Non-transitory computer-readable recording medium, training method, inference method, and information processing apparatus

A non-transitory computer-readable recording medium having stored therein a program that causes a computer to execute a process comprising:

• adding a shared parameter to:
• parameters of a plurality of motion learning models that each predict a second state of a target from a first state of the target;
• and a parameter of a language learning model that has been trained, the language learning model being related to a motion instruction for the target;
• obtaining inference target data including the first state and the second state from the target;
• generating a reference parameter resulting from optimization of the shared parameter based on the inference target data, in a state where optimized parameters of the language learning model and motion learning models have been fixed;
• selecting a motion learning model from the plurality of motion learning models based on the reference parameter and a plurality of shared parameters obtained from the plurality of motion learning models by prior training;
• and
• inferring a second state by inputting a first state in the inference target data to the motion learning model selected.

A training method comprising:

• adding a shared parameter to:
• parameters of a plurality of motion learning models that each predict a second state of a target from a first state of the target;
• and a parameter of a language learning model that has been trained, the language learning model being related to a motion instruction for the target;
• obtaining a training data set including a plurality of sets of training data each having the first state as input data and the second state as ground truth data;
• and
• optimizing, by using the training data set, the shared parameter and a parameter of a motion learning model of the plurality of motion learning models, the motion learning model corresponding to the training data set, in a state where the parameter of the language learning model has been fixed, by using a processor.

An inference method comprising:

• adding a shared parameter to:
• parameters of a plurality of motion learning models that each predict a second state of a target from a first state of the target;
• and a parameter of a language learning model that has been trained, the language learning model being related to a motion instruction for the target;
• obtaining inference target data including the first state and the second state from the target;
• generating a reference parameter resulting from optimization of the shared parameter based on the inference target data, in a state where optimized parameters of the language learning model and motion learning models have been fixed;
• selecting a motion learning model from the plurality of motion learning models based on the reference parameter and a plurality of shared parameters obtained from the plurality of motion learning models by prior training;
• and
• inferring a second state by inputting a first state in the inference target data to the motion learning model selected, by using a processor.

An information processing apparatus comprising:

• a memory;
• and
• a processor coupled to the memory and configured to:
• add a shared parameter to:
• parameters of a plurality of motion learning models that each predict a second state of a target from a first state of the target;
• and a parameter of a language learning model that has been trained, the language learning model being related to a motion instruction for the target;
• obtain a training data set including a plurality of sets of training data each having the first state as input data and the second state as ground truth data;
• and
• optimize, by using the training data set, the shared parameter and a parameter of a motion learning model of the plurality of motion learning models, the motion learning model corresponding to the training data set, in a state where the parameter of the language learning model has been fixed.

An information processing apparatus comprising:

• a memory;
• and
• a processor coupled to the memory and configured to:
• add a shared parameter to:
• parameters of a plurality of motion learning models that each predict a second state of a target from a first state of the target;
• and a parameter of a language learning model that has been trained, the language learning model being related to a motion instruction for the target;
• obtain inference target data including the first state and the second state from the target;
• generate a reference parameter resulting from optimization of the shared parameter based on the inference target data, in a state where optimized parameters of the language learning model and motion learning models have been fixed;
• select a motion learning model from the plurality of motion learning models based on the reference parameter and a plurality of shared parameters obtained from the plurality of motion learning models by prior training;
• and
• infer a second state by inputting a first state in the inference target data to the motion learning model selected.