Sai Aneesh Suryadevara

Hello! I'm Aneesh, a Machine Learning Engineer at Nuro, where I work on the behavior team for autonomous driving. I graduated from UC San Diego with an MS in ECE, specializing in Intelligent Systems, Robotics, and Control (ISRC). I completed my undergraduate from the Indian Institute of Technology Bombay, receiving an Honours degree in Mechanical Engineering and a Minor degree in Artificial Intelligence and Data Science.

My work spans areas like Reinforcement Learning and Robotics, and I've also explored 3D Computer Vision, NeRF, and Language Alignment for open-world mobile manipulation. During my time at UC San Diego, I was a researcher at the Contextual Robotics Institute, working with Prof. Xiaolong Wang. For my undergraduate thesis, I worked on Deep Reinforcement Learning for the Control of Soft Continuum robots advised by Prof. Abhishek Gupta and Prof. Shivaram Kalyanakrishnan.

I love diving deep into fundamentals and am always curious about the application of these fields in the real world—right now this happens to be in autonomous driving!

Research Interests: Robot Learning, 3D Computer Vision, Embodied AI

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ML Engineer

Apr '25 - Present

Autonomy Intern

Jul '24 - Dec '24

M.S. in ECE

Sep '23 - Mar '25

B.Tech in Mech.

Jul '19 - May '23

Long horizon loco-manipulation with LLM planner and wholebody imitation learning skill sets

Bachelor's Thesis: Control of Continuum Robots Using Deep Reinforcement Learning

Implemented a model-free reinforcement learning approach to train control policies for trajectory tracking of a soft continuum robot arm. Developed a custom OpenAI Gym environment and integrated it with VEGA FEM C++ middleware library and ROS to simulate more realistic dynamics.

Decentralized Multi-Agent Patrolling using Q-Learning

In this work, we wish to find an optimal patrolling strategy in a multi-agent setting with the constraint of minimum information sharing. Developed patrolling techniques and analyzed their performance using ROS, TraCI and SUMO simulator

Flipkart National Competition: Autonomous package delivery bots

Developed a system of mobile bots capable of autonomous package sorting using ROS and OpenCV framework, tracking each bot’s pose through ArUco markers.

e -Yantra Robotics Competition: Autonomous Delivery Drone System

Simulated a working prototype of an autonomous drone in Gazebo for package delivery during Covid-19. Designed attitude and position (PID) controllers in ROS and implemented A* algorithm for path planning and obstacle avoidance.

Image-to-Image Translation using CycleGAN and DiscoGAN

Implemented and compared the image generation capabilities of GANs and VAEs in PyTorch. Also investigated the performance of DiscoGAN and CycleGAN architectures for style transfer between Pansy and Tigerlily.

Statistical Solvers using Graph Neural Networks

Worked on a paper implementation to understand Deep Graph Neural Networks as a new class of solvers for permutation-invariant optimization problems that can be trained without a training set of sample solutions

Self Driving Car, University of Toronto

Built an environment perception stack, using a Semantic Segmentation neural network for lane estimation and object detection to alert the car about the position and category of obstacles

credits to       original template