Homanga Bharadhwaj

hbharadh at cs dot cmu dot edu

I am a PhD student in the Robotics Institute, School of Computer Science, Carnegie Mellon University advised by Abhinav Gupta and Shubham Tulsiani, and a visiting researcher at Meta AI in the Embodied AI team. I am engaged in the quest for understanding intelligence by trying to simulate it. Although this quest has kept me fully occupied for the past three years, I also paint, and have a Bachelor of Arts degree in Fine Arts. Some of my paintings can be found here.

I am fortunate to have been advised by fantastic researchers over the course of my studies. In reverse chronological order, I have been a student researcher at Google Brain, a research intern at Nvidia Research , a graduate student in the CS Department, University of Toronto and a student researcher at Vector Institute, a research intern at Preferred Networks Inc. Tokyo and an undergrad in the CSE Department, IIT Kanpur. While being an undergrad, I spent time in Mila, Montreal and the National University of Singapore during semester breaks.

Email  /  Github  /  GScholar  /  Twitter  /  Name  /  Art  /  Poem

• [2022 - Present] Visiting Researcher, Meta AI (Pittsburgh)
• [2021 - Present] PhD in Computer Science and Robotics, Carnegie Mellon University (Pittsburgh)
• [Summer, 2021] Student Researcher, Google Brain (remote, Toronto / Mountain View)
• [Spring, 2021] Research Intern, Nvidia Research (remote, Toronto / Santa Clara)
• [2019 - 2021 ] Msc in Computer Science, University of Toronto / Vector Institute (Toronto)
• [Summer, 2019] Research Intern, Preferred Networks, Inc. (Tokyo)
• [Summer, 2018] Research Intern, Mila (Montreal)
• [Winter, 2017] Research Intern, National University of Singapore (Singapore)
• [2015 - 2019 ] BTech in Computer Science and Engineering, IIT Kanpur (Kanpur)

• [2024] Best Paper in Robot Manipulation Finalist at ICRA 2024
• [2023] Outstanding Presentation Award at the Robot Learning Workshop, NeurIPS 2023
• [2023] Our sample-efficient universal manipulation research was covered by TechCrunch, ACM, IEEE
• [2021] Our research on safe exploration for robotics was convered by VentureBeat

If you have any questions / want to collaborate, feel free to send me an email! I am always excited to learn more by talking with people.

I'm interested in developing capable robot manipulation systems that learn from raw sensory inputs efficiently, are compliant with human preferences, and can generalize to diverse unseen scenarios. To this end, I have worked on learning goal-conditioned manipulation from human videos, exploration for robot learning, and improving efficiency through semantic augmentations and novel policy architectures. I have eclectic research interests, and have also worked on robustness in machine learning, and improving sample efficiency, scalability, and representations in reinforcement learning.

My research has received a Best Paper in Robot Manipulation Finalist at ICRA, the Outstanding Presentation award at NeurIPS Robot Learning Workshop, and has been covered by several media outlets like TechCrunch, ACM, IEEE, and VentureBeat among others.

We can develop a single robot manipulation agent capable of 12 non trivial skills across 100s of scenes, through semantic augmentations for multiplying data, and action chunking transformers for fitting the multi-modal data distribution. We show generalization at different levels of variations including completely new scenarios with an order of magnitude less data compared to prior works.

Learning interaction plans from diverse passive human videos on the web, followed by translation to robotic embodiments can help develop a single goal-conditioned policy that scales to over 100 diverse tasks in unseen scenarios, including real kitchens and offices.

Learning to predict plausible hand motion trajectories from passive human videos on the web, followed by transformation of the predictions to a robot's frame of reference enables zero-shot coarse-manipulation with real-world objects.

Through effective augmentations enabled by recent advances in generative modeling, we can develop a framework for learning robust manipulation policies capable of solving multiple tasks in diverse real-world scenes.

We can enable goal-directed robot exploration in the real world by learning an affordance model to predict plausible future frames given an initial image from passive human interaction videos, in combination with self-behavior cloning for policy learning.

We can learn a latent-space model, and a policy for RL jointly through a single objective, by deriving a lower-bound to the overall RL objective

Auditing robot learning algorithms for safety and compliance is important to ensure human compatibility. This position paper argues for increased synergy between the robot learning and AI Alignment communities and describes a preliminary audit framework based on human-in-the-loop learning.

Empowerment along with mutual information maximization helps learn functionally relevant factors in visual model-based RL, especially in environments with complex visual distractors.

Training a critic to make conservative safety estimates by over-estimating how unsafe a particular state is, can help significantly minimize the number of catastrophic failures in constrained RL

Combining online planning of high level skills with an amortized low level policy can improve sample-efficiency of model-based RL for solving complex tasks, and transferring across tasks with similar dynamics.

Updating the top action sequences identified by CEM through a few gradient steps helps improve sample efficiency and performance of planning in Model-based RL

Task-conditioned hypernetworks can be used to continually adapt to varying environment dynamics, with a limited replay buffer in lifelong robot learning

Keeping track of the currently reachable frontier of states, and executing a deterministic policy to reach the frontier followed by a stochastic policy beyond, can help facilitate principled exploration in RL

Introducing skip connections in the policy and Q function neural networks can improve sample efficiency of reinforcement learning algorithms across different continuous control environments

Training dynamics conditioned policies on dynamics randomized environments and estimating dynamics parameters from off-policy data can help achieve zero-shot adaptation in an unseen test environment

Adversarial domain adaptation can be used for training a gradient descent based planner in simulation and transferrring the learned model to a real navigation environment.

Explicitly maximizing diversity in ensembles through adversarial learning helps improve generalization, transfer, and uncertainty estimation

Appropriately augmenting training with multiple complimentary tasks can improve generalization in Deep Metric Learning.

Adversarially learned inference can be generalized to incorporate multiple layers of feedback through reconstructions, self-supervision, and learned knowledge.

A Generative Framework for Zero Shot Learning with Adversarial Domain Adaptation
Varun Khare, Divyat Mahajan, Homanga Bharadhwaj, VK Verma, Piyush Rai
WACV, 2020 paper code  

Adversarial Domain Adaptation appropriately incorporated in a Generative Zero Shot Learning model can help minimize domain shift and significantly enhance generalization on the unseen test classes

Auditing deep-learning models for human-interpretable specifications, prior to deployment is important in preventing unintended consequences. These specifications can be obtained by considering variations in an interpretable latent space of a generative model.

Recurrent Neural Network based Generative Adversarial Networks can learn to effectively model the latent preference trends of users in time-series recommendation.

In an analysis of ICLR 2020 and 2019 papers, we find positive correlation between releasing preprints on arXiv and acceptance rates of papers by well-known authors. For well known authors, acceptance rates for papers with arxiv preprint are higher than those without preprints released during review.

Passive website recommendations embedded in the new tab displays of browsers (that recommend based on frecency) inhibit peoples' propensity to visit diverse information sources on the internet

Phase matching in Spontaneous Parametric Down Conversion
Suman Karan, Shaurya Aarav, Homanga Bharadhwaj, Lavanya Taneja, Girish Kulkarni, Anand K Jha
Journal of Optics (Accepted 2020)

Spontaneous Parametric Down Conversion is used to generate entangled photon pairs. SPDC can be studies through the lens of Wave Optics by making some simplifying theoretical assumptions without compromising on empirical results. Also, a simulation for SPDC can be conveniently designed, given the assumptions.

• Franka Panda control environments through ROS and Gym
• L4DC, 2020 paper on Cross-Entropy and Gradient-based optimization
• Universal adversarial attack on Dilated Residual Networks
• Code for the web extension used in my paper titled "New tab page recommendations cause a strong suppression of exploratory web browsing behaviors"
• A stemmer for the Bengali language

Teaching Assitant (TA),
Introduction to Mobile Robotics (CSC477), Fall 2020

Teaching Assitant (TA),
Algorithmic Intelligence in Robotics (CSC375), Fall 2020

Teaching Assitant (TA),
Computational Cognitive Science (CS786), Winter 2019

Course Project Mentor,
Topics in Probabilistic Modeling and Inference (CS698), Winter 2019

Course Project Mentor,
Introduction to Machine Learning (CS771), Autumn 2018

Student Volunteer at ACM RecSys 2018, Vancouver, BC

Student Volunteer at ACM SIGIR 2018, Ann Arbor, Michigan

Reviewer for Computers in Human Behavior , ICRA 2019 , ICRA 2020, IROS 2020, CoRL 2020, ACM ToCHI

I love his website design.

Miscellaneous stuffs - Co-authors  /  Fav books  /  My paintings  /  Travel  /  Love  /  Name  /