Search Results for author: Homanga Bharadhwaj

Found 21 papers, 6 papers with code

INFOrmation Prioritization through EmPOWERment in Visual Model-Based RL

no code implementations ICLR 2022 Homanga Bharadhwaj, Mohammad Babaeizadeh, Dumitru Erhan, Sergey Levine

We propose a modified objective for model-based RL that, in combination with mutual information maximization, allows us to learn representations and dynamics for visual model-based RL without reconstruction in a way that explicitly prioritizes functionally relevant factors.

Model-based Reinforcement Learning

Auditing Robot Learning for Safety and Compliance during Deployment

no code implementations12 Oct 2021 Homanga Bharadhwaj

They are also likely going to fail and be incompliant with human preferences in increasingly subtle ways.

Auditing AI models for Verified Deployment under Semantic Specifications

no code implementations25 Sep 2021 Homanga Bharadhwaj, De-An Huang, Chaowei Xiao, Anima Anandkumar, Animesh Garg

We enable such unit tests through variations in a semantically-interpretable latent space of a generative model.

Face Recognition

Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

no code implementations18 Jan 2021 Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth Sinha, Animesh Garg

Learning from visual data opens the potential to accrue a large range of manipulation behaviors by leveraging human demonstrations without specifying each of them mathematically, but rather through natural task specification.

Keypoint Detection Translation

Offline Policy Optimization with Variance Regularization

no code implementations1 Jan 2021 Riashat Islam, Samarth Sinha, Homanga Bharadhwaj, Samin Yeasar Arnob, Zhuoran Yang, Zhaoran Wang, Animesh Garg, Lihong Li, Doina Precup

Learning policies from fixed offline datasets is a key challenge to scale up reinforcement learning (RL) algorithms towards practical applications.

Continuous Control Offline RL +1

Conservative Safety Critics for Exploration

no code implementations ICLR 2021 Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg

Safe exploration presents a major challenge in reinforcement learning (RL): when active data collection requires deploying partially trained policies, we must ensure that these policies avoid catastrophically unsafe regions, while still enabling trial and error learning.

Safe Exploration

D2RL: Deep Dense Architectures in Reinforcement Learning

4 code implementations19 Oct 2020 Samarth Sinha, Homanga Bharadhwaj, Aravind Srinivas, Animesh Garg

While improvements in deep learning architectures have played a crucial role in improving the state of supervised and unsupervised learning in computer vision and natural language processing, neural network architecture choices for reinforcement learning remain relatively under-explored.


Continual Model-Based Reinforcement Learning with Hypernetworks

1 code implementation25 Sep 2020 Yizhou Huang, Kevin Xie, Homanga Bharadhwaj, Florian Shkurti

Effective planning in model-based reinforcement learning (MBRL) and model-predictive control (MPC) relies on the accuracy of the learned dynamics model.

Continual Learning Model-based Reinforcement Learning +1

A Bayesian Approach with Type-2 Student-tMembership Function for T-S Model Identification

no code implementations2 Sep 2020 Vikas Singh, Homanga Bharadhwaj, Nishchal K. Verma

Clustering techniques have been proved highly suc-cessful for Takagi-Sugeno (T-S) fuzzy model identification.

De-anonymization of authors through arXiv submissions during double-blind review

no code implementations1 Jul 2020 Homanga Bharadhwaj, Dylan Turpin, Animesh Garg, Ashton Anderson

Under two conditions: papers that are released on arXiv before the review phase and papers that are not, we examine the correlation between the reputation of their authors with the review scores and acceptance decisions.

Generalized Adversarially Learned Inference

no code implementations15 Jun 2020 Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar, Piyush Rai

Recent approaches, such as ALI and BiGAN frameworks, develop methods of inference of latent variables in GANs by adversarially training an image generator along with an encoder to match two joint distributions of image and latent vector pairs.

LEAF: Latent Exploration Along the Frontier

no code implementations21 May 2020 Homanga Bharadhwaj, Animesh Garg, Florian Shkurti

We target the challenging problem of policy learning from initial and goal states specified as images, and do not assume any access to the underlying ground-truth states of the robot and the environment.

Model-Predictive Control via Cross-Entropy and Gradient-Based Optimization

1 code implementation L4DC 2020 Homanga Bharadhwaj, Kevin Xie, Florian Shkurti

Recent works in high-dimensional model-predictive control and model-based reinforcement learning with learned dynamics and reward models have resorted to population-based optimization methods, such as the Cross-Entropy Method (CEM), for planning a sequence of actions.

Model-based Reinforcement Learning

Diversity inducing Information Bottleneck in Model Ensembles

1 code implementation10 Mar 2020 Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, Florian Shkurti

Although deep learning models have achieved state-of-the-art performance on a number of vision tasks, generalization over high dimensional multi-modal data, and reliable predictive uncertainty estimation are still active areas of research.

Out-of-Distribution Detection

MANGA: Method Agnostic Neural-policy Generalization and Adaptation

no code implementations19 Nov 2019 Homanga Bharadhwaj, Shoichiro Yamaguchi, Shin-ichi Maeda

Efficiently transferring learned policies to an unknown environment with changes in dynamics configurations in the presence of motor noise is very important for operating robots in the real world, and our work is a novel attempt in that direction.

Imitation Learning

A Data-Efficient Framework for Training and Sim-to-Real Transfer of Navigation Policies

no code implementations11 Oct 2018 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 not require manual tuning or calibration.


Layer-wise Relevance Propagation for Explainable Recommendations

no code implementations17 Jul 2018 Homanga Bharadhwaj

Relationships between the images are identified by the model and layer-wise relevance propagation is used to infer pixel-level details of the images that may have significantly informed the model's choice.

Explanations for Temporal Recommendations

no code implementations17 Jul 2018 Homanga Bharadhwaj, Shruti Joshi

Recommendation systems are an integral part of Artificial Intelligence (AI) and have become increasingly important in the growing age of commercialization in AI.

Recommendation Systems

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