Timed Transfer#

Language Instructions

When the white lamp turns green, start counting steps from that exact moment. Move the blue cube from the green disc to the red disc exactly on step 300 of that count.
When the white lamp turns green, start counting steps from that exact moment. Move the blue cube from the green disc to the red disc exactly on step 100 of that count.
When the white lamp turns green, start counting steps from that exact moment. Move the blue cube from the green disc to the red disc exactly on step 1000 of that count.
When the white lamp turns green, start counting steps from that exact moment. Move the blue cube from the green disc to the red disc exactly on step 200 of that count.
When the white lamp turns green, start counting steps from that exact moment. Move the blue cube from the green disc to the red disc exactly on step 500 of that count.
When the white lamp turns green, start counting steps from that exact moment. Move the blue cube from the green disc to the red disc exactly on step 150 of that count.

Task Description

Delayed timing: after a lamp cue, move the cube at a precise future timestep.

Source#

Module: mikasa_robo_suite.vla.memory_envs.timed_transfer_vla
Source file: mikasa_robo_suite/vla/memory_envs/timed_transfer_vla.py

Difficulty and Parameters#

Easy, Medium, Hard, and Long variants increase target count and horizon.

Variants#

Env ID	Horizon	Data Source
`TimedTransferEasy-Long-VLA-v0`	600	MP
`TimedTransferEasy-VLA-v0`	200	MP
`TimedTransferHard-Long-VLA-v0`	1200	MP
`TimedTransferHard-VLA-v0`	300	MP
`TimedTransferMedium-Long-VLA-v0`	900	MP
`TimedTransferMedium-VLA-v0`	250	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(
    "TimedTransferEasy-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 TimedTransferEasy-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 TimedTransferEasy-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.