D
Dhruv Shah
Researcher at Indian Institute of Technology Bombay
Publications - 28
Citations - 350
Dhruv Shah is an academic researcher from Indian Institute of Technology Bombay. The author has contributed to research in topics: Computer science & Reinforcement learning. The author has an hindex of 5, co-authored 14 publications receiving 99 citations. Previous affiliations of Dhruv Shah include University of California, Berkeley.
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Proceedings Article
The Ingredients of Real World Robotic Reinforcement Learning
Henry Zhu,Justin Yu,Abhishek Gupta,Dhruv Shah,Kristian Hartikainen,Avi Singh,Vikash Kumar,Sergey Levine +7 more
TL;DR: This work discusses the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and proposes a particular instantiation of such a system, and demonstrates the efficacy of this proposed system on dexterous robotic manipulation tasks in simulation and thereal world.
Proceedings ArticleDOI
LM-Nav: Robotic Navigation with Large Pre-Trained Models of Language, Vision, and Action
TL;DR: Each model is pre-trained on its own dataset, and it is shown that the complete system can execute a variety of user-specified instructions in real-world outdoor environments — choosing the correct sequence of landmarks through a combination of language and spatial context — and handle mistakes.
Posted Content
The Ingredients of Real-World Robotic Reinforcement Learning
Henry Zhu,Justin Yu,Abhishek Gupta,Dhruv Shah,Kristian Hartikainen,Avi Singh,Vikash Kumar,Sergey Levine +7 more
TL;DR: In this paper, the authors discuss the elements that are needed for a robotic learning system that can continually and autonomously improve with data collected in the real world, using dexterous manipulation as their case study.
Posted Content
ViNG: Learning Open-World Navigation with Visual Goals
TL;DR: Three key insights, waypoint proposal, graph pruning and negative mining, enable the ViNG method to learn to navigate in real-world environments using only offline data, a setting where prior methods struggle.
Proceedings ArticleDOI
Aerial Manipulation Using Hybrid Force and Position NMPC Applied to Aerial Writing
TL;DR: In this article, a hybrid control model for the combined MAV-arm system which incorporates interaction forces acting on the end effector is presented. But the model does not consider the interaction between the two systems.