Bunch Of Colors

Language Instruction

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

Task Description

Set memory: observe a set of colored cubes, then touch all remembered colors in any order and press the center button.

Source

• Module: mikasa_robo_suite.vla.memory_envs.bunch_of_colors_vla

• Source file: mikasa_robo_suite/vla/memory_envs/bunch_of_colors_vla.py

Difficulty and Parameters

Number of colors and Long variants increase capacity and retention time.

Variants

Env ID	Horizon	Data Source
BunchOfColors3-Long-VLA-v0	700	MP
BunchOfColors3-VLA-v0	400	MP
BunchOfColors5-Long-VLA-v0	700	MP
BunchOfColors5-VLA-v0	400	MP
BunchOfColors7-Long-VLA-v0	700	MP
BunchOfColors7-VLA-v0	400	MP

Run Example

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(
    "BunchOfColors3-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:

uv run python mikasa_robo_suite/vla/dataset_collectors/get_mikasa_robo_datasets_motion_planning.py \
  --env-id BunchOfColors3-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:

uv run python utils/prepare_benchmark_demo_videos.py \
  --tasks BunchOfColors3-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.