Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data
Recent advances in imitation learning have enabled robots to learn multiple tasks from large-scale datasets. However, developing a model for multi-tasking humanoid control faces significant challenges. Human kinematic data is available in open-source datasets for humanoid motion learning, but learni...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11000102/ |
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| author | Siddharth Padmanabhan Kazuki Miyazawa Takato Horii Takayuki Nagai |
| author_facet | Siddharth Padmanabhan Kazuki Miyazawa Takato Horii Takayuki Nagai |
| author_sort | Siddharth Padmanabhan |
| collection | DOAJ |
| description | Recent advances in imitation learning have enabled robots to learn multiple tasks from large-scale datasets. However, developing a model for multi-tasking humanoid control faces significant challenges. Human kinematic data is available in open-source datasets for humanoid motion learning, but learning policies from this data requires simulation due to the lack of actions. While dynamic data can accelerate learning via supervision, datasets typically lack substantial amounts of such action labels, that are also difficult to be directly used for training due to the unique structure of each human/humanoid systems. In this study, we pre-trained a Generative Pre-trained Transformer (GPT) based model on expert policy-rollout observations only (without actions) from a humanoid motion dataset. Upon fine-tuning on a smaller dataset with both observations and action labels, we demonstrate that our GPT-based model can predict actions to achieve human-like movements faster in training than training a GPT on the entire dataset from scratch directly. Furthermore, performance evaluation based on motion generation across various behaviors showed that our approach achieves efficient learning comparable to baselines. |
| format | Article |
| id | doaj-art-d77c6c8f97284b7dbf5780603413cf5b |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d77c6c8f97284b7dbf5780603413cf5b2025-08-20T03:07:40ZengIEEEIEEE Access2169-35362025-01-0113838578386610.1109/ACCESS.2025.356878411000102Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action DataSiddharth Padmanabhan0https://orcid.org/0009-0009-3339-3123Kazuki Miyazawa1Takato Horii2https://orcid.org/0000-0001-5374-0887Takayuki Nagai3https://orcid.org/0009-0009-1503-651XGraduate School of Engineering Science, Osaka University, Toyonaka-shi, Osaka, JapanGraduate School of Engineering Science, Osaka University, Toyonaka-shi, Osaka, JapanGraduate School of Engineering Science, Osaka University, Toyonaka-shi, Osaka, JapanGraduate School of Engineering Science, Osaka University, Toyonaka-shi, Osaka, JapanRecent advances in imitation learning have enabled robots to learn multiple tasks from large-scale datasets. However, developing a model for multi-tasking humanoid control faces significant challenges. Human kinematic data is available in open-source datasets for humanoid motion learning, but learning policies from this data requires simulation due to the lack of actions. While dynamic data can accelerate learning via supervision, datasets typically lack substantial amounts of such action labels, that are also difficult to be directly used for training due to the unique structure of each human/humanoid systems. In this study, we pre-trained a Generative Pre-trained Transformer (GPT) based model on expert policy-rollout observations only (without actions) from a humanoid motion dataset. Upon fine-tuning on a smaller dataset with both observations and action labels, we demonstrate that our GPT-based model can predict actions to achieve human-like movements faster in training than training a GPT on the entire dataset from scratch directly. Furthermore, performance evaluation based on motion generation across various behaviors showed that our approach achieves efficient learning comparable to baselines.https://ieeexplore.ieee.org/document/11000102/GPThumanoidimitation learningmotion predictionwhole-body control |
| spellingShingle | Siddharth Padmanabhan Kazuki Miyazawa Takato Horii Takayuki Nagai Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data IEEE Access GPT humanoid imitation learning motion prediction whole-body control |
| title | Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data |
| title_full | Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data |
| title_fullStr | Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data |
| title_full_unstemmed | Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data |
| title_short | Data-Efficient Approach to Humanoid Control by Fine-Tuning a Pre-Trained GPT on Action Data |
| title_sort | data efficient approach to humanoid control by fine tuning a pre trained gpt on action data |
| topic | GPT humanoid imitation learning motion prediction whole-body control |
| url | https://ieeexplore.ieee.org/document/11000102/ |
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