Suraj Nair

I am a PhD candidate in Computer Science at Stanford University, where I work at the intersection of machine learning, robotics, and computer vision. My research focuses on enabling general-purpose robotic agents through large-scale data collection and reinforcement learning. I am co-advised by Professors Chelsea Finn and Silvio Savarese, and am funded by the National Science Foundation Graduate Fellowship.

I completed my Bachelors in Computer Science at the California Institute of Technology (Caltech), where I worked with Yisong Yue on multi-agent reinforcement learning. I have also spent time at Google Brain and GE.

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The goal of my research is to enable robotic agents which can generalize effectively across tasks, objects, and environments by learning from large datasets. To that end my research focuses on methods for scalably collecting robotic data as well as offline reinforcement learning algorithms which can learn to solve many tasks with limited supervision.

We propose a technique for learning multi-task reward functions from a small amount of robot data and large amounts of in-the-wild human videos. By leveraging diverse human data, the learned reward function is able to generalize to new environments and tasks.

We propose a technique for video prediction which trains a hierarchy of action-conditioned VAEs in a greedy fashion, enabling efficient training of large video prediction models.

We propose a method for offline model-based RL which learns a video prediction model and a Q function based distance metric, and uses them to accomplish visually specified goals.

We propose a framework for leveraging weak human superivision to enable better robotic exploration. Using just a few minutes of human supervision, the robot collects high quality data while unsupervised, providing better data for downstream offline RL.

An algorithm for safe reinforcement learning which utilizes a set of offline data to learn about constraints before policy learning and a pair of policies which seperate the often conflicting objectives of task directed exploration and constraint satisfaction to learn contact rich and visuomotor control tasks.

We explore learning visual dynamics models which are conditioned on goals, and learn to model only goal relevant quantities.

We study how we can learn long horizon vision-based tasks in self-supervised settings. Our approach, hierarchical visual foresight, can optimize for a sequence of subgoals that break down the task into easy to complete subsegments.

We propose a technique that uses time-reversal to learn goals and provide a high level plan to reach them. In particular, our approach explores outward from a set of goal states, "unsolving" a task, which then enables solving the task from new initializations at test time.

We explore how to effectively predict causal graphs from a small set of visual observations, and how to encorporate the learned graphs into downstream goal conditioned policy learning.

We collect a dataset of robotic experience across 4 institutions and 7 robots, and demonstrate that robot learning algorithms leveraging this data can adapt to new environments faster than training from scratch.

NTG learns to produce a task graph from a single video demonstration of an unseen task, and leverages it for one-shot imitation learning.

Neural Task Programming (NTP) is a meta-learning framework that learns to generate robot-executable neural programs from task demonstration video.

Efficient prediction of real local earthquake signals from impulsive signals for earthquake early warning (EEW) alerts.

Reconstruct 3D voxel representations of a scene with object labels from RGB images captured from a drone, and use it for exporatory motion planning