Papers
10
Total Citations
98
H-Index
6
About
Sriram Siva is a robotics researcher whose work sits at the intersection of autonomous navigation, adaptive perception, and human-robot collaboration. His research primarily focuses on enabling robots to operate reliably in unstructured, real-world environments — from dense forests to complex off-road terrain — where traditional methods frequently fall short. Siva's most influential contribution, "Robot Adaptation to Unstructured Terrains by Joint Representation and Apprenticeship Learning" (2019, 27 citations), established a foundational framework for terrain-adaptive navigation using learned representations. This theme continues throughout his work, culminating in systems like NAUTS and RIDER, which leverage negotiation strategies and reinforcement learning to handle dynamic, unpredictable outdoor conditions. Complementing his navigation research, his work on omnidirectional multisensory perception fusion (20 citations) and long-term teammate following (19 citations) demonstrates a strong commitment to robust, sensor-diverse perception for sustained autonomy. Beyond mobility, Siva has ventured into ethical dimensions of robotics, exploring context-sensitive moral cognition in robot architectures and multi-robot collaborative scheduling. His accumulated citations — exceeding 95 across ten papers — reflect a growing recognition of his contributions. Siva represents an emerging voice in field robotics, steadily bridging the gap between controlled laboratory systems and genuinely autonomous robots in the wild.
Research Focus
Key Achievements
Top Papers
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- 5NAUTS: Negotiation for Adaptation to Unstructured Terrain Surfaces7 citations · 2022
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