---
title: ABot-ER-Training-Data
canonical_url: "https://www.modelscope.cn/datasets/amap_cvlab/ABot-ER-Training-Data"
md_url: "https://www.modelscope.cn/datasets/amap_cvlab/ABot-ER-Training-Data.md"
repository: amap_cvlab/ABot-ER-Training-Data
last_updated: 2026-10-14
license: other
storage_size: "1.6 GB"
downloads: 29
stars: 0
---

# ABot-ER-Training-Data

> ABot-ER-Training-Data - amap_cvlab 在 ModelScope 开源的数据集。ABot-ER sealed Stage A and Stage B JSONL training data for private review.

amap_cvlab/ABot-ER-Training-Data 是 ModelScope 魔搭社区上的数据集，存储大小 1.6 GB，采用 other 许可。

- **Repository**: amap_cvlab/ABot-ER-Training-Data
- **License**: other
- **Storage size**: 1.6 GB
- **Downloads**: 29
- **Stars**: 0
- **Last updated**: 2026-10-14

Source: https://www.modelscope.cn/datasets/amap_cvlab/ABot-ER-Training-Data

---

# ABot-ER Training Data

ABot-ER Training Data is the JSONL training release used for the ABot-ER multi-capability foundation and final joint post-training stages. It covers spatial and relational reasoning, scene and state understanding, temporal and event reasoning, point grounding and affordance, and long-horizon action organization.

## Dataset structure

| Split | Purpose | Physical records | Shards |
|---|---|---:|---:|
| `stage_a_pretrain` | Multi-capability foundation training | 462,385 | 288 |
| `stage_b_posttrain` | Final joint post-training | 104,960 | 128 |

Files are stored under:

```text
data/stage_a_pretrain/*.jsonl
data/stage_b_posttrain/*.jsonl
```

The release preserves the physical exposure order of the sealed training data. Stage B therefore contains intentional repeated exposures where present in the training release.

## Record format

Each JSONL record contains:

- `conversations`: user/assistant training messages;
- `images` and `videos`: repository-relative media references;
- `task_family`: `2d_qa` or `task_plan`;
- `benchmark_proxy`, `capability_primary`, `answer_format`, and coordinate metadata;
- canonical source name and repository;
- available question, media, trajectory, and grouping hashes.

Task-specific output contracts are retained in the assistant label. VABench-P uses eight normalized points wrapped in its XML answer contract; Where2Place point-supervision records use a tuple list. Auxiliary open-text records preserve their original answer type. Multiple-choice, numeric, open-text, and executable-plan records retain their benchmark-aligned schemas.

## Task-Conditioned Adaptive Training Strategy

Optional `route_token` and `route_source` metadata describe the task-family routing.
Route tokens are injected by the model data loader and are not physically added to the frozen user prompt:

- `2d_qa` records use `[2D_QA]`;
- `task_plan` records use `[TASK_PLAN]`.

Models without task-conditioned residuals can train on the same JSONL without route-token injection.

## Media

This repository is a JSONL-only release. Image and video binaries are not duplicated here. The `images` and `videos` fields are stable relative references, while `manifests/media_manifest.jsonl` provides hashes and source information for media acquisition and verification.

## Sources and terms

The corpus combines public embodied vision-language datasets and ABot-ER annotations. Canonical sources, record counts, upstream repositories, and applicable terms are listed in `manifests/source_manifest.csv`. Source-specific terms continue to apply. HANDAL- and HOI4D-derived records are restricted to non-commercial research use under their respective licenses.

## Intended use

This release is intended for non-commercial technical research and academic use in embodied multimodal understanding and planning. Users are responsible for obtaining referenced media from the corresponding upstream sources and complying with source-specific terms.

## Privacy and integrity

The public records contain no private NAS paths, personal workspace paths, internal model URIs, or credentials. File hashes are provided in `SHA256SUMS.txt`, and aggregate release statistics are recorded in `RELEASE_MANIFEST.json`.

## Citation

Citation information for the ABot-ER technical report will be added to this repository when the report is publicly available.