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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Unit tests for the RoboTwin 2.0 Gymnasium wrapper.
These tests mock out the SAPIEN-based RoboTwin runtime (task modules +
YAML config loader) so they run without the full RoboTwin installation
(SAPIEN, CuRobo, mplib, asset downloads, etc.).
"""
from __future__ import annotations
from contextlib import contextmanager
from unittest.mock import MagicMock, patch
import gymnasium as gym
import numpy as np
import pytest
from lerobot.envs.robotwin import (
ACTION_DIM,
ROBOTWIN_CAMERA_NAMES,
ROBOTWIN_TASKS,
RoboTwinEnv,
create_robotwin_envs,
)
# ---------------------------------------------------------------------------
# Fixtures / helpers
# ---------------------------------------------------------------------------
def _make_mock_task_env(
height: int = 240,
width: int = 320,
cameras: tuple[str, ...] = ROBOTWIN_CAMERA_NAMES,
) -> MagicMock:
"""Return a mock that mimics the RoboTwin task class API.
RoboTwin's real get_obs returns
{"observation": {cam: {"rgb": img}}, "joint_action": {"vector": np.ndarray}, ...}
so the mock follows the same nested shape.
"""
obs_dict = {
"observation": {cam: {"rgb": np.zeros((height, width, 3), dtype=np.uint8)} for cam in cameras},
"joint_action": {"vector": np.zeros(ACTION_DIM, dtype=np.float32)},
"endpose": {},
}
mock = MagicMock()
mock.get_obs.return_value = obs_dict
mock.setup_demo.return_value = None
mock.take_action.return_value = None
mock.eval_success = False
mock.check_success.return_value = False
mock.close_env.return_value = None
return mock
@contextmanager
def _patch_runtime(mock_task_instance: MagicMock):
"""Patch both the task-class loader and the YAML config loader so the
env can construct + reset without a real RoboTwin install."""
task_cls = MagicMock(return_value=mock_task_instance)
fake_setup = {
"head_camera_h": 240,
"head_camera_w": 320,
"left_embodiment_config": {},
"right_embodiment_config": {},
"left_robot_file": "",
"right_robot_file": "",
"dual_arm_embodied": True,
"render_freq": 0,
"task_name": "beat_block_hammer",
"task_config": "demo_clean",
}
with (
patch("lerobot.envs.robotwin._load_robotwin_task", return_value=task_cls),
patch("lerobot.envs.robotwin._load_robotwin_setup_kwargs", return_value=fake_setup),
):
yield
# ---------------------------------------------------------------------------
# RoboTwinEnv unit tests
# ---------------------------------------------------------------------------
class TestRoboTwinEnv:
def test_observation_space_shape(self):
"""observation_space should have the configured h×w×3 for every camera."""
h, w = 240, 320
env = RoboTwinEnv(
task_name="beat_block_hammer",
observation_height=h,
observation_width=w,
camera_names=["head_camera", "left_camera"],
)
pixels_space = env.observation_space["pixels"]
assert pixels_space["head_camera"].shape == (h, w, 3)
assert pixels_space["left_camera"].shape == (h, w, 3)
assert "right_camera" not in pixels_space
def test_action_space(self):
env = RoboTwinEnv(task_name="beat_block_hammer")
assert env.action_space.shape == (ACTION_DIM,)
assert env.action_space.dtype == np.float32
def test_reset_returns_correct_obs_keys(self):
mock_task = _make_mock_task_env()
env = RoboTwinEnv(task_name="beat_block_hammer")
with _patch_runtime(mock_task):
obs, info = env.reset()
assert "pixels" in obs
for cam in ROBOTWIN_CAMERA_NAMES:
assert cam in obs["pixels"], f"Missing camera '{cam}' in obs"
assert "agent_pos" in obs
assert obs["agent_pos"].shape == (ACTION_DIM,)
assert info["is_success"] is False
def test_reset_calls_setup_demo(self):
mock_task = _make_mock_task_env()
env = RoboTwinEnv(task_name="beat_block_hammer")
with _patch_runtime(mock_task):
env.reset(seed=42)
# setup_demo receives the full YAML-derived kwargs plus seed + is_test;
# we only assert the caller-provided bits.
assert mock_task.setup_demo.call_count == 1
call_kwargs = mock_task.setup_demo.call_args.kwargs
assert call_kwargs["seed"] == 42
assert call_kwargs["is_test"] is True
def test_step_returns_correct_types(self):
mock_task = _make_mock_task_env()
env = RoboTwinEnv(task_name="beat_block_hammer")
action = np.zeros(ACTION_DIM, dtype=np.float32)
with _patch_runtime(mock_task):
env.reset()
obs, reward, terminated, truncated, info = env.step(action)
assert isinstance(obs, dict)
assert isinstance(reward, float)
assert isinstance(terminated, bool)
assert isinstance(truncated, bool)
assert isinstance(info, dict)
def test_step_wrong_action_shape_raises(self):
mock_task = _make_mock_task_env()
env = RoboTwinEnv(task_name="beat_block_hammer")
bad_action = np.zeros(7, dtype=np.float32) # wrong dim
with _patch_runtime(mock_task):
env.reset()
with pytest.raises(ValueError, match="Expected 1-D action"):
env.step(bad_action)
def test_success_terminates_episode(self):
mock_task = _make_mock_task_env()
mock_task.check_success.return_value = True
env = RoboTwinEnv(task_name="beat_block_hammer")
action = np.zeros(ACTION_DIM, dtype=np.float32)
with _patch_runtime(mock_task):
env.reset()
_, _, terminated, _, info = env.step(action)
assert terminated is True
assert info["is_success"] is True
def test_truncation_after_episode_length(self):
mock_task = _make_mock_task_env()
env = RoboTwinEnv(task_name="beat_block_hammer", episode_length=2)
action = np.zeros(ACTION_DIM, dtype=np.float32)
with _patch_runtime(mock_task):
env.reset()
env.step(action) # step 1
_, _, _, truncated, _ = env.step(action) # step 2 → truncated
assert truncated is True
def test_close_calls_close_env(self):
mock_task = _make_mock_task_env()
env = RoboTwinEnv(task_name="beat_block_hammer")
with _patch_runtime(mock_task):
env.reset()
env.close()
mock_task.close_env.assert_called_once()
def test_black_frame_for_missing_camera(self):
"""If a camera key is absent from get_obs(), a black frame is returned."""
# Mock exposes only head_camera; we ask for both head_camera + left_camera.
mock_task = _make_mock_task_env(height=10, width=10, cameras=("head_camera",))
env = RoboTwinEnv(
task_name="beat_block_hammer",
camera_names=["head_camera", "left_camera"],
observation_height=10,
observation_width=10,
)
with _patch_runtime(mock_task):
obs, _ = env.reset()
assert obs["pixels"]["left_camera"].shape == (10, 10, 3)
assert obs["pixels"]["left_camera"].sum() == 0
def test_task_and_task_description_attributes(self):
env = RoboTwinEnv(task_name="beat_block_hammer")
assert env.task == "beat_block_hammer"
assert isinstance(env.task_description, str)
def test_deferred_init_env_is_none_before_reset(self):
env = RoboTwinEnv(task_name="beat_block_hammer")
assert env._env is None # noqa: SLF001 (testing internal state)
# ---------------------------------------------------------------------------
# create_robotwin_envs tests
# ---------------------------------------------------------------------------
class TestCreateRoboTwinEnvs:
def test_returns_correct_structure(self):
mock_task = _make_mock_task_env()
with _patch_runtime(mock_task):
envs = create_robotwin_envs(
task="beat_block_hammer",
n_envs=1,
env_cls=gym.vector.SyncVectorEnv,
)
assert "beat_block_hammer" in envs
assert 0 in envs["beat_block_hammer"]
assert isinstance(envs["beat_block_hammer"][0], gym.vector.SyncVectorEnv)
def test_multi_task(self):
mock_task = _make_mock_task_env()
with _patch_runtime(mock_task):
envs = create_robotwin_envs(
task="beat_block_hammer,click_bell",
n_envs=1,
env_cls=gym.vector.SyncVectorEnv,
)
assert set(envs.keys()) == {"beat_block_hammer", "click_bell"}
def test_unknown_task_raises(self):
with pytest.raises(ValueError, match="Unknown RoboTwin tasks"):
create_robotwin_envs(
task="not_a_real_task",
n_envs=1,
env_cls=gym.vector.SyncVectorEnv,
)
def test_invalid_n_envs_raises(self):
with pytest.raises(ValueError, match="n_envs must be a positive int"):
create_robotwin_envs(
task="beat_block_hammer",
n_envs=0,
env_cls=gym.vector.SyncVectorEnv,
)
# ---------------------------------------------------------------------------
# ROBOTWIN_TASKS list
# ---------------------------------------------------------------------------
def test_task_list_not_empty():
assert len(ROBOTWIN_TASKS) >= 50
def test_all_tasks_are_strings():
assert all(isinstance(t, str) and t for t in ROBOTWIN_TASKS)
def test_no_duplicate_tasks():
assert len(ROBOTWIN_TASKS) == len(set(ROBOTWIN_TASKS))
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