no code implementations • 29 May 2024 • Namasivayam Kalithasan, Arnav Tuli, Vishal Bindal, Himanshu Gaurav Singh, Parag Singla, Rohan Paul
Automatically detecting and recovering from failures is an important but challenging problem for autonomous robots.
no code implementations • 11 Apr 2024 • Namasivayam Kalithasan, Sachit Sachdeva, Himanshu Gaurav Singh, Vishal Bindal, Arnav Tuli, Gurarmaan Singh Panjeta, Divyanshu Aggarwal, Rohan Paul, Parag Singla
Additionally, the approach should generalize inductively to novel structures of different sizes or complex structures expressed as a hierarchical composition of previously learned concepts.
no code implementations • 2 Apr 2024 • Saptarshi Dasgupta, Akshat Gupta, Shreshth Tuli, Rohan Paul
This paper presents an approach that enables a robot to rapidly learn the complete 3D model of a given object for manipulation in unfamiliar orientations.
1 code implementation • 24 Feb 2024 • Harshil Vagadia, Mudit Chopra, Abhinav Barnawal, Tamajit Banerjee, Shreshth Tuli, Souvik Chakraborty, Rohan Paul
PhyPlan leverages PINNs to simulate and predict outcomes of actions in a fast and accurate manner and uses MCTS for planning.
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.