DoneHans/NavQA_Revised

NavQA Revised

NavQA Revised is a re-annotated version of the NaVQA dataset released with NVIDIA ReMEmbR. The original annotations are distributed in `remembr/data/navqa/data.csv`.

This repository provides the revised annotations as JSONL files:

navqa.jsonl
navqa_over_sequence.jsonl

Each line is one question-answer example. navqa.jsonl is the primary revised annotation file. navqa_over_sequence.jsonl uses the same schema and examples, but uses the beginning of the full sequence as the query start point. In this variant, the query does not need to consider selecting a shorter time segment from within the sequence.

Dataset Details

Configurations

What Changed

Compared with the original NaVQA annotations, this version makes two main corrections.

1. 1.Timezone correction

The original timestamps are interpreted using the local timezone of the UT Austin campus where the data was collected (America/Chicago). We convert Unix timestamps to the correct local time before matching them with navigation events and before inserting them into the natural-language query.

1. 1.2 fps matching and direct query generation

Instead of using a coarser caption interval, examples are matched at 2 fps. The final query text is generated directly from the corrected temporal and spatial context, including the start time, current time, and current robot pose when available.

Dataset Summary

Each file contains 210 examples:

Answer types:

File Variants

• —navqa.jsonl: The query start time is the start of the selected QA time window.
• —navqa_over_sequence.jsonl: The query start time is the beginning of the corresponding sequence. This version treats the query as being asked over the whole sequence history, so there is no need to choose an additional temporal segment inside the sequence.

Data Format

Each JSON object has the following fields:

Example:

{
  "id": "LONG_5a6cef0c-175d-4779-bfd4-f7b193fb9039",
  "seq_id": 0,
  "length_category": "LONG",
  "question": "You started moving at 2023-01-16 09:55:32. The current time is 2023-01-16 10:03:02 and you are located at [1.42, 2.28, -0.17].\nWhen did you leave the building?",
  "type": "time",
  "answers": {
    "text": ["4.07 minutes ago"],
    "time": 4.07
  },
  "answer_timestamp": "9:59:00",
  "parsable_answer": "[minutes] ago"
}

Loading the Data

import json

examples = []
with open("navqa.jsonl", "r", encoding="utf-8") as f:
    for line in f:
        examples.append(json.loads(line))

print(len(examples))
print(examples[0]["question"])

Intended Use

This dataset is intended for evaluating navigation-oriented video question answering systems and embodied agents that reason over long-horizon spatial and temporal memory. It is especially useful for models that need to answer:

• —spatial questions that return a goal position,
• —temporal questions that return a time or elapsed duration,
• —descriptive questions that return text,
• —binary questions about whether an event or object appears in the navigation history.

Relationship to ReMEmbR and CODa

ReMEmbR introduces NaVQA as an evaluation dataset for long-horizon spatio-temporal robot memory. The upstream evaluation instructions describe NaVQA as 210 examples built from selected CODa sequences, with 30 questions per sequence.

This repository only provides the revised QA annotations. It does not include the original CODa sensor data, images, captions, or ReMEmbR model outputs. To reconstruct the full evaluation pipeline, refer to the upstream ReMEmbR repository and its evaluation instructions.

License

This repository is released under the Apache 2.0 license. The data is derived from the NaVQA annotations.