MotIF-1K Dataset

Multimodal trajectories of human and Stretch-robot motion paired with task and motion annotations, released with the paper "MotIF: Motion Instruction Fine-tuning".

• Paper: MotIF: Motion Instruction Fine-tuning (arXiv:2409.10683)
• Project website: https://motif-1k.github.io
• Code (GitHub): https://github.com/Minyoung1005/motif
• Authors: Minyoung Hwang, Joey Hejna, Dorsa Sadigh, Yonatan Bisk
• Contact: myhwang@mit.edu

Abstract

Many robotics tasks require observing the full motion of the robot — not just start/end states — to correctly judge success (e.g., brushing hair requires the right trajectory, not just ending up "at" the hair). Off-the-shelf vision-language models (VLMs) struggle here because they are trained on single frames and lack robot-motion data. MotIF fine-tunes VLMs using abstract visual motion representations (e.g., keypoint trajectories overlaid on the initial frame, optical flow, key-frame storyboards) to semantically ground robot behavior in the environment. On MotIF-1K, the resulting model outperforms state-of-the-art VLMs by ≥2× in precision and by 56.1% in recall, generalizing across unseen motions, tasks, and environments.

Configs

Config	Rows	Source
human_motion	653	Human demonstrations (brushing, pouring, etc.)
stretch_motion	370	Hello Robot Stretch teleop demonstrations

Covers 13 task categories with varied feasible motions per task.

Fields

• traj_idx (int) — trajectory index
• num_steps (int) — number of frames
• trajectory (list[list[int]]) — 2D keypoint path [x, y] per frame
• task_instruction (str) — high-level task (e.g. "brush hair")
• motion_description (str) — motion verb phrase (e.g. "move downward and upward, repeating 3 times")
• video_raw, video_trajviz (video) — original and trajectory-overlaid clips (mp4)
• last_frame_raw, last_frame_trajviz (image) — final-frame stills
• opticalflow (image) — optical-flow visualization (some rows may be null in human_motion)
• storyboard_key{2,4,9,16} (image) — storyboard grids at K key-frames
• storyboard_key{2,4,9,16}_trajviz (image) — same grids with trajectory overlay

Usage

from datasets import load_dataset

ds = load_dataset("myconnects/motif", "human_motion", split="train")
row = ds[0]
print(row["task_instruction"], row["motion_description"])
row["video_raw"]              # decoded video
row["last_frame_trajviz"]     # PIL Image

Training & Evaluation Code

The MotIF GitHub repository contains the full codebase: data-collection scripts, LoRA fine-tuning pipeline (LLaVA-based), evaluation (with optional logits), pretrained model checkpoints, a videoLM-architecture variant, a success-detection comparison (GPT / Gemini / MotIF), and a Gradio web UI.

Citation

@article{hwang2024motif,
  title     = {MotIF: Motion Instruction Fine-tuning},
  author    = {Hwang, Minyoung and Hejna, Joey and Sadigh, Dorsa and Bisk, Yonatan},
  booktitle = {arXiv preprint arXiv:2409.10683},
  year      = {2024},
}

Acknowledgements

We thank Abitha Thankaraj, Hao Zhu, Leena Mathur, Quanting Xie, Rosa Vitiello, Su Li, Tiffany Min, Vidhi Jain, and Yingshan Chang for helping us collect the dataset and providing thoughtful feedback.