---
tags:
- lerobot
- maniskill
- non-markovian
- vla
---

# ManiSkill-Memory-Dependence Benchmark

A comprehensive memory dependence robot benchmark across 4 manipulation tasks from different memory dimensions, introduced in the paper **"Non-Markovian Long-Horizon Robot Manipulation via Keyframe Chaining"**.

## Dataset Description

This dataset provides a suite of Non-Markovian manipulation tasks built upon the ManiSkill simulator to measure task success rates in scenarios requiring long-horizon memory and state disambiguation. 
It is specifically designed to evaluate Vision-Language-Action (VLA) models on their ability to resolve state aliasing and handle memory-dependent operations.

### Benchmark Tasks

The benchmark evaluates models across four distinct memory dependence dimensions:

1. **Spatial Reconfiguration:** The agent must dismantle a vertical stack of three randomly ordered blocks and reconstruct them in a permuted sequence. 
2. **Temporal Sequencing:** The robot must perform a “pick-lift-reset” cycle for three colored cubes strictly in the order of 𝑅𝑒𝑑 → 𝐺𝑟𝑒𝑒𝑛 → 𝐵𝑙𝑢𝑒.
3. **Counting & Latency:** A signal lamp flashes twice with a randomized interval. The agent must count these pulses and push the target only after the second flash. 
4. **Identity Tracking:** Three visually identical red blocks are aligned, and an auxiliary arm performs a rapid swap between two of them. The agent is tasked with picking the specific block that was originally in the center. 

## Usage

For detailed instructions on how to set up the environment, load the benchmark, and evaluate your models, please refer to our official GitHub repository:

🔗 **[GitHub Repository: How to use the ManiSkill-Memory-Dependence Benchmark](https://github.com/cytoplastm/VLA_Memory_dependence_benchmark)**

## Citation

If you find this benchmark useful in your research, please cite our paper:

```bibtex
@article{KC-VLA,
  title={Non-Markovian Long-Horizon Robot Manipulation via Keyframe Chaining},
  author={Yipeng Chen and Wentao Tan and Lei Zhu and Fengling Li and Jingjing Li and Guoli Yang and Heng Tao Shen},
  journal={arXiv preprint arXiv},
  year={2026},
}