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Homanga Bharadhwaj

Alumni

Publications

Offline Policy Optimization in RL with Variance Regularizaton
Riashat Islam
Samarth Sinha
Homanga Bharadhwaj
Samin Yeasar Arnob
Zhuoran Yang
Animesh Garg
Zhaoran Wang
Lihong Li
Doina Precup
2022-12-29
ArXiv (prépublication)
doi.org
arxiv.org
DIBS: Diversity inducing Information Bottleneck in Model Ensembles
Samarth Sinha
Homanga Bharadhwaj
Anirudh Goyal
Hugo Larochelle
Animesh Garg
Florian Shkurti
2021-05-18
Proceedings of the AAAI Conference on Artificial Intelligence (publié)
doi.org
arxiv.org
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Timo Milbich
Karsten Roth
Homanga Bharadhwaj
Samarth Sinha
Yoshua Bengio
Björn Ommer
Joseph Paul Cohen
2020-11-07
Computer Vision – ECCV 2020 (publié)
doi.org
arxiv.org
A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies
Homanga Bharadhwaj
Zihan Wang
Yoshua Bengio
Liam Paull
Learning effective visuomotor policies for robots purely from data is challenging, but also appealing since a learning-based system should n… (voir plus)ot require manual tuning or calibration. In the case of a robot operating in a real environment the training process can be costly, time-consuming, and even dangerous since failures are common at the start of training. For this reason, it is desirable to be able to leverage simulation and off-policy data to the extent possible to train the robot. In this work, we introduce a robust framework that plans in simulation and transfers well to the real environment. Our model incorporates a gradient-descent based planning module, which, given the initial image and goal image, encodes the images to a lower dimensional latent state and plans a trajectory to reach the goal. The model, consisting of the encoder and planner modules, is first trained through a meta-learning strategy in simulation. We subsequently perform adversarial domain transfer on the encoder by using a bank of unlabelled but random images from the simulation and real environments to enable the encoder to map images from the real and simulated environments to a similarly distributed latent representation. By fine tuning the entire model (encoder + planner) with only a few real world expert demonstrations, we show successful planning performances in different navigation tasks.
2019-05-20
2019 International Conference on Robotics and Automation (ICRA) (publié)
doi.org
arxiv.org