Aseem Saxena

I am currently studying Robotics at Oregon State University.

Until very recently, I worked as a Machine Learning Engineer at Panasonic, Singapore.

Before that, I was working as a Researcher at M2AP Lab, School of Computing, NUS, Singapore under the guidance of Prof. David Hsu.

I did a Dual Major in Biology and Electrical And Electronics Engineering at BITS Pilani, my education was funded by the KVPY Fellowship. I spent time on my thesis at Robotics Research Lab, IIIT Hyderabad, where I was advised by Prof Madhava Krishna.

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Research Interests

I am passionate about Robotics, Computer Vision, Machine Learning, Statistics and Optimization. I also like to read about Protein Structure Prediction and Cancer Genomics.

Other Interests

I play music - Youtube, Soundcloud. I click photographs sometimes - Photos. I run, swim and cycle to stay fit.


LeTS-Drive: Driving in a Crowd by Learning from Tree Search
Panpan Cai, Yuanfu Luo, Aseem Saxena, David Hsu, Wee Sun Lee
RSS (Robotics Science and Systems), 2019 (Accepted)

Autonomous driving in a crowded environment, e.g., a busy traffic intersection, is an unsolved challenge for robotics. We propose LeTS-Drive, which integrates online POMDP planning and deep learning.

Exploring Convolutional Networks for End-to-End Visual Servoing
Aseem Saxena, Harit Pandya, Gourav Kumar, K. Madhava Krishna
IEEE ICRA (International Conference on Robotics and Automation), 2017 (Accepted)
video code

We present an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available apriori. This is achieved by training a convolutional neural network over color images with synchronised camera poses.


Visualizing QMDPNet
Aseem Saxena

I created a full fledged GUI Visualizer using Python Tkinter Library to understand the QMDPnet algorithm. I visualize various components of a POMDP such as reward map, belief and value function to get an intuition on how the algorithm works.


Guess from Far, Recognize when Near: Searching the Floor for Small Objects
M Siva Karthik, Sudhanshu Mittal, K. Madhava Krishna, ICVGIP 2014

Object recognition is achieved using 3-D Point Cloud data from Kinect sensors and constructing a Bag of Words Model on it. It is trained using a Support Vector Machine Classifier. Object Detection is achieved using segmentation of 2-D images by Markov Random Fields. The implementation is done on a Turtlebot with a Kinect Sensor mounted on top of it.

Deep Learning for Table Interest Point Detection
Aseem Saxena

I attempt to find interest points or corner points of tables in a scene using cues from semantic segmentation and vanishing lines. Availabilty of semantic information such as interest points can help mobile robots navigate in a better way.

Automating GrabCut for Multilabel Image Segmentation
Aseem Saxena

Performing Image Segmentation for 3 labels without user guidance by learning a GMM for each label and performing alpha expansion algorithm using MRF2.2 Library.

Inspired by this