Take It Back#
Language Instruction
Push the cube onto the red target, and when the target changes color, return the cube to its original position.
Task Description
Spatial restoration: push a cube to a target, then return it to its original position after the target changes.
Source#
Module:
mikasa_robo_suite.vla.memory_envs.take_it_back_vlaSource file:
mikasa_robo_suite/vla/memory_envs/take_it_back_vla.py
Difficulty and Parameters#
Two-stage spatial memory and manipulation.
Variants#
Env ID |
Horizon |
Data Source |
|---|---|---|
|
60 |
PPO |
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(
"TakeItBack-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#
PPO-sourced MIKASA-Robo-90 variants use oracle checkpoints:
uv run python mikasa_robo_suite/vla/dataset_collectors/get_mikasa_robo_datasets.py \
--env-id TakeItBack-VLA-v0 \
--path-to-save-data data_mikasa_robo \
--ckpt-dir . \
--num-train-data 250
Render Videos#
Generate a fresh MP4/GIF render with:
uv run python utils/prepare_benchmark_demo_videos.py \
--tasks TakeItBack-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.