no code implementations • 12 Nov 2022 • Namasivayam Kalithasan, Himanshu Singh, Vishal Bindal, Arnav Tuli, Vishwajeet Agrawal, Rahul Jain, Parag Singla, Rohan Paul
Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot.
1 code implementation • 14 May 2022 • Shreya Sharma, Jigyasa Gupta, Shreshth Tuli, Rohan Paul, Mausam
Our goal is to enable a robot to learn how to sequence its actions to perform tasks specified as natural language instructions, given successful demonstrations from a human partner.
1 code implementation • 5 May 2021 • Shreshth Tuli, Rajas Bansal, Rohan Paul, Mausam
We introduce a novel neural model, termed TANGO, for predicting task-specific tool interactions, trained using demonstrations from human teachers instructing a virtual robot.
no code implementations • EACL 2021 • Rohan Paul, Haw-Shiuan Chang, Andrew McCallum
To address the violation of the USchema assumption, we propose multi-facet universal schema that uses a neural model to represent each sentence pattern as multiple facet embeddings and encourage one of these facet embeddings to be close to that of another sentence pattern if they co-occur with the same entity pair.
1 code implementation • 22 Dec 2020 • Kevin A. Thomas, Dominik Krzemiński, Łukasz Kidziński, Rohan Paul, Elka B. Rubin, Eni Halilaj, Marianne S. Black, Akshay Chaudhari, Garry E. Gold, Scott L. Delp
Subregional T2 values and four-year changes were calculated using a musculoskeletal radiologist's segmentations (Reader 1) and the model's segmentations.
1 code implementation • 9 Jun 2020 • Rajas Bansal, Shreshth Tuli, Rohan Paul, Mausam
When compared to a graph neural network baseline, it achieves 14-27% accuracy improvement for predicting known tools from new world scenes, and 44-67% improvement in generalization for novel objects not encountered during training.
Robotics
no code implementations • CONLL 2019 • Subhro Roy, Michael Noseworthy, Rohan Paul, Daehyung Park, Nicholas Roy
We therefore reframe the grounding problem from the perspective of coreference detection and propose a neural network that detects when two expressions are referring to the same object.