Authors:
Takahiro Suzuki
;
Yuta Ando
and
Manabu Hashimoto
Affiliation:
Graduate School of Engineering, Chukyo University, Aichi, Japan
Keyword(s):
Robot Motion Generation, Automatic Error Correction, Part Affordance, Functional Consistency, Robot Motion Template, GPT.
Abstract:
In this research, we proposed a technique that, given a simple instruction such as ”Please make a cup of coffee” as would commonly be used when one human gives another human an instruction, determines an appropriate robot motion sequence and the tools to be used for that task and generates a motion trajectory for a robot to execute the task. The proposed method uses a large language model (GPT) to determine robot motion sequences and tools to be used. However, GPT may select tools that do not exist in the scene or are not appropriate. To correct this error, our research focuses on function and functional consistency. An everyday object has a role assigned to each region of that object, such as ”scoop” or ”contain”. There are also constraints such as the fact that a ladle must have scoop and grasp functions. The proposed method judges whether the tools in the scene are inconsistent with these constraints, and automatically corrects the tools as necessary. Experimental results confirme
d that the proposed method was able to generate motion sequences a from simple instruction and that the proposed method automatically corrects errors in GPT outputs.
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