\u3cem\u3eGRASP News\u3c/em\u3e, Volume 8, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory. Edited by Thomas Lindsay

Predicting human motion intention for pHRI assistive control

This work addresses human intention identification during physical Human-Robot Interaction (pHRI) tasks to include this information in an assistive controller. To this purpose, human intention is defined as the desired trajectory that the human wants to follow over a finite rolling prediction horizon so that the robot can assist in pursuing it. This work investigates a Recurrent Neural Network (RNN), specifically, Long-Short Term Memory (LSTM) cascaded with a Fully Connected layer. In particular, we propose an iterative training procedure to adapt the model. Such an iterative procedure is powerful in reducing the prediction error. Still, it has the drawback that it is time-consuming and does not generalize to different users or different co-manipulated objects. To overcome this issue, Transfer Learning (TL) adapts the pre-trained model to new trajectories, users, and co-manipulated objects by freezing the LSTM layer and fine-tuning the last FC layer, which makes the procedure faster. Experiments show that the iterative procedure adapts the model and reduces prediction error. Experiments also show that TL adapts to different users and to the co-manipulation of a large object. Finally, to check the utility of adopting the proposed method, we compare the proposed controller enhanced by the intention prediction with the other two standard controllers of pHRI

A fabric-based approach for wearable haptics

In recent years, wearable haptic systems (WHS) have gained increasing attention as a novel and exciting paradigm for human-robot interaction (HRI).These systems can be worn by users, carried around, and integrated in their everyday lives, thus enabling a more natural manner to deliver tactile cues.At the same time, the design of these types of devices presents new issues: the challenge is the correct identification of design guidelines, with the two-fold goal of minimizing system encumbrance and increasing the effectiveness and naturalness of stimulus delivery.Fabrics can represent a viable solution to tackle these issues.They are specifically thought “to be worn”, and could be the key ingredient to develop wearable haptic interfaces conceived for a more natural HRI.In this paper, the author will review some examples of fabric-based WHS that can be applied to different body locations, and elicit different haptic perceptions for different application fields.Perspective and future developments of this approach will be discussed

Intelligent Robotic Perception Systems

Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics

Robot manipulation in human environments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 211-228).Human environments present special challenges for robot manipulation. They are often dynamic, difficult to predict, and beyond the control of a robot engineer. Fortunately, many characteristics of these settings can be used to a robot's advantage. Human environments are typically populated by people, and a robot can rely on the guidance and assistance of a human collaborator. Everyday objects exhibit common, task-relevant features that reduce the cognitive load required for the object's use. Many tasks can be achieved through the detection and control of these sparse perceptual features. And finally, a robot is more than a passive observer of the world. It can use its body to reduce its perceptual uncertainty about the world. In this thesis we present advances in robot manipulation that address the unique challenges of human environments. We describe the design of a humanoid robot named Domo, develop methods that allow Domo to assist a person in everyday tasks, and discuss general strategies for building robots that work alongside people in their homes and workplaces.by Aaron Ladd Edsinger.Ph.D

Intelligent Haptic Perception for Physical Robot Interaction

Doctorado en Ingeniería mecatrónica. Fecha de entrega de la Tesis doctoral: 8 de enero de 2020. Fecha de lectura de Tesis doctoral: 30 de marzo 2020.The dream of having robots living among us is coming true thanks to the recent advances in Artificial Intelligence (AI). The gap that still exists between that dream and reality will be filled by scientific research, but manifold challenges are yet to be addressed. Handling the complexity and uncertainty of real-world scenarios is still the major challenge in robotics nowadays. In this respect, novel AI methods are giving the robots the capability to learn from experience and therefore to cope with real-life situations. Moreover, we live in a physical world in which physical interactions are both vital and natural. Thus, those robots that are being developed to live among humans must perform tasks that require physical interactions. Haptic perception, conceived as the idea of feeling and processing tactile and kinesthetic sensations, is essential for making this physical interaction possible. This research is inspired by the dream of having robots among us, and therefore, addresses the challenge of developing robots with haptic perception capabilities that can operate in real-world scenarios. This PhD thesis tackles the problems related to physical robot interaction by employing machine learning techniques. Three AI solutions are proposed for different physical robot interaction challenges: i) Grasping and manipulation of humans’ limbs; ii) Tactile object recognition; iii) Control of Variable-Stiffness-Link (VSL) manipulators. The ideas behind this research work have potential robotic applications such as search and rescue, healthcare or rehabilitation. This dissertation consists of a compendium of publications comprising as the main body a compilation of previously published scientific articles. The baseline of this research is composed of a total of five papers published in prestigious peer-reviewed scientific journals and international robotics conferences

Autonomous aerial robot for high-speed search and intercept applications

In recent years, high-speed navigation and environment interaction in the context of aerial robotics has become a field of interest for several academic and industrial research studies. In particular, Search and Intercept (SaI) applications for aerial robots pose a compelling research area due to their potential usability in several environments. Nevertheless, SaI tasks involve a challenging development regarding sensory weight, onboard computation resources, actuation design, and algorithms for perception and control, among others. In this work, a fully autonomous aerial robot for high-speed object grasping has been proposed. As an additional subtask, our system is able to autonomously pierce balloons located in poles close to the surface. Our first contribution is the design of the aerial robot at an actuation and sensory level consisting of a novel gripper design with additional sensors enabling the robot to grasp objects at high speeds. The second contribution is a complete software framework consisting of perception, state estimation, motion planning, motion control, and mission control in order to rapidly and robustly perform the autonomous grasping mission. Our approach has been validated in a challenging international competition and has shown outstanding results, being able to autonomously search, follow, and grasp a moving object at 6 m/s in an outdoor environment.Agencia Estatal de InvestigaciónKhalifa Universit