Publications (30)
Protective Policy Transfer
Wenhao Yu, C. Karen Liu, Greg Turk
Being able to transfer existing skills to new situations is a key capability when training robots to operate in unpredictable real-world environments. A successful transfer algorit…
Fluid Simulation on Vortex Particle Flow Maps
Sinan Wang, Junwei Zhou, Fan Feng +5
We propose the Vortex Particle Flow Map (VPFM) method to simulate incompressible flow with complex vortical evolution in the presence of dynamic solid boundaries. The core insight…
Generative Modeling with Orbit-Space Particle Flow Matching
Sinan Wang, Jinjin He, Shenyifan Lu +3
We present Orbit-Space Geometric Probability Paths (OGPP), a particle-native flow-matching framework for generative modeling of particle systems. OGPP is motivated by two insights:…
Shape Transformation Using Variational Implicit Functions
Greg Turk, James F. O'Brien
Traditionally, shape transformation using implicit functions is performed in two distinct steps: 1) creating two implicit functions, and 2) interpolating between these two function…
Learning to Transfer In-Hand Manipulations Using a Greedy Shape Curriculum
Yunbo Zhang, Alexander Clegg, Sehoon Ha +2
In-hand object manipulation is challenging to simulate due to complex contact dynamics, non-repetitive finger gaits, and the need to indirectly control unactuated objects. Further…
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
Zackory Erickson, Henry M. Clever, Greg Turk +2
Robot-assisted dressing offers an opportunity to benefit the lives of many people with disabilities, such as some older adults. However, robots currently lack common sense about th…
Multi-task Learning with Gradient Guided Policy Specialization
Wenhao Yu, C. Karen Liu, Greg Turk
We present a method for efficient learning of control policies for multiple related robotic motor skills. Our approach consists of two stages, joint training and specialization tra…
Transforming a Quadruped into a Guide Robot for the Visually Impaired: Formalizing Wayfinding, Interaction Modeling, and Safety Mechanism
J. Taery Kim, Wenhao Yu, Yash Kothari +3
This paper explores the principles for transforming a quadrupedal robot into a guide robot for individuals with visual impairments. A guide robot has great potential to resolve the…
Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing
Zackory Erickson, Henry M. Clever, Vamsee Gangaram +3
Robotic assistance presents an opportunity to benefit the lives of many people with physical disabilities, yet accurately sensing the human body and tracking human motion remain di…
Policy Transfer with Strategy Optimization
Wenhao Yu, C. Karen Liu, Greg Turk
Computer simulation provides an automatic and safe way for training robotic control policies to achieve complex tasks such as locomotion. However, a policy trained in simulation us…
Learning Symmetric and Low-energy Locomotion
Wenhao Yu, Greg Turk, C. Karen Liu
Learning locomotion skills is a challenging problem. To generate realistic and smooth locomotion, existing methods use motion capture, finite state machines or morphology-specific…
Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Image using Synthetic Data
Henry M. Clever, Zackory Erickson, Ariel Kapusta +3
People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial applications, yet line-of-sight p…
Auditing Gender Presentation Differences in Text-to-Image Models
Yanzhe Zhang, Lu Jiang, Greg Turk +1
Text-to-image models, which can generate high-quality images based on textual input, have recently enabled various content-creation tools. Despite significantly affecting a wide ra…
Characterizing Multidimensional Capacitive Servoing for Physical Human-Robot Interaction
Zackory Erickson, Henry M. Clever, Vamsee Gangaram +4
Towards the goal of robots performing robust and intelligent physical interactions with people, it is crucial that robots are able to accurately sense the human body, follow trajec…
Preparing for the Unknown: Learning a Universal Policy with Online System Identification
Wenhao Yu, Jie Tan, C. Karen Liu +1
We present a new method of learning control policies that successfully operate under unknown dynamic models. We create such policies by leveraging a large number of training exampl…
Learning to Navigate Cloth using Haptics
Alexander Clegg, Wenhao Yu, Zackory Erickson +3
We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a…
Learning Novel Policies For Tasks
Yunbo Zhang, Wenhao Yu, Greg Turk
In this work, we present a reinforcement learning algorithm that can find a variety of policies (novel policies) for a task that is given by a task reward function. Our method does…
Simulation and Retargeting of Complex Multi-Character Interactions
Yunbo Zhang, Deepak Gopinath, Yuting Ye +3
We present a method for reproducing complex multi-character interactions for physically simulated humanoid characters using deep reinforcement learning. Our method learns control p…
Lagrangian Covector Fluid with Free Surface
Zhiqi Li, Barnabás Börcsök, Duowen Chen +3
This paper introduces a novel Lagrangian fluid solver based on covector flow maps. We aim to address the challenges of establishing a robust flow-map solver for incompressible flui…
Learning to Manipulate Amorphous Materials
Yunbo Zhang, Wenhao Yu, C. Karen Liu +2
We present a method of training character manipulation of amorphous materials such as those often used in cooking. Common examples of amorphous materials include granular materials…
Learning to Collaborate from Simulation for Robot-Assisted Dressing
Alexander Clegg, Zackory Erickson, Patrick Grady +3
We investigated the application of haptic feedback control and deep reinforcement learning (DRL) to robot-assisted dressing. Our method uses DRL to simultaneously train human and r…
Learning Human Behaviors for Robot-Assisted Dressing
Alexander Clegg, Wenhao Yu, Jie Tan +3
We investigate robotic assistants for dressing that can anticipate the motion of the person who is being helped. To this end, we use reinforcement learning to create models of huma…
BodyPressure -- Inferring Body Pose and Contact Pressure from a Depth Image
Henry M. Clever, Patrick Grady, Greg Turk +1
Contact pressure between the human body and its surroundings has important implications. For example, it plays a role in comfort, safety, posture, and health. We present a method t…
Sim-to-Real Transfer for Biped Locomotion
Wenhao Yu, Visak CV Kumar, Greg Turk +1
We present a new approach for transfer of dynamic robot control policies such as biped locomotion from simulation to real hardware. Key to our approach is to perform system identif…
Understanding Expectations for a Robotic Guide Dog for Visually Impaired People
J. Taery Kim, Morgan Byrd, Jack L. Crandell +3
Robotic guide dogs hold significant potential to enhance the autonomy and mobility of blind or visually impaired (BVI) individuals by offering universal assistance over unstructure…
The Fiber Walk: A Model of Tip-Driven Growth with Lateral Expansion
Alexander Bucksch, Greg Turk, Joshua S. Weitz
Tip-driven growth processes underlie the development of many plants. To date, tip-driven growth processes have been modelled as an elongating path or series of segments without tak…
Annotated Hands for Generative Models
Yue Yang, Atith N Gandhi, Greg Turk
Generative models such as GANs and diffusion models have demonstrated impressive image generation capabilities. Despite these successes, these systems are surprisingly poor at crea…
Robot Learning from Randomized Simulations: A Review
Fabio Muratore, Fabio Ramos, Greg Turk +3
The rise of deep learning has caused a paradigm shift in robotics research, favoring methods that require large amounts of data. Unfortunately, it is prohibitively expensive to gen…
Functional Mean Flow in Hilbert Space
Zhiqi Li, Yuchen Sun, Greg Turk +1
We present Functional Mean Flow (FMF) as a one-step generative model defined in infinite-dimensional Hilbert space. FMF extends the one-step Mean Flow framework to functional domai…
An Adjoint Method for Differentiable Fluid Simulation on Flow Maps
Zhiqi Li, Jinjin He, Barnabás Börcsök +6
This paper presents a novel adjoint solver for differentiable fluid simulation based on bidirectional flow maps. Our key observation is that the forward fluid solver and its corres…