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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 `RobotClient` action-queue logic (pure Python, no gRPC).
We monkey-patch `lerobot.robots.utils.make_robot_from_config` so that
no real hardware is accessed. Only the queue-update mechanism is verified.
"""
from __future__ import annotations
import time
from queue import Queue
import pytest
import torch
# Skip entire module if grpc is not available
pytest.importorskip("grpc")
# -----------------------------------------------------------------------------
# Test fixtures
# -----------------------------------------------------------------------------
@pytest.fixture()
def robot_client():
"""Fresh `RobotClient` instance for each test case (no threads started).
Uses DummyRobot."""
# Import only when the test actually runs (after decorator check)
from lerobot.async_inference.configs import RobotClientConfig
from lerobot.async_inference.robot_client import RobotClient
from tests.mocks.mock_robot import MockRobotConfig
test_config = MockRobotConfig()
# gRPC channel is not actually used in tests, so using a dummy address
test_config = RobotClientConfig(
robot=test_config,
server_address="localhost:9999",
policy_type="test",
pretrained_name_or_path="test",
actions_per_chunk=20,
)
client = RobotClient(test_config)
# Initialize attributes that are normally set in start() method
client.chunks_received = 0
client.available_actions_size = []
yield client
if client.robot.is_connected:
client.stop()
# -----------------------------------------------------------------------------
# Helper utilities for tests
# -----------------------------------------------------------------------------
def _make_actions(start_ts: float, start_t: int, count: int):
"""Generate `count` consecutive TimedAction objects starting at timestep `start_t`."""
from lerobot.async_inference.helpers import TimedAction
fps = 30 # emulates most common frame-rate
actions = []
for i in range(count):
timestep = start_t + i
timestamp = start_ts + i * (1 / fps)
action_tensor = torch.full((6,), timestep, dtype=torch.float32)
actions.append(TimedAction(action=action_tensor, timestep=timestep, timestamp=timestamp))
return actions
# -----------------------------------------------------------------------------
# Tests
# -----------------------------------------------------------------------------
def test_update_action_queue_discards_stale(robot_client):
"""`_update_action_queue` must drop actions with `timestep` <= `latest_action`."""
# Pretend we already executed up to action #4
robot_client.latest_action = 4
# Incoming chunk contains timesteps 3..7 -> expect 5,6,7 kept.
incoming = _make_actions(start_ts=time.time(), start_t=3, count=5) # 3,4,5,6,7
robot_client._aggregate_action_queues(incoming)
# Extract timesteps from queue
resulting_timesteps = [a.get_timestep() for a in robot_client.action_queue.queue]
assert resulting_timesteps == [5, 6, 7]
@pytest.mark.parametrize(
"weight_old, weight_new",
[
(1.0, 0.0),
(0.0, 1.0),
(0.5, 0.5),
(0.2, 0.8),
(0.8, 0.2),
(0.1, 0.9),
(0.9, 0.1),
],
)
def test_aggregate_action_queues_combines_actions_in_overlap(
robot_client, weight_old: float, weight_new: float
):
"""`_aggregate_action_queues` must combine actions on overlapping timesteps according
to the provided aggregate_fn, here tested with multiple coefficients."""
from lerobot.async_inference.helpers import TimedAction
robot_client.chunks_received = 0
# Pretend we already executed up to action #4, and queue contains actions for timesteps 5..6
robot_client.latest_action = 4
current_actions = _make_actions(
start_ts=time.time(), start_t=5, count=2
) # actions are [torch.ones(6), torch.ones(6), ...]
current_actions = [
TimedAction(action=10 * a.get_action(), timestep=a.get_timestep(), timestamp=a.get_timestamp())
for a in current_actions
]
for a in current_actions:
robot_client.action_queue.put(a)
# Incoming chunk contains timesteps 3..7 -> expect 5,6,7 kept.
incoming = _make_actions(start_ts=time.time(), start_t=3, count=5) # 3,4,5,6,7
overlap_timesteps = [5, 6] # properly tested in test_aggregate_action_queues_discards_stale
nonoverlap_timesteps = [7]
robot_client._aggregate_action_queues(
incoming, aggregate_fn=lambda x1, x2: weight_old * x1 + weight_new * x2
)
queue_overlap_actions = []
queue_non_overlap_actions = []
for a in robot_client.action_queue.queue:
if a.get_timestep() in overlap_timesteps:
queue_overlap_actions.append(a)
elif a.get_timestep() in nonoverlap_timesteps:
queue_non_overlap_actions.append(a)
queue_overlap_actions = sorted(queue_overlap_actions, key=lambda x: x.get_timestep())
queue_non_overlap_actions = sorted(queue_non_overlap_actions, key=lambda x: x.get_timestep())
assert torch.allclose(
queue_overlap_actions[0].get_action(),
weight_old * current_actions[0].get_action() + weight_new * incoming[-3].get_action(),
)
assert torch.allclose(
queue_overlap_actions[1].get_action(),
weight_old * current_actions[1].get_action() + weight_new * incoming[-2].get_action(),
)
assert torch.allclose(queue_non_overlap_actions[0].get_action(), incoming[-1].get_action())
@pytest.mark.parametrize(
"chunk_size, queue_len, expected",
[
(20, 12, False), # 12 / 20 = 0.6 > g=0.5 threshold, not ready to send
(20, 8, True), # 8 / 20 = 0.4 <= g=0.5, ready to send
(10, 5, True),
(10, 6, False),
],
)
def test_ready_to_send_observation(robot_client, chunk_size: int, queue_len: int, expected: bool):
"""Validate `_ready_to_send_observation` ratio logic for various sizes."""
robot_client.action_chunk_size = chunk_size
# Clear any existing actions then fill with `queue_len` dummy entries ----
robot_client.action_queue = Queue()
dummy_actions = _make_actions(start_ts=time.time(), start_t=0, count=queue_len)
for act in dummy_actions:
robot_client.action_queue.put(act)
assert robot_client._ready_to_send_observation() is expected
@pytest.mark.parametrize(
"g_threshold, expected",
[
# The condition is `queue_size / chunk_size <= g`.
# Here, ratio = 6 / 10 = 0.6.
(0.0, False), # 0.6 <= 0.0 is False
(0.1, False),
(0.2, False),
(0.3, False),
(0.4, False),
(0.5, False),
(0.6, True), # 0.6 <= 0.6 is True
(0.7, True),
(0.8, True),
(0.9, True),
(1.0, True),
],
)
def test_ready_to_send_observation_with_varying_threshold(robot_client, g_threshold: float, expected: bool):
"""Validate `_ready_to_send_observation` with fixed sizes and varying `g`."""
# Fixed sizes for this test: ratio = 6 / 10 = 0.6
chunk_size = 10
queue_len = 6
robot_client.action_chunk_size = chunk_size
# This is the parameter we are testing
robot_client._chunk_size_threshold = g_threshold
# Fill queue with dummy actions
robot_client.action_queue = Queue()
dummy_actions = _make_actions(start_ts=time.time(), start_t=0, count=queue_len)
for act in dummy_actions:
robot_client.action_queue.put(act)
assert robot_client._ready_to_send_observation() is expected
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