Abstract: In this talk, we cover four aspects of research on general manipulation for assistive robotics : i), efficiently adapting large-scale models to downstream scene affordance understanding tasks, especially in daily living scenarios where gathering multi-task data involving humans requires strenuous effort; ii), effectively learning robot action trajectories with our flow matching policy; iii) sim-to-real physics learning for deformable manipulation and robot-assisted dressing;  iv) multi-modal (vision, tactile, audio) learning.

Title: Uncertainty-Aware Motion Planning for Autonomous Driving in Interactive Urban Scenarios

Speaker: Zhaocheng Liu

Title: Learning protein transition states from simulation data via Flow Matching

Abstract: We have a long history of robotics in manufacturing, and over the past 20 years a second wave of robotics in warehouses - both controlled environments. As we bring robots into the world to operate in unconstrained environments around the general public, new challenges arise. This talk will review work from Willow Garage with early humanoid robots, and some of the challenges of bringing robots to hotels and hospitals. We will introduce the Stanford Robotics Center, which is looking at the next generation of challenges.

Abstract: Fiber Bragg Gratings (FBGs) based shape sensing allows for precise, real-time tracking of medical instruments within the body. Standard FBG (SFBG) sensors detect shape changes by analysing the reflected light wavelengths from multiple FBGs along an optical fibre, which respond to strain variations as the fibre bends or twists. Tilted Fiber Bragg Gratings (TFBGs) offer the additional capability of sensing temperature and biologically relevant signals like pH independent of strain in situ.

Abstract: Vision-based methods have been shown to achieve clinically acceptable accuracy in measuring patient mobility. In this work, the daily behaviours and mobility of older adults are monitored using a privacy-preserving camera in their homes in Edinburgh for several weeks. The monitoring approach includes a discriminative model to classify individuals' Functional Behavioural States (postural-mobility features), and a personalized generative model to describe each individual’s statistical patterns over time.

Abstract: In this talk, I will outline ingredients for enabling efficient robot learning. First, I will demonstrate how large vision-language models can enhance scene understanding and generalization, allowing robots to learn general rules from specific examples for handling everyday objects. Then, I will describe a policy learning method that leverages equivariance to significantly reduce the amount of training data needed for learning from human demonstrations. Moving beyond learning from demonstrations, we will explore how simulation can enable robots to learn autonomously.

Abstract: The reward hypothesis states "all of what we mean by goals and purposes can be well thought of as maximization of the expected value of the cumulative sum of a received scalar signal (reward)." In this talk, I present our recent line of work that aims to settle this hypothesis---we prove that, under a particular interpretation, the reward hypothesis is true if and only if a set of five conditions hold. I then explore some of the consequences of this finding for the design of learning agents and their goals.

Description: Hydraulic actuation is used wherever high forces are needed in compact packages, including heavy offroad machinery, industrial equipment and aircraft. But this multi-billion dollar industry has a guilty secret – despite over a century of incremental improvement, today’s hydraulic systems are still based on legacy analog architectures, wasting as much as 70% of the energy used to power them… causing millions of tonnes of needless CO2 emissions per year.

Bees are the only species other than humans that have been shown to be capable of communicating detailed information about their spatial surroundings to conspecifics. By performing a stereotyped 'dance' in the nest, the bee provides its nestmate with the spatial coordinates of a distant food source, such that the nestmate can subsequently travel there independently. This behaviour has famously been described as a language, and has been studied behaviourally for many years, but only recently have plausible neural models to explain this behaviour been proposed.