Papers
17
Total Citations
467
H-Index
11
About
Jacob Arkin is a leading researcher in human-robot interaction, with a focus on enabling robots to understand and execute complex, natural language instructions. His work bridges the gap between abstract linguistic concepts and robotic action, pioneering methods for grounding spatial language and task planning. Arkin’s most impactful contribution is the development of models that allow robots to efficiently interpret commands involving objects, regions, and motion constraints, as evidenced by his highly cited paper “Efficient Grounding of Abstract Spatial Concepts for Natural Language Interaction with Robot Manipulators” (90 citations). He has further advanced the field with “AutoTAMP” (71 citations), which uses large language models (LLMs) as translators and checkers for task and motion planning, and “Scalable Multi-Robot Collaboration with Large Language Models” (67 citations), exploring centralized versus decentralized systems. His work on multimodal estimation and real-time corrective instructions has been instrumental in making assistive robots more robust and responsive. With over 400 total citations, Arkin’s research is foundational for creating robots that can collaborate seamlessly with humans in dynamic, unstructured environments, from homes to field operations.
Research Focus
Key Achievements
Top Papers
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- 6Real-time natural language corrections for assistive robotic manipulators25 citations · 2017
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- 8Robot-Initiated Specification Repair through Grounded Language Interaction14 citations · 2017
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- 10Grounding Abstract Spatial Concepts for Language Interaction with Robots13 citations · 2017