A fine-tuned FunctionGemma 270M model that converts natural language into structured robot action and emotion function calls. Supports 6 languages with 98% accuracy at ~59ms on NVIDIA Jetson AGX Thor.
Supported Languages
🇬🇧 English · 🇨🇳 中文 · 🇯🇵 日本語 · 🇫🇷 Français · 🇩🇪 Deutsch · 🇪🇸 Español
Example
Input: "Can you shake hands with me?" → robot_action(shake_hand) + show_emotion(happy)
Input: "跟我握手" → robot_action(shake_hand) + show_emotion(happy)
Input: "握手してください" → robot_action(shake_hand) + show_emotion(happy)
Input: "Serrez-moi la main" → robot_action(shake_hand) + show_emotion(happy)
Input: "Gib mir die Hand" → robot_action(shake_hand) + show_emotion(happy)
Input: "Dame la mano" → robot_action(shake_hand) + show_emotion(happy)
Input: "我今天心情不好" → robot_action(stand_still) + show_emotion(sad)
Input: "あれは何ですか？" → robot_action(stand_still) + show_emotion(confused)
Input: "Raconte-moi une blague" → robot_action(stand_still) + show_emotion(think)
Supported Actions
Action
Description
shake_hand
Handshake gesture
face_wave
Wave hello / goodbye
hands_up
Raise both hands
stand_still
Stay idle (default for general conversation)
show_hand
Show open hand / present card for payment
do_payment
Do the payment / do the payment
down_payment
Finished the payment
Supported Emotions
Emotion
Animation
happy
Happy.riv
sad
Sad.riv
excited
Excited.riv
confused
Confused.riv
curious
Curious.riv
think
Think.riv
Constrained decoding uses 2 forward passes instead of 33 autoregressive steps, achieving ~18x speedup over standard model.generate().
Training Details
Parameter
Value
Base model
google/functiongemma-270m-it
Method
LoRA (rank 8, alpha 16)
Training data
~6,000 examples (545 English + ~5,450 multilingual)
Multilingual training data was generated using Claude API — 2 natural phrasings per language per English prompt, resulting in diverse and natural expressions rather than literal translations.
