262 citations · 369 across the 12 of their papers we have counts for
13 papers
Large Language Models Encode Clinical Knowledge
Karan Singhal, Shekoofeh Azizi, Tao Tu +27
Large language models (LLMs) have demonstrated impressive capabilities in natural language understanding and generation, but the quality bar for medical and clinical applications i…
RepsNet: Combining Vision with Language for Automated Medical Reports
Ajay Kumar Tanwani, Joelle Barral, Daniel Freedman
Writing reports by analyzing medical images is error-prone for inexperienced practitioners and time consuming for experienced ones. In this work, we present RepsNet that adapts pre…
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,…
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer
Ajay Kumar Tanwani
Generating large-scale synthetic data in simulation is a feasible alternative to collecting/labelling real data for training vision-based deep learning models, albeit the modelling…
Non-Markov Policies to Reduce Sequential Failures in Robot Bin Picking
Kate Sanders, Michael Danielczuk, Jeffrey Mahler +2
A new generation of automated bin picking systems using deep learning is evolving to support increasing demand for e-commerce. To accommodate a wide variety of products, many autom…
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos
Ajay Kumar Tanwani, Pierre Sermanet, Andy Yan +3
Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we lear…
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…
A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
Ajay Kumar Tanwani, Nitesh Mor, John Kubiatowicz +2
The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and…
Generalizing Robot Imitation Learning with Invariant Hidden Semi-Markov Models
Ajay Kumar Tanwani, Jonathan Lee, Brijen Thananjeyan +5
Generalizing manipulation skills to new situations requires extracting invariant patterns from demonstrations. For example, the robot needs to understand the demonstrations at a hi…
Dynamic Regret Convergence Analysis and an Adaptive Regularization Algorithm for On-Policy Robot Imitation Learning
Jonathan N. Lee, Michael Laskey, Ajay Kumar Tanwani +2
On-policy imitation learning algorithms such as DAgger evolve a robot control policy by executing it, measuring performance (loss), obtaining corrective feedback from a supervisor,…
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
Daniel Seita, Nawid Jamali, Michael Laskey +6
A fundamental challenge in manipulating fabric for clothes folding and textiles manufacturing is computing "pick points" to effectively modify the state of an uncertain manifold. W…
Small Variance Asymptotics for Non-Parametric Online Robot Learning
Ajay Kumar Tanwani, Sylvain Calinon
Small variance asymptotics is emerging as a useful technique for inference in large scale Bayesian non-parametric mixture models. This paper analyses the online learning of robot m…