• • Built complete simulation scenes using Omniverse USD, URDF, and modular assets to replicate complex robot workcells and industrial layouts, reducing environment setup time by 25%.
• • Led ROS2-based robotics integration across multi-robot fleets, developing and validating launch files, lifecycle nodes, and autonomy behaviors on Linux with Docker, reducing system bring-up time by 25%.
• • Built AI-ready simulation data pipelines using NVIDIA Isaac Sim and Omniverse to generate high-fidelity synthetic datasets for perception model validation.
• • Tuned PhysX parameters (friction, damping, solver iterations), achieving 18% improvement in physics simulation consistency.
• • Configured RTX-based LiDAR and camera sensors with ray tracing and material mapping, improving scene realism by 22% and enhancing perception validation.
• • Validated sim-to-real transfer by aligning simulated robot behavior, sensor outputs, and environmental conditions with real-world performance.
• • Validated AMR navigation behaviors in simulation prior to deployment, reducing on-site configuration errors and accelerating robot bring-up timelines.

• • Designed and developed Physical AI Systems robotic systems from scratch, including URDF modeling and integration with perception and control pipelines for simulation and validation.
• • Generated large-scale synthetic datasets using Replicator with domain randomization and lighting variations, increasing dataset diversity by 35% and accelerating perception model training.
• • Built scalable synthetic data pipelines, improving robustness and generalization across diverse simulated scenarios.
• • Developed and evaluated ROS2 software nodes, improving reliability and consistency in multi-robot integration.
• • Utilized NVIDIA Isaac Sim and Isaac Lab to simulate Physical AI Systems grasping and manipulation tasks for AI-driven control and perception workflows.
• • Worked with VLM/VLA frameworks to explore multimodal perception and task execution in Physical AI Systems robotics.
• • Diagnosed system-level issues using Docker logs, improving system reliability and reducing mission failures.

• • Performed Digital Twin validation by mirroring robot motion and sensor data in simulation, reducing sim-to-real deviation by 17% through tuning of USD assets and physics parameters.
• • Developed sim-to-real validation workflows, improving deployment reliability and consistency across AMR systems.
• • Trained reinforcement and imitation learning policies using Isaac Lab, achieving 20% higher task-completion reliability under continuous simulation cycles.
• • Designed modular simulation tasks and behavioral pipelines for scalable evaluation of autonomous navigation and task execution.
• • Integrated AMRs with edge systems and validated APIs using Python, improving deployment stability by 16% during pilot rollouts.
• • Debugged runtime issues using Docker logs, reducing task interruption events by 21% across deployed systems.

• • Developed autonomy and calibration software using ROS2 and Python, improving system stability and ensuring reliable sensor alignment.
• • Built an end-to-end mobile manipulation pipeline integrating perception, grasp planning, and navigation for dynamic pick-and-place tasks.
• • Engineered perception systems using YOLOv8/v9 and RCNN, improving 3D object localization accuracy by 25%.
• • Designed 3D mapping pipelines using Octomap and MoveIt, achieving over 90% task success rate in dynamic environments.
• • Optimized sensor fusion using Kalman Filter, improving localization and tracking accuracy by 15%.
• • Deployed navigation workflows using Nav2 and NVIDIA Isaac Sim, enabling sim-to-real validation on robotic platforms.
• • Implemented motion planning and control using RRT* and Pure Pursuit, enabling safe navigation and reliable trajectory tracking.
• • Built occupancy grid mapping pipelines using LiDAR data, improving navigation accuracy through noise reduction and map refinement.
• • Worked with RTX LiDAR and RMPFlow, enhancing navigation efficiency and motion policy performance.

Started as a trainee, rapidly progressed through mentor and educator roles, won national and international robotics competitions, conducted workshops and events across India, and was elected President after 4+ years of continuous technical and leadership contribution to the organization.

Orchestrated a member base of 1500+, improved event turnout by 30%, championed cutting-edge robotics workshops, secured long-term sponsorships increasing funding by 25%, and elevated the club's national reputation through strategic leadership and operational excellence.

Led Roboveda 2019, a national-level robotics symposium, managing end-to-end operations, logistics, sponsorships, dispute resolution, and technical execution across 1000+ participants from institutions across India.

Developed and delivered a differentiated robotics curriculum improving technical proficiency by 40% for 30-40 teams annually, led ROS-based hands-on training sessions, delivered motivational talks across universities, and mentored teams to achieve national competition wins.

Served as Technical Deputy and Head of Eight Events at Roboveda 2018, developing challenging problem statements for eight unique robotics competitions, overseeing event logistics, team allocations, and ensuring seamless technical execution throughout the symposium.

Elected Class Representative for Section G for four consecutive years, organized weekly meetings with HOD and Directors, resolved student disputes, managed classroom resources, and ensured seamless communication between faculty and classmates.

Worked on electronics and sensor integration for robotics systems, including calibration of sensors, signal processing, and control systems. Applied concepts such as PWM, PID control, and microcontroller interfacing to develop and test robotic components, improving system reliability and performance.