Papers
21
Total Citations
580
H-Index
10
About
Nakul Gopalan is a leading researcher at the intersection of robotics and natural language processing, dedicated to building robots that can understand and act upon human language. His work centers on enabling robots to map words to the physical world, bridging the gap between high-level commands and low-level sensorimotor control. Gopalan’s most influential contribution is the widely-cited survey "Robots That Use Language" (204 citations), which provides a comprehensive robotics-centric perspective on grounding language in physical action. He has pioneered techniques for parsing natural language into grounded reward functions and semantic goal representations, allowing robots to generalize from instructions to novel tasks. His research on planning with abstract Markov decision processes (49 citations) and sequence-to-sequence language grounding (45 citations) addresses the critical challenge of hierarchical abstraction for efficient planning under uncertainty. Gopalan has also explored innovative human-robot interfaces, including using mixed reality to control drones with natural language. His work on Bayesian optimization for bipedal locomotion (61 citations) demonstrates a broader expertise in robot learning. Through these contributions, Gopalan is shaping a future where robots can intuitively collaborate with humans in complex, real-world environments.
Research Focus
Key Achievements
Top Papers
- 1Robots That Use Language204 citations · 2020
- 2Bayesian Gait Optimization for Bipedal Locomotion61 citations · 2014
- 3Planning with Abstract Markov Decision Processes49 citations · 2017
- 4Sequence-to-Sequence Language Grounding of Non-Markovian Task Specifications45 citations · 2018
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- 6Robot Object Retrieval with Contextual Natural Language Queries43 citations · 2020
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