My Journey into Robotics
I'm a Robotics Software Engineer passionate about building systems that move, think, and actually function in the real world.
My academic path began with a degree in Aerospace Engineering from IIT Kharagpur, India, where I first got pulled into the world of control systems. That curiosity gradually snowballed into a broader obsession with robotics, leading me to pursue a Master's in Robotic Systems Development at Carnegie Mellon University.
At CMU, I built a strong foundation across the robotics stack, working with hardware, perception, localization, planning, control, and systems integration. Along the way, I discovered a deep interest for motion planning and reinforcement learning, areas where I continue to build expertise.
I currently work at HEBI Robotics, where we build modular robots, tools, and mobile platforms designed to bring real-world autonomy to life. Our long-term vision is to make robots as intuitive and modular as LEGO blocks — enabling anyone to build, adapt, and deploy our robots to solve their problems.
Robots, Code, and Everything Between
At HEBI Robotics, I've worked on a range of projects from designing a monocular visual odometry system for feature-sparse environments, to developing a modular, multi-arm mobile robot with reconfigurable tooling for soil characteristic analysis.
I also built a physics-based simulation environment using MuJoCo, emulating HEBI modules as virtual actuators on the network. This allows users to run the exact same control code in both simulation and on real hardware, making it easier to test, iterate, and deploy algorithms safely.
Beyond that, I lead the development of our open-source HEBI ROS 2 APIs, making sure our tools are accessible, robust, and developer-friendly.
Before HEBI, during my time at CMU, I worked on several exciting projects including a lunar-terrain excavation robot as part of my year-long capstone. I also built an autonomy stack for Roboracer (formerly F1Tenth — small racecars, serious autonomy problems) and researched learning-based motion planning for off-road vehicles using self-supervised techniques.
Across all these projects, I've always been drawn to one thing: building autonomy stacks that leaves the lab and survive (ideally, thrive) in the messy, unpredictable chaos of the real world. Whether it's data-driven autonomy stacks, classical motion planning, or robot arm APIs, I enjoy building software that interacts meaningfully with the physical world.
If you want to dig deeper into the technical side of my work, check out the Projects section.
Outside the Lab
Basketball has always been a huge part of my life, and I'm a lifelong Kobe fan (yes, he lives rent-free on my lock screen). His work ethic and the way he approached the game continue to influence how I think about focus, calmness, and discipline, both on and off the court.
I grew up playing the piano and still sit down at the keys whenever I need to reset. Primavera by Ludovico Einaudi is my go-to song — mostly because it's beautiful and also since it's the only one I still remember how to play ( ´･･)ﾉ(._.`)
During the pandemic, I picked up chess and have been stuck in the 3-minute blitz loop ever since. It's fast, competitive, and a great way to pretend I'm sharpening my brain.
What I'm Into Lately.
Last updated: Jun 11, 2025
Lately, I've been deep in work and haven't had much time for personal projects. I know it's not ideal, but hey, life isn't always apples and oranges. ¯\_(ツ)_/¯
- At work, I've been focused on trajectory generation for our robot arms. Our current API generates minimum jerk trajectories from a set of time-stamped waypoints, and I'm exploring ways to eliminate the need for timestamps and instead generate optimal trajectories based on constraints like joint and velocity limits. The problem is surprisingly complex, and I'm trying to strike a balance between performance and usability. I've done a ton of research, reading, coding, and testing various approaches, but in the end, it all comes down to the optimizer — and that can be slow to run. For context, our current API takes less than 10ms to generate a trajectory for a 7-DOF arm with 100 waypoints, which is incredibly fast. So, I've also been exploring heuristic-based time estimation methods as a potentially faster (though slightly less accurate) alternative. The goal is to make trajectory generation fast, flexible, and user-friendly. More updates soon, once I come up for air.
- I recently competed in the Roboracer Sim Racing competition at ICRA 2025. I got through the qualification round using the Follow-the-Gap method (check it out here), but couldn't participate in the finals due to work commitments. Still, it was a great experience, and I'm planning to jump back in for the next tournament. I'll also be revisiting classical planning approaches like Model Predictive Control (MPC), which I first explored during the F1Tenth course at CMU, and tuning it for the sim.
- Outside of work, I've been doing a lot of traveling. First to Atlanta for ICRA, then to the Bay Area to catch up with friends, and finally to Yosemite, where I hiked the Mist Trail up to Vernal Falls. It was absolutely worth the sore legs "( - ⌓ -)
- I've also started learning more about investing, now that I actually earn money and feel vaguely responsible for it. I'm hoping to dive into a few good books now that I've got some context and a few awkward mistakes under my belt (ᵕ—ᴗ—)
Life's been a bit hectic with all the travel. I'm looking forward to getting home, settling into a rhythm, embracing the grind again, "maybe" even a little routine xD
Let's Connect
If you're into robotics, autonomy stacks, embodied AI — or want to argue why Max Verstappen is the best driver out there, emotionally process the news of Dallas Mavericks trading Luka Doncic, or just chat about life — feel free to hit me up on any of the platforms below.
