Complex and unstructured environments pose several challenges for traditional rigid robot technologies.
Inspired by biological systems, soft robots offer a promising alternative with respect to their rigid counterparts and demonstrate increased resilience and adaptation, resulting in machines that can safely interact with natural environments.
Mimicking how biological systems use their soft and dexterous body to interact with and exploit their surroundings entails addressing multiple fundamental challenges related to the design, manufacturing, and control of soft robots.
In this talk, I will present our research on developing new manufacturing methods to enable the fabrication of multi-degrees-of-freedom soft robots with distributed actuation and multiscale features.
I will also discuss opportunities and challenges arising in deploying soft multi-degrees-of-freedom soft robots in the real world. Specifically, I will introduce our work on methods to embed control and computational capabilities onboard soft robots to increase their autonomy, focusing on our efforts towards enabling electronic control of multi-DoF fluidic soft robots.
Finally, I will present our work on the application of soft robotic technologies in minimally invasive surgery. I will discuss various applications, including atraumatic manipulation of large abdominal organs and accurate and effective manipulation of delicate structures inside the beating heart.
Tom Ranzani received a Bachelor’s and Master’s degree in Biomedical Engineering from the University of Pisa, Italy. He did his Ph.D. at the BioRobotics Institute of the Sant’Anna School of Advanced Studies. In 2014, he joined the Wyss Institute for Biologically Inspired Engineering at the Harvard John A. Paulson School of Engineering and Applied Sciences as a postdoctoral fellow.
He is currently an Assistant Professor in the Department of Mechanical Engineering, Biomedical Engineering, and in the Division of Materials Science and Engineering at Boston University, where he established the Morphable Biorobotics Lab in 2018.
In 2020 he was awarded the NIH Trailblazer Award for New and Early Stage Investigators.
His research focuses on soft and bioinspired robotics with applications ranging from underwater exploration to surgical and wearable devices. He is interested in expanding the potential of soft robots across different scales to develop novel reconfigurable soft-bodied robots capable of operating in environments where traditional robots cannot.
Recent technological advances in the field of surgical Robotics have resulted in the development of a range of
new techniques and technologies that have reduced patient trauma, shortened hospitalization, and improved
diagnostic accuracy and therapeutic outcome. Despite the many appreciated benefits of robot-assisted mini-
mally invasive surgery (MIS), there are still significant drawbacks associated with these technologies including, dexterity, intelligence, and autonomy of the developed robotic devices and prognosis design of medical devices and implants.
The dexterity limitation is associated with the poor accessibility to the areas of interest and insufficient
instrument control and ergonomics caused by rigidity of the conventional instruments and implants. In other words, the ability to adequately access different target anatomy is still the main challenge of MIS end endoscopic procedures demanding specialized instrumentation, sensing and control paradigms.
To enhance the safety of robot-assisted procedures, current robotics research is also exploring new ways of providing synergistic intelligent semi/autonomous control between the surgeon and the robot. In this context, the robot can perform certain surgical tasks autonomously under the supervision of the surgeon. However, such autonomy not only requires understanding the robot’s perception and adaptation to dynamically changing environments of the tissue, but also it requires understanding the mental workload and decision-making state of the surgeon as the decision-maker and key component of this systems. This demands a Surgeon-Centric Brain-In-the-Loop Autonomous Control techniques.
To address these challenges, this talk covers our efforts towards engineering of surgery (surgineering) and bringing dexterity and autonomy in various robot-assisted minimally invasive surgical procedures. Particulalrly, I will discuss our efforts towards enhancing the existing paradigm in spinal fixation, colorectal cancer diagnosis, and bioprinting of volumetric muscle loss injuries using continuum manipulators, soft sensors, flexible implants, and semi/autonomous intelligent surgical robotic systems.
Dr. Farshid Alambeigi is an Assistant Professor at the Walker Department of Mechanical Engineering at the University of Texas at Austin since August 2019. He is also one of the core faculties of the Texas Robotics. Dr. Alambeigi received his Ph.D. in Mechanical Engineering from the Johns Hopkins University, in 2019. He also holds an M.Sc. degree (2017) in Robotics from the Johns Hopkins University. In summer of 2018, Dr. Alambeigi received the 2019 SIEBEL Scholarship because of the academic excellence and demonstrated leadership. In 2020, Dr. Alambeigi received the NIH NIBIB Trailblazer Career Award to develop novel flexible implants and robots for minimally invasive spinal fixation surgery. He also has received the prestigious 2022 NIH Director’s New Innovator Award to develop an in vivo bioprinting surgical robotic system for treatment of volumetric muscle loss.
At The University of Texas at Austin, Dr. Alambeigi directs the Advanced Robotic Technologies for Surgery (ARTS) Lab. Dr. Alambeigi’s research focuses on developing high dexterity and situationally aware continuum manipulators, soft robots, and appropriate instruments and sensors designed for less/minimally invasive treatment of various medical applications. Utilizing these novel surgical instruments together with intelligent control algorithms, the ARTS Lab in collaboration with the UT Dell Medical School will work toward engineering of the surgery (Surgineering) and partnering dexterous intelligent robots with surgeons. Ultimately, our goal is to augment the clinicians’ skills and quality of the surgery to further improve patient’s safety and outcomes.
Conventional robotic systems are most effective in structured environments with well-defined tasks. The next frontier of robotics is to create systems that can operate in challenging environments while autonomously adapting to changing and uncertain task requirements. In the field of modular self-reconfigurable robotics, we approach this challenge by designing a set of robotic building blocks that can be combined to form a variety of robot morphologies. By autonomously rearranging these modules, the system can change its shape to complete a wider variety of tasks than is possible with a fixed morphology.
In this talk, I will present my research on a new modular robot, the Variable Topology Truss (VTT). Most existing modular self-reconfigurable robots use cube-shaped modules that connect together on a lattice or as a serial string of joints. These architectures are convenient when it comes to designing reconfiguration algorithms, but they face serious practical challenges when it comes to scaling the system up to solve larger tasks. Instead, VTT uses a truss-based architecture. Individual modules are beams which can extend or retract using a novel high-extension-ratio linear actuator: the Spiral Zipper. By connecting the beam modules together like a truss, we can create large, lightweight structures with much greater structural efficiency than conventional modular architectures. Furthermore, the length-changing ability of the Spiral Zipper allows the system to more flexibly adapt its scale and geometry without needing to use as many modules. However, the truss architecture poses new challenges when it comes to motion and reconfiguration planning. I will discuss the hardware design of the VTT system as well as my research on collision-free motion and reconfiguration planning for this novel system.
Alexander Spinos received his Bachelor’s degree in Mechanical Engineering from Johns Hopkins University. He then joined the Modlab in GRASP at the University of Pennsylvania, where he received his PhD in Mechanical Engineering and Applied Mechanics. His dissertation centered around the mechanical design and self-reconfiguration planning of the Variable Topology Truss, a modular self-reconfigurable parallel robot. He is now a robotics researcher at the JHU Applied Physics Lab, where he works on multi-robot planning and the design of novel robot hardware.
Title: Decision Making with Internet-Scale Knowledge
Abstract: Machine learning models pretrained on internet data have acquired broad knowledge about the world but struggle to solve complex tasks that require extended reasoning and planning. Sequential decision making, on the other hand, has empowered AlphaGo’s superhuman performance, but lacks visual, language, and physical knowledge about the world. In this talk, I will present my research towards enabling decision making with internet-scale knowledge. First, I will illustrate how language models and video generation are unified interfaces that can integrate internet knowledge and represent diverse tasks, enabling the creation of a generative simulator to support real-world decision-making. Second, I will discuss my work on designing decision making algorithms that can take advantage of generative language and video models as agents and environments. Combining pretrained models with decision making algorithms can effectively enable a wide range of applications such as developing chatbots, learning robot policies, and discovering novel materials.
Bio: Sherry is a final year PhD student at UC Berkeley advised by Pieter Abbeel and a senior research scientist at Google DeepMind. Her research aims to develop machine learning models with internet-scale knowledge to make better-than-human decisions. To this end, she has developed techniques for generative modeling and representation learning from large-scale vision, language, and structured data, coupled with developing algorithms for sequential decision making such as imitation learning, planning, and reinforcement learning. Sherry initiated and led the Foundation Models for Decision Making workshop at NeurIPS 2022 and 2023, bringing together research communities in vision, language, planning, and reinforcement learning to solve complex decision making tasks at scale. Before her current role, Sherry received her Bachelor’s degree and Master’s degree from MIT advised by Patrick Winston and Julian Shun.