Human joints are unique in the sense that their stiffness is not fixed and rather adjustable which aids in interacting with the physical world. A robot with servo-adjustable stiffness is intrinsically safe and capable of imitating human-like interactions. Apart from safety, soft joints are efficient in executing cyclic tasks which opens the door to have an agile behaviour. In this paper we extend our prior work on variable stiff joints and use two of them to design a two-link planar robot. Here we modify the dynamic model for this manipulator to include the variable stiffness term in addition to the joint torques as inputs, followed by joint torque calibration method, and then a simplified manipulator control template. This paper serves as an introduction to our ongoing work on vSEA and a foray into "true" manipulator space application.

Emotion detection presents challenges to intelligent human-robot interaction (URI). Foundational deep learning techniques used in emotion detection are limited by information-constrained datasets or models that lack the necessary complexity to learn interactions between input data elements, such as the the variance of human emotions across different contexts. In the current effort, we introduce 1) MoEmo (Motion to Emotion), a cross-attention vision transformer (ViT) for human emotion detection within robotics systems based on 3D human pose estimations across various contexts, and 2) a data set that offers full-body videos of human movement and corresponding emotion labels based on human gestures and environmental contexts. Compared to existing approaches, our method effectively leverages the subtle connections between movement vectors of gestures and environmental contexts through the use of cross-attention on the extracted movement vectors of full-body human gestures/poses and feature maps of environmental contexts. We implement a cross-attention fusion model to combine movement vectors and environment contexts into a joint representation to derive emotion estimation. Leveraging our Naturalistic Motion Database, we train the MoEmo system to jointly analyze motion and context, yielding emotion detection that outperforms the current state-of-the-art.

The cluster space state representation for multi-robot systems provides a simple means of specifying and monitoring the geometry and motion characteristics of a cluster of mobile robots. In previous work, this approach has been experimentally verified and validated for controlling the motion of mobile multi-robot systems ranging from land rovers to autonomous boats. In this paper we introduce a compact collision avoidance algorithm that operates at the level of the cluster, leading to coordinated translational and rotational motions that allow obstacles to be avoided while maintaining the relative geometry of the cluster. This paper formulates the potential-field based obstacle avoidance algorithm, describes its integration within the cluster space control architecture, and presents successful experimental results of its application to two simple, diverse multi-robot testbeds.

The tasks of entrapping/escorting and patrolling around an autonomous target are presented making use of the multi-robot cluster space control approach. The cluster space control technique promotes simplified specification and monitoring of the motion of mobile multi-robot systems of limited size. Previous work has established the conceptual foundation of this approach and has experimentally verified and validated its use for 2-robot, 3-robot and 4-robot systems, with varying implementations ranging from automated trajectory control to human-in-the-loop piloting. In this publication, we show that the problem of entrapping/escorting/patrolling is trivial to define and manage from a cluster space perspective. Using a 3-robot experimental testbed, results are shown for the given tasks. We also revise the definition of the cluster space framework for a three-robot formation and incorporate a robot-level obstacle avoidance functionality.

The cluster space control technique promotes simplified specification and monitoring of the motion of mobile multi-robot systems of limited size. Previous work has established the conceptual foundation of this approach and has experimentally verified and validated its use for two diverse 2- robot systems and with varying implementations ranging from automated trajectory control to human-in-the-loop piloting. In this paper, we present the cluster space control of a 3-robot system. In doing so, we develop the fundamental kinematic relationships, illustrate the closed-loop control framework, describe the simulation and hardware testbed environments used for verification, and present initial experimental results of the successfully implemented system.

Santa Clara University's Robotic Systems Laboratory conducts an aggressive robotic development and operations program in which interdisciplinary teams of undergraduate students build and deploy a wide range of robotic systems, ranging from underwater vehicles to spacecraft. These year-long projects expose students to the breadth of and interdependence among engineering disciplines, the span of processes in a system development lifecycle, and the challenges of managing a development process. Over the past five years, this program has provided more than 150 students with exposure to computer science and engineering topics, including software engineering, algorithm development, human-computer interface design, and artificial intelligence. This program provides exciting and compelling educational opportunities for students, offers real-world applications that naturally motivate the need for specific computing technologies, and serves a broader research and development program that utilizes the functional robotic systems to support externally-funded science and technology demonstration missions. The experience of the authors, as well as formal program assessment data, show that this program provides strong student motivation for learning, offers comprehensive and valuable educational experiences, and enhances student performance. This article reviews the Santa Clara robotics program, highlights the role of computer science and engineering in several projects, and presents the assessment data showing the positive results of this program.