activity
20182020
most citedActive Domain Randomization

33 citations · 51 across the 6 of their papers we have counts for

collaborators

7 papers

cs.LG2020★ 4 cited

A User's Guide to Calibrating Robotics Simulators

Bhairav Mehta, Ankur Handa, Dieter Fox +1

Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world…

cs.RO2020★ 1 cited

Integrated Benchmarking and Design for Reproducible and Accessible Evaluation of Robotic Agents

Jacopo Tani, Andrea F. Daniele, Gianmarco Bernasconi +10

As robotics matures and increases in complexity, it is more necessary than ever that robot autonomy research be reproducible. Compared to other sciences, there are specific challen…

cs.LG2020★ 8 cited

Curriculum in Gradient-Based Meta-Reinforcement Learning

Bhairav Mehta, Tristan Deleu, Sharath Chandra Raparthy +2

Gradient-based meta-learners such as Model-Agnostic Meta-Learning (MAML) have shown strong few-shot performance in supervised and reinforcement learning settings. However, specific…

cs.LG2020★ 5 cited

Generating Automatic Curricula via Self-Supervised Active Domain Randomization

Sharath Chandra Raparthy, Bhairav Mehta, Florian Golemo +1

Goal-directed Reinforcement Learning (RL) traditionally considers an agent interacting with an environment, prescribing a real-valued reward to an agent proportional to the complet…

cs.LG2019★ 33 cited

Active Domain Randomization

Bhairav Mehta, Manfred Diaz, Florian Golemo +2

Domain randomization is a popular technique for improving domain transfer, often used in a zero-shot setting when the target domain is unknown or cannot easily be used for training…

cs.RO2019

The AI Driving Olympics at NeurIPS 2018

Julian Zilly, Jacopo Tani, Breandan Considine +14

Despite recent breakthroughs, the ability of deep learning and reinforcement learning to outperform traditional approaches to control physically embodied robotic agents remains lar…

cs.CY2018

A Scalable, Flexible Augmentation of the Student Education Process

Bhairav Mehta, Adithya Ramanathan

We present a novel intelligent tutoring system which builds upon well-established hypotheses in educational psychology and incorporates them inside of a scalable software architect…