Gesture enhanced comprehension of ambiguous human-to-robot instructions
This work demonstrates the feasibility and benefits of using pointing gestures, a naturally-generated additional input modality, to improve the multi-modal comprehension accuracy of human instructions to robotic agents for collaborative tasks.We present M2Gestic, a system that combines neural-based...
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| Main Authors: | , , , , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2020
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/5369 https://ink.library.smu.edu.sg/context/sis_research/article/6373/viewcontent/26._Gesture_Enhanced_Comprehension_of_Ambiguous_Human_To_Robot_Instructi....pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | This work demonstrates the feasibility and benefits of using pointing gestures, a naturally-generated additional input modality, to improve the multi-modal comprehension accuracy of human instructions to robotic agents for collaborative tasks.We present M2Gestic, a system that combines neural-based text parsing with a novel knowledge-graph traversal mechanism, over a multi-modal input of vision, natural language text and pointing. Via multiple studies related to a benchmark table top manipulation task, we show that (a) M2Gestic can achieve close-to-human performance in reasoning over unambiguous verbal instructions, and (b) incorporating pointing input (even with its inherent location uncertainty) in M2Gestic results in a significant (30%) accuracy improvement when verbal instructions are ambiguous. |
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