The CIS Department and GRASP Lab invite you to please join us on Thursday, November 17th, at 3:30pm as we celebrate the life and legacy of Dr. Max Mintz, Professor of Computer and Information Science. Max joined Penn as an assistant professor of Systems Engineering (now part of ESE) in 1974. He changed his primary […]
Aquatic modular self-reconfigurable robotic systems (MSRRs) have incredible potential for bringing practical, flexible, and adaptable robotic tools to challenging environments. They could build mobile ocean platforms or bridges for larger vehicles, act as ocean-going manipulators to perform repairs on infrastructure, or function as oceanographic research platforms, using reconfiguration to achieve precise spatial resolution when sensing […]
ABSTRACT: An important goal of the field sensorimotor robot learning is to do away with cumbersome expertise-intensive task specification, so that general-purpose robots of the future might learn large numbers of new skills. In this talk, I will discuss our recent work on algorithms that exploit goals as a versatile and accessible task specification interface. Goals might […]
Imperfect but useful physical models play a crucial role in planning and controlling robot locomotion and manipulation. However, as the field shifts toward complex real-world applications, model accuracy requirements are increasing while calibration data is becoming scarcer. Consequently, model inaccuracy or the sim2real gap often hinders the development of performant robotics algorithms. Frictional contact, the […]
By mirroring the success of biological systems, legged robots have the potential to be successful in almost every terrestrial environment. While legged machines have made significant advancements in the past 20 years, there still exists a considerable gap between what they can achieve and the abilities of animals. In this talk I’ll discuss some of […]
Over the next decade, the biggest generator of data is expected to be devices that sense and control the physical world. The explosion of real-time data that is emerging from the physical world requires a rapprochement of areas such as machine learning, control theory, and optimization. While control theory has been firmly rooted in the […]
Planning fast and autonomous robotic motion in unstructured, cluttered environments remains a core challenge of the robotics community. The robot’s motion must be agile and dynamic, operating near the limit of its physical capabilities, to reach its goal location as quickly as possible. Fast motion in turn creates a need for fast plan computation over […]
Imperfect but useful dynamical models have enabled significant progress in planning and controlling robotic locomotion and manipulation. Traditionally, these models have been physics-based, with accuracy relying upon manual calibration only feasible in laboratory environments. As robotics expands into complex real-world applications, models must instead be automatically fit to limited data. One major challenge is modeling […]
Multi-robot coordination and cooperation are critical behaviors that improve team performance and enable new tasks in application areas like autonomous construction, agriculture, and extended operation in large unknown regions. This talk explores the utility of graph-based modeling and computational frameworks to improve the coordination and cooperation of multi-robot teams in a variety of problems. I […]
A modular self-reconfigurable robot (MSRR) is a set of robotic building blocks that can be connected together in different ways. By rearranging these connections, the robot can adapt its shape to address a wider variety of tasks than a robot with a fixed morphology. However, traditional modular architectures fail to scale up to address large-scale […]
