficiency . Results proved that by acting coefficiently the robot could meet the individual preferences of most subjects involved in the experiments, improve the human perceived comfort, and foster trust in the robotic partner

Human-robot interaction using a behavioural control strategy

PhD ThesisA topical and important aspect of robotics research is in the area of human-robot interaction (HRI), which addresses the issue of cooperation between a human and a robot to allow tasks to be shared in a safe and reliable manner. This thesis focuses on the design and development of an appropriate set of behaviour strategies for human-robot interactive control by first understanding how an equivalent human-human interaction (HHI) can be used to establish a framework for a robotic behaviour-based approach. To achieve the above goal, two preliminary HHI experimental investigations were initiated in this study. The first of which was designed to evaluate the human dynamic response using a one degree-of-freedom (DOF) HHI rectilinear test where the handler passes a compliant object to the receiver along a constrained horizontal path. The human dynamic response while executing the HHI rectilinear task has been investigated using a Box-Behnken design of experiments [Box and Hunter, 1957] and was based on the McRuer crossover model [McRuer et al. 1995]. To mimic a real-world human-human object handover task where the handler is able to pass an object to the receiver in a 3D workspace, a second more substantive one DOF HHI baton handover task has been developed. The HHI object handover tests were designed to understand the dynamic behavioural characteristics of the human participants, in which the handler was required to dexterously pass an object to the receiver in a timely and natural manner. The profiles of interactive forces between the handler and receiver were measured as a function of time, and how they are modulated whilst performing the tasks, was evaluated. Three key parameters were used to identify the physical characteristics of the human participants, including: peak interactive force (fmax), transfer time (Ttrf), and work done (W). These variables were subsequently used to design and develop an appropriate set of force and velocity control strategies for a six DOF Stäubli robot manipulator arm (TX60) working in a human-robot interactive environment. The optimal design of the software and hardware controller implementation for the robot system has been successfully established in keeping with a behaviour-based approach. External force control based on proportional plus integral (PI) and fuzzy logic control (FLC) algorithms were adopted to control the robot end effector velocity and interactive force in real-time. ii The results of interactive experiments with human-to-robot and robot-to-human handover tasks allowed a comparison of the PI and FLC control strategies. It can be concluded that the quantitative measurement of the performance of robot velocity and force control can be considered acceptable for human-robot interaction. These can provide effective performance during the robot-human object handover tasks, where the robot was able to successfully pass the object from/to the human in a safe, reliable and timely manner. However, after careful analysis with regard to human-robot handover test results, the FLC scheme was shown to be superior to PI control by actively compensating for the dynamics in the non-linear system and demonstrated better overall performance and stability. The FLC also shows superior performance in terms of improved sensitivity to small error changes compared to PI control, which is an advantage in establishing effective robot force control. The results of survey responses from the participants were in agreement with the parallel test outcomes, demonstrating significant satisfaction with the overall performance of the human-robot interactive system, as measured by an average rating of 4.06 on a five point scale. In brief, this research has contributed the foundations for long-term research, particularly in the development of an interactive real-time robot-force control system, which enables the robot manipulator arm to cooperate with a human to facilitate the dextrous transfer of objects in a safe and speedy manner.Thai government and Prince of Songkla University (PSU

Reasoning and understanding grasp affordances for robot manipulation

This doctoral research focuses on developing new methods that enable an artificial agent to grasp and manipulate objects autonomously. More specifically, we are using the concept of affordances to learn and generalise robot grasping and manipulation techniques. [75] defined affordances as the ability of an agent to perform a certain action with an object in a given environment. In robotics, affordances defines the possibility of an agent to perform actions with an object. Therefore, by understanding the relation between actions, objects and the effect of these actions, the agent understands the task at hand, providing the robot with the potential to bridge perception to action. The significance of affordances in robotics has been studied from varied perspectives, such as psychology and cognitive sciences. Many efforts have been made to pragmatically employ the concept of affordances as it provides the potential for an artificial agent to perform tasks autonomously. We start by reviewing and finding common ground amongst different strategies that use affordances for robotic tasks. We build on the identified grounds to provide guidance on including the concept of affordances as a medium to boost autonomy for an artificial agent. To this end, we outline common design choices to build an affordance relation; and their implications on the generalisation capabilities of the agent when facing previously unseen scenarios. Based on our exhaustive review, we conclude that prior research on object affordance detection is effective, however, among others, it has the following technical gaps: (i) the methods are limited to a single object ↔ affordance hypothesis, and (ii) they cannot guarantee task completion or any level of performance for the manipulation task alone nor (iii) in collaboration with other agents. In this research thesis, we propose solutions to these technical challenges. In an incremental fashion, we start by addressing the limited generalisation capabilities of, at the time state-of-the-art methods, by strengthening the perception to action connection through the construction of an Knowledge Base (KB). We then leverage the information encapsulated in the KB to design and implement a reasoning and understanding method based on statistical relational leaner (SRL) that allows us to cope with uncertainty in testing environments, and thus, improve generalisation capabilities in affordance-aware manipulation tasks. The KB in conjunctions with our SRL are the base for our designed solutions that guarantee task completion when the robot is performing a task alone as well as when in collaboration with other agents. We finally expose and discuss a range of interesting avenues that have the potential to thrive the capabilities of a robotic agent through the use of the concept of affordances for manipulation tasks. A summary of the contributions of this thesis can be found at: https://bit.ly/grasp_affordance_reasonin

The impact of artificial intelligence on jobs and work in New Zealand

Artificial Intelligence (AI) is a diverse technology. It is already having significant effects on many jobs and sectors of the economy and over the next ten to twenty years it will drive profound changes in the way New Zealanders live and work. Within the workplace AI will have three dominant effects. This report (funded by the New Zealand Law Foundation) addresses: Chapter 1 Defining the Technology of Interest; Chapter 2 The changing nature and value of work; Chapter 3 AI and the employment relationship; Chapter 4 Consumers, professions and society. The report includes recommendations to the New Zealand Government