Trace Shape Sequence
====================

.. admonition:: Language Instruction

   Watch the red cube trace a sequence of shapes. When the lamp turns green, pick up the green cube and trace the same sequence in order. After finishing all shapes, press the button to submit your answer.

.. admonition:: Task Description
   :class: tip

   Long-horizon demonstration imitation: watch and reproduce a sequence of traced shapes.

.. image:: ../_static/videos/trace_shape_seq.gif
   :alt: Render preview for Trace Shape Sequence
   :width: 720px
   :align: center

Source
------

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

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

Sequence variants require retaining order and path geometry.

Variants
--------

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

   * - Env ID
     - Horizon
     - Data Source
   * - ``TraceShapeSeqEasy-VLA-v0``
     - 1500
     - MP
   * - ``TraceShapeSeqHard-VLA-v0``
     - 1500
     - MP
   * - ``TraceShapeSeqMedium-VLA-v0``
     - 1500
     - 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(
       "TraceShapeSeqEasy-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 TraceShapeSeqEasy-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 TraceShapeSeqEasy-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.