Papers
6
Total Citations
92
H-Index
4
About
Rahul Tallamraju is a robotics researcher whose work sits at the intersection of multi-robot coordination, motion planning, and autonomous aerial systems. His key contributions span decentralized control for target tracking, deep reinforcement learning for human motion capture, and cooperative payload transport. In his most-cited work (42 citations), he developed a decentralized Model Predictive Control framework enabling multiple robots to track targets while dynamically avoiding obstacles—a critical capability for real-world deployment. His paper "AirCapRL" (34 citations) introduced a deep RL-based formation controller for autonomous aerial human motion capture, advancing vision-based MoCap by enabling multiple drones to collaboratively estimate a moving person's body pose and shape. Tallamraju has also made notable contributions to deformable payload transportation, designing algorithms for loosely coupled nonholonomic robots to navigate static and dynamic obstacles, and even proposing robot replacement strategies to extend operational time. His work on motion planning for multi-mobile-manipulator systems addresses the challenging problem of navigating tight spaces with high-dimensional configuration spaces. Through these contributions, Tallamraju has demonstrated a consistent focus on enabling practical, real-time multi-robot systems for complex, dynamic environments.
Research Focus
Key Achievements
Top Papers
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- 4Loosely Coupled Payload Transport System with Robot Replacement4 citations · 2019
- 5Motion Planning for Multi-Mobile-Manipulator Payload Transport Systems2 citations · 2019
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