I graduated from the University of Washington (UW), Seattle where I was part of the Robot Learning Lab, directed by Professor Byron Boots. As a Research Assistant, I’ve had the pleasure of working on the DARPA RACER program, conducting research on robot motion planning and controls for high-speed off-road level 4 autonomous vehicles. After graduation, I joined the Bosch Center for Artificial Intelligence, working on the precise navigation problem for Lunar Rover autonomous docking, which is prepared for a series of flight missions, in collaboration with NASA. Post internship, I joined as a visiting researcher at the Carnegie Mellon University Robotics Institute, working with Professor Andrea Bajcsy on safe human-robot interaction for autonomous driving. Currently, I work as a Software Intern in the Planning and Controls team at Kodiak Robotics Inc., an autonomous long-haul trucking company. Broadly, my research interests include robot policy learning, safe human-robot interaction, and planning under uncertainty.

Before joining UW, my undergraduate years at NIT Warangal, India, culminated in my leadership of a multifaceted SAE BAJA off-road racing team, comprising 25 individuals spanning software, simulation, and hardware domains. I invite you to explore my work below.

Resume - Here

Projects - Here

You can contact me at sbaddam@uw.edu or LinkedIn

Work Experience


Research Intern | Robotics Institute - Carnegie Mellon University, Pittsburgh, PA (Sep. 2023 - Jan.2024)

  • Worked on integrating human motion prediction models with reachability to maintain optimally conservative safety monitor (Backward Reachable Tube for active collision-avoidance) in interactive autonomous driving

Robotics AI Intern | NASA PFP - Bosch Research, Pittsburgh, PA (Mar. 2023 - Sep. 2023)

  • Performed experiments on Wi-Fi based sensor modality for lunar CubeRover state estimation in smart docking with the lander
  • Developed optimal control strategies under sensor uncertainties for effective rover positioning and achieved over 98% success rate
  • Incorporated external uncertainty learning with expert demos making EKF quickly adapt to extraterrestrial environments

Research Assistant | Robot Learning Lab, Paul G. Allen School of CSE, UW (Sep. 2022 - Mar. 2023)

  • Carrying out Inverse Reinforcement Learning to autotune the planner cost function parameters that can save 3-5 manual hours
  • Implemented parallel version for cross-track error in MPC-based local planning that is 115X faster than serial computation
  • Developed motion planning debugging tools to understand the planner decisions quantitatively using expert demonstrations

Software and Field Test Engineering Intern | DARPA RACER Program, University of Washington (Jun. 2022 – Sep. 2022)

  • Worked on motion planning and control for Level 4 autonomous high-speed off-road vehicle
  • Contributing algorithms to safely handle vehicle attitude (roll, pitch) in a model predictive local planner and autonomy status
  • Executed a logic to speed manual takeover by approximately 0.5 sec

Graduate Student Researcher | Ultra Precision Controls Laboratory, University of Washington (Dec. 2021 – Jun. 2022)

  • Wrote an obstacle avoidance algorithm that uses a 2D LiDAR point cloud to maintain multi-robot formation during navigation
  • Worked on A* search and CUDA-based Depth Estimation using Stereovision that is 57X faster than the serial version
  • Implemented Feedback Linearization-based trajectory planning for non-linear models using self-made control tuning UI

Research & Development, Graduate Trainee Engineer | Bajaj Auto Ltd., Pune, India (Jan. 2021 – Jul. 2021)

  • Designed and validated a technique to find the precise cabin volume of any closed car with an accuracy of 98%
  • Wrote an optimization algorithm to find the best HVAC design parameters and experimented in real-time on different cars which resulted in validation accuracy > 95%

Captain, Designer, Driver– SAE BAJA off-road racing team | NIT Warangal (Apr. 2017 - Feb. 2020)

  • Managed a 25-member cross-functional (software, design, suspension, steering, brakes) team in building a light off-road vehicle
  • Used IoT and Matlab’s ThingSpeak to develop a graphical visualization to get the running status of the vehicle during race
  • Improved chassis design using grid-independent technique and achieved overall weight under 150kg maintaining min. fos of 1.8