Ryan Hoque
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View article: EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video
EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video Open
Imitation learning for manipulation has a well-known data scarcity problem. Unlike natural language and 2D computer vision, there is no Internet-scale corpus of data for dexterous manipulation. One appealing option is egocentric human vide…
View article: ARMADA: Augmented Reality for Robot Manipulation and Robot-Free Data Acquisition
ARMADA: Augmented Reality for Robot Manipulation and Robot-Free Data Acquisition Open
Teleoperation for robot imitation learning is bottlenecked by hardware availability. Can high-quality robot data be collected without a physical robot? We present a system for augmenting Apple Vision Pro with real-time virtual robot feedba…
View article: IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning
IntervenGen: Interventional Data Generation for Robust and Data-Efficient Robot Imitation Learning Open
Imitation learning is a promising paradigm for training robot control policies, but these policies can suffer from distribution shift, where the conditions at evaluation time differ from those in the training data. A popular approach for i…
View article: FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity
FogROS2-SGC: A ROS2 Cloud Robotics Platform for Secure Global Connectivity Open
The Robot Operating System (ROS2) is the most widely used software platform for building robotics applications. FogROS2 extends ROS2 to allow robots to access cloud computing on demand. However, ROS2 and FogROS2 assume that all robots are …
View article: IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors
IIFL: Implicit Interactive Fleet Learning from Heterogeneous Human Supervisors Open
Imitation learning has been applied to a range of robotic tasks, but can struggle when robots encounter edge cases that are not represented in the training data (i.e., distribution shift). Interactive fleet learning (IFL) mitigates distrib…
View article: Semantic Mechanical Search with Large Vision and Language Models
Semantic Mechanical Search with Large Vision and Language Models Open
Moving objects to find a fully-occluded target object, known as mechanical search, is a challenging problem in robotics. As objects are often organized semantically, we conjecture that semantic information about object relationships can fa…
View article: Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features
Self-Supervised Visuo-Tactile Pretraining to Locate and Follow Garment Features Open
Humans make extensive use of vision and touch as complementary senses, with vision providing global information about the scene and touch measuring local information during manipulation without suffering from occlusions. While prior work d…
View article: Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies
Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies Open
Simulation-to-reality transfer has emerged as a popular and highly successful method to train robotic control policies for a wide variety of tasks. However, it is often challenging to determine when policies trained in simulation are ready…
View article: Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision
Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision Open
Commercial and industrial deployments of robot fleets at Amazon, Nimble, Plus One, Waymo, and Zoox query remote human teleoperators when robots are at risk or unable to make task progress. With continual learning, interventions from the re…
View article: Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research Open
Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware. Using Reach, a cloud robotics platform that enables low-latency remote execution …
View article: ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning Open
Effective robot learning often requires online human feedback and interventions that can cost significant human time, giving rise to the central challenge in interactive imitation learning: is it possible to control the timing and length o…
View article: LazyDAgger: Reducing Context Switching in Interactive Imitation Learning
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning Open
Corrective interventions while a robot is learning to automate a task provide an intuitive method for a human supervisor to assist the robot and convey information about desired behavior. However, these interventions can impose significant…
View article: VisuoSpatial Foresight for Physical Sequential Fabric Manipulation
VisuoSpatial Foresight for Physical Sequential Fabric Manipulation Open
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but rel…
View article: MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects
MMGSD: Multi-Modal Gaussian Shape Descriptors for Correspondence Matching in 1D and 2D Deformable Objects Open
We explore learning pixelwise correspondences between images of deformable objects in different configurations. Traditional correspondence matching approaches such as SIFT, SURF, and ORB can fail to provide sufficient contextual informatio…
View article: VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation Open
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery.Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but rela…
View article: Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics Open
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics. There has been significant prior work on learning policies for specific deformable manipulatio…
View article: VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation Open
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but rel…
View article: Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor Open
Sequential pulling policies to flatten and smooth fabrics have applications from surgery to manufacturing to home tasks such as bed making and folding clothes. Due to the complexity of fabric states and dynamics, we apply deep imitation le…