RI Seminar
Robots That Know When They Don’t Know
Abstract: Foundation models from machine learning have enabled rapid advances in perception, planning, and natural language understanding for robots. However, current systems lack any rigorous assurances when required to generalize to novel scenarios. For example, perception systems can fail to identify or localize unfamiliar objects, and large language model (LLM)-based planners can hallucinate outputs that [...]
Abstraction Barriers for Embodied Algorithms
Abstract: Designing robotic systems to reliably modify their environment typically requires expert engineers and several design iterations. This talk will cover abstraction barriers that can be used to make the process of building such systems easier and the results more predictable. By focusing on approximate mathematical representations that model the process dynamics, these representations can [...]
Autonomous Robotic Surgery: Science Fiction or Reality?
Abstract: Robotic assisted surgery (RAS) systems incorporate highly dexterous tools, hand tremor filtering, and motion scaling to enable a minimally invasive surgical approach, reducing collateral damage and patient recovery times. However, current state-of-the-art telerobotic surgery requires a surgeon operating every motion of the robot, resulting in long procedure times and inconsistent results. The advantages of [...]
Learning for Dynamic Robot Manipulation of Deformable and Transparent Objects
Abstract: Dynamics, softness, deformability, and difficult-to-detect objects will be critical for new domains in robotic manipulation. But there are complications--including unmodelled dynamic effects, infinite-dimensional state spaces of deformable objects, and missing features from perception. This talk explores learning methods based on multi-view sensing, acoustics, physics-based regularizations, and Koopman operators and proposes a novel multi-finger soft [...]
Uncertainty and Contact with the World
Abstract: As robots move out of the lab and factory and into more challenging environments, uncertainty in the robot's state, dynamics, and contact conditions becomes a fact of life. We will never be able to perfectly predict the forces on the robot's feet as it walks through unknown mud or control the deflections of a [...]
Towards Open World Robot Safety
Abstract: Robot safety is a nuanced concept. We commonly equate safety with collision-avoidance, but in complex, real-world environments (i.e., the “open world’’) it can be much more: for example, a mobile manipulator should understand when it is not confident about a requested task, that areas roped off by caution tape should never be breached, and [...]
Developing Physically Capable and Intelligent Robots
Abstract: Dr. Rizzi will provide an overview of the ongoing work at the Robotics and AI Institute (RAI Institute) and its ongoing research efforts focused on the design and control of the next generation of intelligent and capable robotics systems. The focus is on the development of systems capable of performing complex dynamic tasks at [...]
Is Data All You Need?: Large Robot Action Models and Good Old Fashioned Engineering
Abstract: Enthusiasm has been skyrocketing for humanoids based on recent advances in "end-to-end" large robot action models. Initial results are promising, and several collaborative efforts are underway to collect the needed demonstration data. But is data really all you need? Although end-to-end Large Vision, Language, Action (VLA) Models have potential to generalize and reliably solve [...]
