References were removed from the final submission that were part of the accepted paper. There were also two duplicative references.

Successful entrainment during collaboration positively affects trust, willingness to collaborate, and likeability towards collaborators. In this paper, we present a mixed-method study to investigate characteristics of successful entrainment leading to pair and group-based synchronisation. Drawing inspiration from industrial settings, we designed a fast-paced, short-cycle repetitive task. Using motion tracking, we investigated entrainment in both dyadic and triadic task completion. Furthermore, we utilise audio-video recordings and semi-structured interviews to contextualise participants’ experiences. This paper contributes to the Human-Computer/Robot Interaction (HCI/HRI) literature using a human-centred approach to identify characteristics of entrainment during pair- and group-based collaboration. We present five characteristics related to successful entrainment. These are related to the occurrence of entrainment, leader-follower patterns, interpersonal communication, the importance of the point-of-assembly, and the value of acoustic feedback. Finally, we present three design considerations for future research and design on collaboration with robots.

In this work, we present a novel, reactive, modulated control strategy based on dynamical systems (DS) for planning in the context of multiple non-convex obstacles. Our DS modulation strategy leverages an on-manifold planning methodology and provides several methods for real-time on-manifold navigation around non-convex obstacles. We introduce a sample-based obstacle representation for complex, non-convex obstacles, as well as a projection-based method for representing surfaces such as tables, cylinders, and ellipsoids. These representations can be combined to represent multiple obstacles and obstacle types (sample- or projection-based) with a single, continuously differentiable function. We validate our approach in several real-world scenarios, including navigation within (simulated) constrained environments, as well as reactive control of a real 7DoF manipulator with dynamic obstacles (including humans) while utilizing a 1 kHz control loop rate. Using our samplebased representation, we can calculate the obstacle representation function in less than 1 ms with up to 35k points using a CPU implementation, and up to 600k points with a GPU implementation.

Research in joint action focuses on the psychological, neurological, and physical mechanisms by which humans collabo-rate with other agents, and overlaps with several domains related to human-robot interaction. The development of artificial systems that can support or emulate the requisite aspects of joint action could lead to improved human-robot team performance as well as improvements in subjective metrics (e.g., trust). This workshop highlights theoretical and technical considerations about human-robot joint action and real-time adaptation, with a particular focus on socio-motor entrainment, showing how the emulation of psychological mechanisms (e.g., emotion, intention signaling, mirroring) can lead to improved performance. We will invite speakers with backgrounds in robotics, neuroscience and psychol-ogy, as well as speakers with a focus in adjacent works, such as in human-robot coordinated dance, alignment, or synchronization. We will call for papers that utilize the theory of joint-action in an interactive human-robot context. We will also call for position papers on the application of the theory of joint action to robotics, with a heavy focus on psychological mechanisms that could potentially be emulated or adapted to a human-robot context. Participants will have the opportunity to brainstorm considerations and techniques that would be applicable to joint action inspired works through breakout sessions with the aim to lead to new and improved collaborations across fields.

As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human's time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot's skill and to generalize its capabilities, especially as learning improves.

This thesis work intends to explore the development of a shared mental model between an autonomous agent and a human, where we aim to promote fluency in continuing interactions defined by repetitive tasks. That is, with repetitive actions, experimentation and increasing iterations, we wish the robot to learn how its own behavior affects that of its partner. To accomplish this, we propose a model that encodes both human and robot actions in a probabilistic space describing the temporal transition points between activities. The purpose of such a model is not only in passive predictive power (understanding the future actions of an associate), but also to encode the latent effect of a robot’s action on the future actions of the associate.

Augmenting a robot with the capacity to understand the activities of the people it collaborates with in order to then label and segment those activities allows the robot to generate an efficient and safe plan for performing its own actions. In this work, we introduce an online activity segmentation algorithm that can detect activity segments by processing a partial trajectory. We model the transitions through activities as a hidden Markov model, which runs online by implementing an efficient particle-filtering approach to infer the maximum a posteriori estimate of the activity sequence. This process is complemented by an online search process to refine activity segments using task model information about the partial order of activities. We evaluated our algorithm by comparing its performance to two state-of-the-art activity segmentation algorithms on three human activity datasets. The proposed algorithm improved activity segmentation accuracy across all three datasets compared with the other two approaches, with a range from 11.3% to 65.5%, and could accurately recognize an activity through observation alone for 31.6% of the initial trajectory of that activity, on average. We also implemented the algorithm onto an industrial mobile robot during an automotive assembly task in which the robot tracked a human worker's progress and provided the worker with the correct materials at the appropriate time.