activity
20182020
most citedActive Domain Randomization

33 citations · 46 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG20204 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.RO20201 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.LG20208 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

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.LG201933 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…