louisye/social-robotics-motor-resonance

Social Robotics: Motor Resonance (03f)

Did the bystander flinch in sympathy with a sudden camera jolt, or mirror the wearer's motion?

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: 965 — videos in the evaluation slice for which this layer produced a measured signal (videos it could not measure are excluded from this per-layer dataset; the layers still join 1:1 on video_id).
• Signal: Sympathetic motor responses: an impulsive bystander 'flinch' time-locked to the wearer's dominant camera jolt (motor resonance), and postural mirroring of the wearer's vertical motion.
• Method: YOLOv8-pose dense in-window sampling + Farneback optical flow; impulse + dominant-jolt resonance gate.

⚠️ Read this first — interpretation caveats

• max_ego_chaos_score describes the wearer's camera, not the bystander — it is context for the flinch detector, not a bystander reaction.
• Motor-resonance is deliberately conservative (impulse ≥ 3× the bystander's own median, time-locked to the single dominant jolt, ≥ 2 genuine detections), so camera motion through a fixed bbox cannot manufacture a flinch.
• Egocentric footage is legitimately low-yield (small/sparse bystander faces, heavy camera motion); we publish honest measurements only, never fabricated zeros.

Columns

Identity & manifest (shared across all SAF datasets)

column	type	meaning
video_id	string	Ego4D source-clip UUID. The rehydration key — map back to your own legally-obtained Ego4D copy (<video_id>.mp4).
source_dataset	string	Origin corpus (ego4d).
task_labels	string	Comma-joined VLM task label(s) — the activity the camera-wearer performed.
duration_sec	float	Source clip duration (seconds).
fps	float	Source clip frame rate.

Motor Resonance signal

column	type	meaning
motor_resonance_tasks_analyzed_raw	JSON string	Per-task / per-person detail: ego_kinetic_chaos_score, bystander_pose_velocity_peak, resonance_delay_sec, motor_resonance_detected, empathy_scalar, mirroring_detected, mirroring_scalar.
motor_resonance_max_ego_chaos_score	float (~0–1)	Max per-task ego-kinetic chaos — the camera-wearer's motion intensity (context), NOT a bystander signal.
motor_resonance_max_empathy_scalar	float (0–1)	Max empathy scalar; non-zero only when a resonance fired.
motor_resonance_any_motor_resonance	bool	True if any bystander produced an impulsive flinch time-locked to the dominant ego jolt.
motor_resonance_max_mirroring_scalar	float (0–1)	Max mirroring-correlation strength across bystanders.
motor_resonance_any_mirroring	bool	True if any bystander's spine-angle change correlated with sustained downward camera motion.

The *_raw JSON column

The *_raw column holds the full nested per-task / per-person detail as a JSON string. Parse it with:

import json, pandas as pd
df = pd.read_parquet("hf://datasets/louisye/social-robotics-motor-resonance/social_metadata.parquet")
raw_col = next(c for c in df.columns if c.endswith("_raw"))
detail = json.loads(df.iloc[0][raw_col])

How to load

import pandas as pd
df = pd.read_parquet("hf://datasets/louisye/social-robotics-motor-resonance/social_metadata.parquet")
# or:  from datasets import load_dataset;  ds = load_dataset("louisye/social-robotics-motor-resonance")

Rehydration — mapping back to video

video_id is the Ego4D clip UUID. With your own licensed Ego4D copy, the file is <video_id>.mp4; timestamps in the *_raw columns index into that clip. A helper (rehydrate_dataset.py) is included. We never redistribute source media — obtain Ego4D under its own license.

Provenance

Generated by the SAF pipeline (export_metadata.json records schema_version + pipeline_git_sha). Headers are the descriptive layer name + metric; the pipeline's internal 03f_ layer-id prefix is stripped at publish time. License MIT (this metadata only; Ego4D videos remain under the Ego4D license).