datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
reachy-mini-emotions-library
Reachy Mini Emotions Library
Curated emotion recordings for the Reachy Mini robot, maintained by
Pollen Robotics. Each move is a JSON trajectory (head pose, antennas,
body yaw, sampled over time) paired with an Opus audio track.
Motion is sampled at 50 Hz; audio is mono Ogg/Opus (decoded natively by
the robot). Requires reachy_mini ≥ v1.8.4 (its move loader resolves
non-.wav audio sidecars).
File layout
Files live at the root of the dataset, named <emotion>.json +… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/reachy-mini-emotions-library.speech-commands-v0.02
Speech Commands Dataset v0.02
This is a re-hosted copy of the Google Speech Commands v0.02 dataset in Parquet format for compatibility with the Hugging Face Dataset Viewer.
⚠️ Credits
This dataset was created by Pete Warden / Google. All credit goes to the original authors and the crowdsourcing contributors.
Original source: http://download.tensorflow.org/data/speech_commands_v0.02.tar.gz
Paper: Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/speech-commands-v0.02.microduck-emotions
Microduck Emotions
A collection of emotions for the Microduck robot. Each one is a motion and a sound designed together, beat by
beat, with the beak opening on the sound, rendered in the physics simulation and validated on the real robot. Every
emotion is three files: the motion (emotions/<name>.json, keyframes at 30 fps: head and body offsets played on
top of whichever trained policy is active, plus the policy hand-overs, such as the sit that devastated and play dead
start)… See the full description on the dataset page: https://huggingface.co/datasets/pollen-robotics/microduck-emotions.social-robotics-acoustic-prosody
Social Robotics: Acoustic Prosody (03c)
Ambient vocal tone around each task — alarming vs soothing — as corroborating context.
One layer of the Social-Affective Filter (SAF) — dehydrated social-signal metadata extracted from egocentric (first-person) video so robots can learn to read human reactions. No raw pixels and no audio. Each row is one source video, keyed by video_id; rehydrate against your own legally-obtained Ego4D copies (below).
Rows: 989 — videos in the… See the full description on the dataset page: https://huggingface.co/datasets/louisye/social-robotics-acoustic-prosody.