328 citations · 1.9k across the 108 of their papers we have counts for
20 papers · 1 filter
Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles
Albert Wilcox, Justin Kerr, Brijen Thananjeyan +5
Robotic Surgical Assistants (RSAs) are commonly used to perform minimally invasive surgeries by expert surgeons. However, long procedures filled with tedious and repetitive tasks s…
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…
AlphaGarden: Learning to Autonomously Tend a Polyculture Garden
Mark Presten, Yahav Avigal, Mark Theis +9
This paper presents AlphaGarden: an autonomous polyculture garden that prunes and irrigates living plants in a 1.5m x 3.0m physical testbed. AlphaGarden uses an overhead camera and…
Planar Robot Casting with Real2Sim2Real Self-Supervised Learning
Vincent Lim, Huang Huang, Lawrence Yunliang Chen +5
This paper introduces the task of {\em Planar Robot Casting (PRC)}: where one planar motion of a robot arm holding one end of a cable causes the other end to slide across the plane…
IPC-GraspSim: Reducing the Sim2Real Gap for Parallel-Jaw Grasping with the Incremental Potential Contact Model
Chung Min Kim, Michael Danielczuk, Isabella Huang +1
Accurately simulating whether an object will be lifted securely or dropped during grasping is a longstanding Sim2Real challenge. Soft compliant jaw tips are almost universally used…
Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects
Jeffrey Ichnowski, Yahav Avigal, Justin Kerr +1
The ability to grasp and manipulate transparent objects is a major challenge for robots. Existing depth cameras have difficulty detecting, localizing, and inferring the geometry of…
GOMP-FIT: Grasp-Optimized Motion Planning for Fast Inertial Transport
Jeffrey Ichnowski, Yahav Avigal, Yi Liu +1
High-speed motions in pick-and-place operations are critical to making robots cost-effective in many automation scenarios, from warehouses and manufacturing to hospitals and homes.…
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…
FogROS: An Adaptive Framework for Automating Fog Robotics Deployment
Kaiyuan, Chen, Yafei Liang +6
As many robot automation applications increasingly rely on multi-core processing or deep-learning models, cloud computing is becoming an attractive and economically viable resource…
Accelerating Quadratic Optimization with Reinforcement Learning
Jeffrey Ichnowski, Paras Jain, Bartolomeo Stellato +6
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapid…
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…
LS3: Latent Space Safe Sets for Long-Horizon Visuomotor Control of Sparse Reward Iterative Tasks
Albert Wilcox, Ashwin Balakrishna, Brijen Thananjeyan +2
Reinforcement learning (RL) has shown impressive success in exploring high-dimensional environments to learn complex tasks, but can often exhibit unsafe behaviors and require exten…
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,…
Policy Gradient Bayesian Robust Optimization for Imitation Learning
Zaynah Javed, Daniel S. Brown, Satvik Sharma +5
The difficulty in specifying rewards for many real-world problems has led to an increased focus on learning rewards from human feedback, such as demonstrations. However, there are…
PAC Best Arm Identification Under a Deadline
Brijen Thananjeyan, Kirthevasan Kandasamy, Ion Stoica +3
We study -PAC best arm identification, where a decision-maker must identify an -optimal arm with probability at least , while minimizing the number of arm pulls (…
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…
A Multi-Chamber Smart Suction Cup for Adaptive Gripping and Haptic Exploration
Tae Myung Huh, Kate Sanders, Michael Danielczuk +4
We present a novel robot end-effector for gripping and haptic exploration. Tactile sensing through suction flow monitoring is applied to a new suction cup design that contains mult…
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,…