Arnav Tuli

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About

Arnav Tuli is an emerging researcher working at the exciting intersection of robotics, natural language processing, and neuro-symbolic artificial intelligence. His work focuses on enabling robots to understand and execute complex manipulation tasks guided by natural language instructions — a challenge that sits at the heart of making robots genuinely useful in unstructured, real-world environments. Tuli's most significant contribution is his research on neuro-symbolic program learning for language-guided robot manipulation, which has garnered 13 citations across its iterations. This work addresses critical limitations in prior approaches by moving beyond hand-coded symbolic representations, allowing models to generalize more effectively to novel concepts and instructions. By bridging neural learning with symbolic reasoning, his framework enables robots to translate human language into executable manipulation programs in a principled and flexible manner. Building on this foundation, his more recent 2024 work tackles the equally important challenge of error recovery during plan execution, introducing neuro-symbolic methods that allow robots to autonomously detect and recover from failures — a crucial capability for robust autonomous systems operating without constant human oversight. Though early in his career, Tuli's research addresses fundamental bottlenecks in human-robot interaction and autonomous manipulation, positioning him as a promising contributor to next-generation intelligent robotics systems.