Shanthosh Raaj
Mohanram Mageswari
MEng Robotics · University of Maryland College Park
Researching robot learning, tactile sensing, and perception driven manipulation. Building systems that let robots feel, see, and act in the real world.
About
I'm a Master's student in Robotics at the University of Maryland, College Park, working as a Research Assistant in the Perception & Robotics Group (PRG) under Prof. Yiannis Aloimonos.
My research focuses on building robots that can manipulate objects with human like dexterity combining vision, touch, and language to create richer, more adaptive robot behaviors.
Before graduate school, I founded a software startup (ApexMind Tech Solutions) and hold a B.E. in Computer Science Engineering from Panimalar Engineering College, Chennai, Tamil Nadu, India.
Education
Experience
- Researching multi-modal representation learning for contact-rich manipulation — combining vision, tactile sensing, proprioception, and language.
- Developing robot learning policies on a real UR5e using Diffusion Policy trained on synchronized multimodal demonstrations with GelSight Mini tactile feedback.
- Investigating tactile-driven manipulation to improve contact awareness, grasp robustness, and execution reliability on physical hardware.
- Building end-to-end data collection and evaluation workflows including robot calibration and policy benchmarking on task success and robustness metrics.
- Founded and led a software startup serving small businesses, delivering custom websites and billing/invoicing systems.
- Built full-stack solutions using Java and Python — customer records, invoice generation, payment tracking, and sales reports.
- Led and mentored a small team, setting coding standards and running code reviews to improve delivery cadence.
- Collaborated with senior developers to build and test full-stack applications using Java, React, and SQL.
- Gained hands-on experience in debugging, deployment, and integrating backend services with frontend interfaces.
Publications
Developed a UR5e-based robot learning system combining RGB perception, GelSight Mini tactile feedback, proprioceptive state, and continuous action trajectories. Designed modality-specialized encoders, asymmetric temporal horizons, discriminative embeddings, and event-based memory for language-guided, phase-adaptive sensor prioritization. Evaluated vision-only vs. vision+tactile policy variants on physical robot hardware.