2018-10-23-sharma18a.md

title	abstract	keywords	layout	series	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	publisher	container-title	volume	genre	issued	pdf	extras
Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation	In recent years, we have seen an emergence of data-driven approaches in robotics. However, most existing efforts and datasets are either in simulation or focus on a single task in isolation such as grasping, pushing or poking. In order to make progress and capture the space of manipulation, we would need to collect a large-scale dataset of diverse tasks such as pouring, opening bottles, stacking objects etc. But how does one collect such a dataset? In this paper, we present the largest available robotic-demonstration dataset (MIME) that contains 8260 human-robot demonstrations over 20 different robotic tasks2. These tasks range from the simple task of pushing objects to the difficult task of stacking household objects. Our dataset consists of videos of human demonstrations and kinesthetic trajectories of robot demonstrations. We also propose to use this dataset for the task of mapping 3rd person video features to robot trajectories. Furthermore, we present two different approaches using this dataset and evaluate the predicted robot trajectories against ground-truth trajectories. We hope our dataset inspires research in multiple areas including visual imitation, trajectory prediction and multi-task robotic learning.	Learning from Demonstration, Kinesthetic data	inproceedings	Proceedings of Machine Learning Research	sharma18a	0	Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation	906	915	906-915	906	false	Sharma, Pratyusha and Mohan, Lekha and Pinto, Lerrel and Gupta, Abhinav	given family Pratyusha Sharma given family Lekha Mohan given family Lerrel Pinto given family Abhinav Gupta	2018-10-23		PMLR	Proceedings of The 2nd Conference on Robot Learning	87	inproceedings	date-parts 2018 10 23	http://proceedings.mlr.press/v87/sharma18a/sharma18a.pdf	label link Supplementary video https://youtu.be/M_k5OzQti0U