README.md

---
license: cc-by-nc-4.0
language:
- en
pretty_name: EmbodiedMemory-Bench
size_categories:
- 1K<n<10K
task_categories:
- robotics
configs:
- config_name: default
  data_files:
  - split: all
    path: data/embodied_memory_bench.jsonl
dataset_info:
- config_name: default
  features:
  - name: id
    dtype: string
  - name: family
    dtype: string
  - name: family_id
    dtype: string
  - name: difficulty
    dtype: string
  - name: episode_name
    dtype: string
  - name: description
    dtype: string
  - name: scene
    dtype: string
  - name: probe_id
    dtype: string
  - name: probe_instruction
    dtype: string
  - name: context_sessions
    dtype: int32
  - name: context_steps
    dtype: int32
  - name: memory_cues
    dtype: string
  - name: episode_path
    dtype: string
  - name: preview_image
    dtype: string
  splits:
  - name: all
    num_examples: 2554
tags:
- embodied-ai
- embodied-memory
- multimodal
- long-horizon-planning
- ai2-thor
- procthor
- benchmark
---

<div align="center">
  <img src="assets/mark.webp" alt="EmbodiedMemory-Bench logo" width="76">
  <h1>EmbodiedMemory-Bench</h1>
  <p><strong>Benchmarking Embodied Memory for Long-Horizon Embodied Tasks</strong></p>
  <p><em>Remember what the world was. Track how it changed. Act beyond the moment.</em></p>
  <p>
    <a href="#benchmark"><img src="https://img.shields.io/badge/Benchmark-Overview-176B70?style=flat-square" alt="Benchmark overview"></a>
    <a href="#quick-start"><img src="https://img.shields.io/badge/Data-Quick_Start-202A33?style=flat-square" alt="Quick start"></a>
  </p>
  <p><strong>2,554 episodes · 4 memory challenges · AI2-THOR &amp; ProcTHOR</strong></p>
</div>

<p align="center">
  <video controls playsinline preload="metadata" poster="https://huggingface.co/datasets/lzLiang/EmbodiedMemoryBench/resolve/main/assets/video-poster.webp" width="100%">
    <source src="https://huggingface.co/datasets/lzLiang/EmbodiedMemoryBench/resolve/main/assets/overview.mp4" type="video/mp4">
  </video>
</p>

## Benchmark

**EmbodiedMemory-Bench (EMem-Bench)** evaluates memory through actions in an interactive environment. An agent first observes multimodal interaction history without knowing its future task. It must then use that experience to complete a later task: remember a detail, update a changed state, avoid a previous failure, or apply a learned rule.

<p align="center">
  <img src="assets/task-overview.webp" alt="Four memory challenges: Passive Observation, Dynamic Tracking, Interaction Failure, and Experience Generalization" width="100%">
</p>

| Memory challenge | What the agent must remember | Episodes |
|:--|:--|--:|
| **Passive Observation** | An object’s location seen earlier, amid unrelated activity | **1,036** |
| **Dynamic Tracking** | The latest location or state after an object changes | **1,052** |
| **Interaction Failure** | A constraint revealed by an earlier interaction outcome | **263** |
| **Experience Generalization** | A pattern learned from corrections and applied to a new object or scene | **203** |
| **Total** | Four complementary memory capabilities | **2,554** |

Interaction Failure episodes are stored under `interaction_experience/`. The dataset contains historical RGB observations, action and feedback traces, task probes, and evaluation annotations. Benchmark membership is fixed by [the Full-2554 manifest](manifests/full2554.jsonl). The **Dataset Viewer** uses the compact `all` split at [`data/embodied_memory_bench.jsonl`](data/embodied_memory_bench.jsonl); open an `episode_path` to inspect the complete record and `preview_image` to view a representative observation.

<details>
<summary><strong>How the benchmark is constructed</strong></summary>

<p align="center">
  <img src="assets/construction.webp" alt="Benchmark construction: scene selection, memory cue design, distractor synthesis, and quality checks" width="100%">
</p>

Construction combines simulator-grounded scenes, task-specific memory cues, and distractor trajectories. The accompanying paper describes the execution checks and quality review used to retain the final episodes. Construction and evaluation resources will accompany the public code release.

</details>

## Quick start

Download the complete episode and image package:

```bash
pip install -U huggingface_hub
hf download lzLiang/EmbodiedMemoryBench \
  --repo-type dataset \
  --local-dir EmbodiedMemoryBench-Data
```

Read an episode through the portable index:

```python
import csv
import json
from pathlib import Path

root = Path("EmbodiedMemoryBench-Data")
with (root / "browse.tsv").open(encoding="utf-8") as f:
    index = list(csv.DictReader(f, delimiter="\t"))

row = index[0]
episode_path = root / row["episode_json"]
episode = json.loads(episode_path.read_text(encoding="utf-8"))
print(row["family"], episode["episode_id"])
print("Context sessions:", len(episode["sessions"]))
```

This release is an episode-and-media package. The index provides paths to its nested JSON records; downloading the repository also retrieves the context images. The accompanying evaluation code supports `full_context` and `emem` modes; its public release will provide simulator setup and execution instructions.

<details>
<summary><strong>Repository layout and episode schema</strong></summary>

```text
EmbodiedMemoryBench-Data/
├── episodes/
│   ├── passive_observation/<episode>/
│   ├── dynamic_tracking/<episode>/
│   ├── interaction_experience/<episode>/
│   └── experience_generalization/<episode>/
├── manifests/
│   ├── full2554.jsonl
│   ├── <family>.jsonl
│   └── audit_report.json
├── browse.tsv
├── metadata.json
└── verification_report.json
```

Each episode directory contains its JSON record and historical images. Use `browse.tsv` or a family manifest’s `organized_view.episode_json` to locate the downloaded JSON. The canonical manifest’s `source.episode_path` records the original research-workspace path.

| Field | Contents |
|:--|:--|
| `episode_id` | Stable episode identifier |
| `sessions` | Context trajectories with observation references, actions, and feedback |
| `sessions[].micro_probe` | Session-level probe, when present |
| `macro_probe` | Final probe, when present |
| `memory_cues`, `hidden_rules` | Evaluation annotations for planted cues and constraints |
| `scene_metadata` | Scene provenance and setup metadata |

The raw files include evaluator annotations. Use the evaluation code to construct model inputs: future task information must stay out of history ingestion, and hidden rules, cue labels, expected actions, and privileged simulator state must stay evaluator-side.

</details>

<details>
<summary><strong>Frozen membership and integrity records</strong></summary>

The [metadata](metadata.json), [membership audit](manifests/audit_report.json), and [verification report](verification_report.json) record the release counts and integrity checks. The verification report records **2,554 verified episodes** and no issues. Each canonical manifest row includes an episode JSON SHA-256.

Canonical manifest SHA-256:

```text
a4af3641a607dd69e6afc1a09661a4b1046747206e064016fd2ebb8c56745c92
```

Episode names, top-level descriptions, and probe instructions have been translated to English. The original language version is available at revision `28694bf83b78049ac96ba01c9ffb13aeac7eaef9`. Episode membership and evaluation annotations are unchanged; episode and manifest hashes have been updated.

The release is the complete evaluation set; manifest rows use `split: all`. No train/validation partition is declared here.

</details>

## Evaluation

The benchmark measures **Success Rate (SR ↑)** and **Error Recurrence Rate (ERR ↓)**. Report per-family scores and their equal-family macro average so the smaller task families remain represented.

## Sources and license

The benchmark is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). It builds on [AI2-THOR](https://ai2thor.allenai.org/) and [ProcTHOR](https://procthor.allenai.org/); their environments, assets, and dependencies remain subject to their respective upstream terms.

## Reference

**EmbodiedMemory-Bench: Benchmarking Embodied Memory for Long-Horizon Embodied Tasks.** Final author information, the paper link, and BibTeX will be added with the public preprint.

Project resources (public access forthcoming): [project page](https://zju-omniai.github.io/EmbodiedMemoryBench/) · [code](https://github.com/ZJU-OmniAI/EmbodiedMemoryBench).