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.