Trace Shape

Language Instruction

Watch the red cube trace a shape on the table. When the lamp turns green, pick up the green cube and trace exactly the same shape.

Task Description

Demonstration imitation: watch a cube trace a shape, then reproduce the path.

Source

• Module: mikasa_robo_suite.vla.memory_envs.trace_shape_vla

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

Difficulty and Parameters

Easy, Medium, and Hard variants increase path complexity.

Variants

Env ID	Horizon	Data Source
TraceShapeEasy-VLA-v0	250	MP
TraceShapeHard-VLA-v0	350	MP
TraceShapeMedium-VLA-v0	300	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(
    "TraceShapeEasy-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 TraceShapeEasy-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 TraceShapeEasy-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.