Interactive Intelligence: Multimodal AI for Real-Time Interaction Loop towards Attentive E-Reading

E-learning has shifted the traditional learning paradigms in higher education, offering more flexible, ubiquitous, and personalized learning experiences. The previous years COVID-19 pandemic required a re-calibration of education to accommodate virtual learning environments from the traditional classroom-based education. Widespread learning...

A review on machine learning in flexible surgical and interventional robots: Where we are and where we are going

Minimally Invasive Procedures (MIPs) emerged as an alternative to more invasive surgical approaches, offering patient benefits such as smaller incisions, less pain, and shorter hospital stay. In one class of MIPs, where natural body lumens or small incisions are used to access deeper anatomical locations, Flexible Surgical and Interventional...

Individual and joint body movement assessed by wearable sensing as a predictor of attraction in speed dates

Interpersonal attraction is known to motivate behavioral responses in the person experiencing this subjective phenomenon. Such responses may involve the imitation of behavior, as in mirroring or mimicry of postures or gestures, which have been found to be associated with the desire to be liked by an interlocutor. Speed dating provides a...

What factors contribute to the acceptance of artificial intelligence? A systematic review

Artificial Intelligence (AI) agents are predicted to infiltrate most industries within the next decade, creating a personal, industrial, and social shift towards the new technology. As a result, there has been a surge of interest and research towards user acceptance of AI technology in recent years. However, the existing research appears...

Interaction-Aware Motion Planning in Crowded Dynamic Environments

Autonomous robots will profoundly impact our society, making our roads safer, reducing labor costs and carbon dioxide (CO2) emissions, and improving our life quality. However, to make that happen, robots need to navigate among humans, which is extremely difficult. Firstly, humans do not explicitly communicate their intentions and use intuition...

Modeling Human Motor Skills to Enhance Robots’ Physical Interaction

The need for users’ safety and technology acceptability has incredibly increased with the deployment of co-bots physically interacting with humans in industrial settings, and for people assistance. A well-studied approach to meet these requirements is to ensure human-like robot motions and interactions. In this manuscript, we present a...

Selecting Informative Data Samples for Model Learning Through Symbolic Regression

Continual model learning for nonlinear dynamic systems, such as autonomous robots, presents several challenges. First, it tends to be computationally expensive as the amount of data collected by the robot quickly grows in time. Second, the model accuracy is impaired when data from repetitive motions prevail in the training set and outweigh...

Deep Learning and Machine Learning in Robotics

Deep learning has gone through massive growth in recent years. In many fields—computer vision, speech recognition, machine translation, game playing, and others—deep learning has brought unprecedented progress and become the method of choice. Will the same happen in robotics and automation? In a sense, it is already happening. Today, deep...

A selective muscle fatigue management approach to ergonomic human-robot co-manipulation

In this paper, we propose a method for selective monitoring and management of human muscle fatigue in human-robot co-manipulation scenarios. The proposed approach uses a machine learning technique to learn the complex relationship between individual human muscle forces, arm configuration and arm endpoint force that are provided by a...

Using Gaussian mixture models for gesture recognition during haptically guided telemanipulation

Haptic guidance is a promising method for assisting an operator in solving robotic remote operation tasks. It can be implemented through different methods, such as virtual fixtures, where a predefined trajectory is used to generate guidance forces, or interactive guidance, where sensor measurements are used to assist the operator in real-time...

Deep Learning Object-Recognition in a Design-to-Robotic-Production and -Operation Implementation

This paper presents a new instance in a series of discrete proof-of-concept implementations of comprehensively intelligent built-environments based on Design-to-Robotic-Production and -Operation (D2RP&O) principles developed at Delft University of Technology (TUD). With respect to D2RP, the featured implementation presents a customized...

Reinforcement learning of potential fields to achieve limit-cycle walking

Reinforcement learning is a powerful tool to derive controllers for systems where no models are available. Particularly policy search algorithms are suitable for complex systems, to keep learning time manageable and account for continuous state and action spaces. However, these algorithms demand more insight into the system to choose a...