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 [...]
HaptiClay: An Interactive Haptic Interface for Gestured Concretization of Polynomial Functions
Abstract: In this work we present HaptiClay, a low-cost kinesthetic haptic interface that elevates the understanding of mathematics language by providing embodied non-verbal representations of math concepts. Our interface integrates four key components: a haptic device, a high-level simulation that communicates with a low-level controller for force and position updates, a low-level controller that executes [...]
High-resolution cloth simulation in milliseconds: Efficient GPU Cloth Simulation with Non-distance Barriers and Subspace Reuse Interactions
Abstract: We show how to push the performance of high-resolution cloth simulation, making the simulation interactive (in milliseconds) for models with one million degrees of freedom (DOFs) while keeping every triangle untangled. The guarantee of being penetration-free is inspired by the interior-point method, which converts the inequality constraints to barrier potentials. Nevertheless, we propose a [...]
Better Standards for Trajectory Forecasting: Data, Evaluation, and Methods
Abstract: Ensuring pedestrian safety in dynamic environments is a key challenge for autonomous systems, particularly in dynamic, multi-agent environments. Trajectory forecasting plays a central role in enabling these systems to anticipate pedestrian behaviors and respond appropriately. This thesis addresses three core limitations in trajectory forecasting systems which impede safe and robust trajectory forecasting: inadequate evaluation protocols [...]
Bridging Generative and Discriminative Learning with Diffusion Models
Abstract: Generative models have advanced significantly, synthesizing photorealistic images, videos, and text. Building on this progress, our work explores the potential of diffusion models to bridge generative and discriminative learning, uncovering new pathways for leveraging their strengths in visual perception tasks. In the first part, we propose Diff-2-in-1, a unified framework for multi-modal data generation [...]
Bring Hand to The Air: Towards Universal Aerial Manipulation
Abstract: Uncrewed Aerial Vehicles (UAVs) have attracted the interest of researchers, industry, and the general public in many applications. Noticing that high-altitude tasks sometimes require active interaction with the environment, there have been more and more works focusing on aerial manipulation recently. Each of them has demonstrated the ability to use a specific aerial manipulator [...]
Robust Reinforcement Learning for Safety Critical Applications via Curricular Learning
Abstract: Reinforcement Learning (RL) presents great promises for autonomous agents. However, when using robots in a safety critical domain, a system has to be robust enough to be deployed in real life. For example, the robot should be able to perform across different scenarios it will encounter. The robot should avoid entering undesirable and irreversible [...]
Leveraging Sense of Agency to Improve the Experience of Control Over Assistive Robots
Abstract: In an age of autonomous driving and robotics, we are increasingly engaging with robots that deploy autonomous assistance. Cognitive science and human-computer interaction literature tells us that, when we apply autonomy in assistive settings, we are often augmenting the user's sense of agency over the system. Sense of agency is a phenomenon from cognitive [...]
RI Faculty Business Meeting
Meeting for RI Faculty. Agenda was sent via a calendar invite.