Observational learning is a promising approach to enable people without expertise in programming to transfer skills to robots in a user-friendly manner, since it mirrors how humans learn new behaviors by observing others. Many existing methods focus on instructing robots to mimic human trajectories, but motion-level strategies often pose challenges in skills generalization across diverse environments. This paper proposes a novel framework that allows robots to achieve a higher-level understanding of human-demonstrated manual tasks recorded in RGB videos. By recognizing the task structure and goals, robots generalize what observed to unseen scenarios. We found our task representation on Shannon's Information Theory (IT), which is applied for the first time to manual tasks. IT helps extract the active scene elements and quantify the information shared between hands and objects. We exploit scene graph properties to encode the extracted interaction features in a compact structure and segment the demonstration into blocks, streamlining the generation of Behavior Trees for robot replicas. Experiments validated the effectiveness of IT to automatically generate robot execution plans from a single human demonstration. Additionally, we provide HANDSOME, an open-source dataset of HAND Skills demOnstrated by Multi-subjEcts, to promote further research and evaluation in this field.

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects.

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.

In hybrid industrial environments, workers' comfort and positive perception of safety are essential requirements for successful acceptance and usage of collaborative robots. This paper proposes a novel human-robot interaction framework in which the robot behaviour is adapted online according to the operator's cognitive workload and stress. The method exploits the generation of B-spline trajectories in the joint space and formulation of a multi-objective optimisation problem to online adjust the total execution time and smoothness of the robot trajectories. The former ensures human efficiency and productivity of the workplace, while the latter contributes to safeguarding the user's comfort and cognitive ergonomics. The performance of the proposed framework was evaluated in a typical industrial task. Results demonstrated its capability to enhance the productivity of the human-robot dyad while mitigating the cognitive workload induced in the worker.