Chain Of Colors
===============

.. admonition:: Language Instruction

   Observe which colored cubes appear during the cue, wait, then touch all of them in the same order as the cubes were shown and press the center button.

.. admonition:: Task Description
   :class: tip

   Ordered sequence memory: observe colors in order, then touch the remembered colors in the same order before submission.

.. image:: ../_static/videos/chain_of_colors.gif
   :alt: Render preview for Chain Of Colors
   :width: 720px
   :align: center

Source
------

- Module: ``mikasa_robo_suite.vla.memory_envs.chain_of_colors_vla``
- Source file: ``mikasa_robo_suite/vla/memory_envs/chain_of_colors_vla.py``

Difficulty and Parameters
-------------------------

Sequence length and Long variants increase order-memory difficulty.

Variants
--------

.. list-table::
   :header-rows: 1
   :widths: 38 12 16

   * - Env ID
     - Horizon
     - Data Source
   * - ``ChainOfColors3-Long-VLA-v0``
     - 800
     - MP
   * - ``ChainOfColors3-VLA-v0``
     - 400
     - MP
   * - ``ChainOfColors5-Long-VLA-v0``
     - 1000
     - MP
   * - ``ChainOfColors5-VLA-v0``
     - 400
     - MP
   * - ``ChainOfColors7-Long-VLA-v0``
     - 1200
     - MP
   * - ``ChainOfColors7-VLA-v0``
     - 400
     - MP


Run Example
-----------

.. code-block:: python

   import gymnasium as gym
   import torch

   import mikasa_robo_suite.vla.memory_envs  # registers VLA env IDs
   from mikasa_robo_suite.vla.utils.apply_wrappers import apply_mikasa_vla_wrappers

   env = gym.make(
       "ChainOfColors3-VLA-v0",
       num_envs=1,
       obs_mode="rgb",
       control_mode="pd_ee_delta_pose",
       render_mode="all",
   )
   env = apply_mikasa_vla_wrappers(env)
   obs, info = env.reset(seed=42)
   for _ in range(env.max_episode_steps):
       action = env.action_space.sample()
       if not torch.is_tensor(action):
           action = torch.as_tensor(action, device=env.unwrapped.device)
       obs, reward, terminated, truncated, info = env.step(action)
   env.close()

Dataset Collection
------------------

Motion-planning MIKASA-Robo-90 variants use planner plus replay collection:

.. code-block:: bash

   uv run python mikasa_robo_suite/vla/dataset_collectors/get_mikasa_robo_datasets_motion_planning.py \
     --env-id ChainOfColors3-VLA-v0 \
     --path-to-save-data data_mikasa_robo \
     --num-train-data 250 \
     --max-attempts 5000 \
     --seed 0

Render Videos
-------------

Generate a fresh MP4/GIF render with:

.. code-block:: bash

   uv run python utils/prepare_benchmark_demo_videos.py \
     --tasks ChainOfColors3-VLA-v0 \
     --output-dir videos/benchmark_demos \
     --max-attempts-per-task 8 \
     --overwrite

Generated media stays under ``videos/`` and should be published deliberately rather than committed by default.