144 citations · 484 across the 36 of their papers we have counts for
22 papers · 1 filter
Robo-DM: Data Management For Large Robot Datasets
Kaiyuan Chen, Letian Fu, David Huang +9
Recent results suggest that very large datasets of teleoperated robot demonstrations can be used to train transformer-based models that have the potential to generalize to new scen…
Robot Data Curation with Mutual Information Estimators
Joey Hejna, Suvir Mirchandani, Ashwin Balakrishna +7
The performance of imitation learning policies often hinges on the datasets with which they are trained. Consequently, investment in data collection for robotics has grown across b…
GHIL-Glue: Hierarchical Control with Filtered Subgoal Images
Kyle B. Hatch, Ashwin Balakrishna, Oier Mees +8
Image and video generative models that are pre-trained on Internet-scale data can greatly increase the generalization capacity of robot learning systems. These models can function…
Language-Embedded Gaussian Splats (LEGS): Incrementally Building Room-Scale Representations with a Mobile Robot
Justin Yu, Kush Hari, Kishore Srinivas +9
Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Langu…
OpenVLA: An Open-Source Vision-Language-Action Model
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti +15
Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rath…
Learning Switching Criteria for Sim2Real Transfer of Robotic Fabric Manipulation Policies
Satvik Sharma, Ellen Novoseller, Vainavi Viswanath +6
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…
LEGS: Learning Efficient Grasp Sets for Exploratory Grasping
Letian Fu, Michael Danielczuk, Ashwin Balakrishna +4
While deep learning has enabled significant progress in designing general purpose robot grasping systems, there remain objects which still pose challenges for these systems. Recent…
ThriftyDAgger: Budget-Aware Novelty and Risk Gating for Interactive Imitation Learning
Ryan Hoque, Ashwin Balakrishna, Ellen Novoseller +3
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…
Kit-Net: Self-Supervised Learning to Kit Novel 3D Objects into Novel 3D Cavities
Shivin Devgon, Jeffrey Ichnowski, Michael Danielczuk +6
In industrial part kitting, 3D objects are inserted into cavities for transportation or subsequent assembly. Kitting is a critical step as it can decrease downstream processing and…
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan +7
Robot manipulation for untangling 1D deformable structures such as ropes, cables, and wires is challenging due to their infinite dimensional configuration space, complex dynamics,…
Disentangling Dense Multi-Cable Knots
Vainavi Viswanath, Jennifer Grannen, Priya Sundaresan +7
Disentangling two or more cables requires many steps to remove crossings between and within cables. We formalize the problem of disentangling multiple cables and present an algorit…
Orienting Novel 3D Objects Using Self-Supervised Learning of Rotation Transforms
Shivin Devgon, Jeffrey Ichnowski, Ashwin Balakrishna +2
Orienting objects is a critical component in the automation of many packing and assembly tasks. We present an algorithm to orient novel objects given a depth image of the object in…
LazyDAgger: Reducing Context Switching in Interactive Imitation Learning
Ryan Hoque, Ashwin Balakrishna, Carl Putterman +6
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 be…
VisuoSpatial Foresight for Physical Sequential Fabric Manipulation
Ryan Hoque, Daniel Seita, Ashwin Balakrishna +6
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks,…
Exploratory Grasping: Asymptotically Optimal Algorithms for Grasping Challenging Polyhedral Objects
Michael Danielczuk, Ashwin Balakrishna, Daniel S. Brown +2
There has been significant recent work on data-driven algorithms for learning general-purpose grasping policies. However, these policies can consistently fail to grasp challenging…
Accelerating Grasp Exploration by Leveraging Learned Priors
Han Yu Li, Michael Danielczuk, Ashwin Balakrishna +2
The ability of robots to grasp novel objects has industry applications in e-commerce order fulfillment and home service. Data-driven grasping policies have achieved success in lear…
Untangling Dense Knots by Learning Task-Relevant Keypoints
Jennifer Grannen, Priya Sundaresan, Brijen Thananjeyan +7
Untangling ropes, wires, and cables is a challenging task for robots due to the high-dimensional configuration space, visual homogeneity, self-occlusions, and complex dynamics. We…
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
Priya Sundaresan, Jennifer Grannen, Brijen Thananjeyan +5
Robotic manipulation of deformable 1D objects such as ropes, cables, and hoses is challenging due to the lack of high-fidelity analytic models and large configuration spaces. Furth…
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan +10
Robotic fabric manipulation is challenging due to the infinite dimensional configuration space, self-occlusion, and complex dynamics of fabrics. There has been significant prior wo…
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Ryan Hoque, Daniel Seita, Ashwin Balakrishna +6
Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks,…
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
Daniel Seita, Aditya Ganapathi, Ryan Hoque +11
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 complexit…
Mechanical Search: Multi-Step Retrieval of a Target Object Occluded by Clutter
Michael Danielczuk, Andrey Kurenkov, Ashwin Balakrishna +6
When operating in unstructured environments such as warehouses, homes, and retail centers, robots are frequently required to interactively search for and retrieve specific objects…