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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.
from collections.abc import Callable
from dataclasses import dataclass, field
import torch
from lerobot.robots.config import RobotConfig
from .constants import (
DEFAULT_FPS,
DEFAULT_INFERENCE_LATENCY,
DEFAULT_OBS_QUEUE_TIMEOUT,
)
# Aggregate function registry for CLI usage
AGGREGATE_FUNCTIONS = {
"weighted_average": lambda old, new: 0.3 * old + 0.7 * new,
"latest_only": lambda old, new: new,
"average": lambda old, new: 0.5 * old + 0.5 * new,
"conservative": lambda old, new: 0.7 * old + 0.3 * new,
}
def get_aggregate_function(name: str) -> Callable[[torch.Tensor, torch.Tensor], torch.Tensor]:
"""Get aggregate function by name from registry."""
if name not in AGGREGATE_FUNCTIONS:
available = list(AGGREGATE_FUNCTIONS.keys())
raise ValueError(f"Unknown aggregate function '{name}'. Available: {available}")
return AGGREGATE_FUNCTIONS[name]
@dataclass
class PolicyServerConfig:
"""Configuration for PolicyServer.
This class defines all configurable parameters for the PolicyServer,
including networking settings and action chunking specifications.
"""
# Networking configuration
host: str = field(default="localhost", metadata={"help": "Host address to bind the server to"})
port: int = field(default=8080, metadata={"help": "Port number to bind the server to"})
# Timing configuration
fps: int = field(default=DEFAULT_FPS, metadata={"help": "Frames per second"})
inference_latency: float = field(
default=DEFAULT_INFERENCE_LATENCY, metadata={"help": "Target inference latency in seconds"}
)
obs_queue_timeout: float = field(
default=DEFAULT_OBS_QUEUE_TIMEOUT, metadata={"help": "Timeout for observation queue in seconds"}
)
def __post_init__(self):
"""Validate configuration after initialization."""
if self.port < 1 or self.port > 65535:
raise ValueError(f"Port must be between 1 and 65535, got {self.port}")
if self.environment_dt <= 0:
raise ValueError(f"environment_dt must be positive, got {self.environment_dt}")
if self.inference_latency < 0:
raise ValueError(f"inference_latency must be non-negative, got {self.inference_latency}")
if self.obs_queue_timeout < 0:
raise ValueError(f"obs_queue_timeout must be non-negative, got {self.obs_queue_timeout}")
@classmethod
def from_dict(cls, config_dict: dict) -> "PolicyServerConfig":
"""Create a PolicyServerConfig from a dictionary."""
return cls(**config_dict)
@property
def environment_dt(self) -> float:
"""Environment time step, in seconds"""
return 1 / self.fps
def to_dict(self) -> dict:
"""Convert the configuration to a dictionary."""
return {
"host": self.host,
"port": self.port,
"fps": self.fps,
"environment_dt": self.environment_dt,
"inference_latency": self.inference_latency,
}
@dataclass
class RobotClientConfig:
"""Configuration for RobotClient.
This class defines all configurable parameters for the RobotClient,
including network connection, policy settings, and control behavior.
"""
# Policy configuration
policy_type: str = field(metadata={"help": "Type of policy to use"})
pretrained_name_or_path: str = field(metadata={"help": "Pretrained model name or path"})
# Robot configuration (for CLI usage - robot instance will be created from this)
robot: RobotConfig = field(metadata={"help": "Robot configuration"})
# Policies typically output K actions at max, but we can use less to avoid wasting bandwidth (as actions
# would be aggregated on the client side anyway, depending on the value of `chunk_size_threshold`)
actions_per_chunk: int = field(metadata={"help": "Number of actions per chunk"})
# Task instruction for the robot to execute (e.g., 'fold my tshirt')
task: str = field(default="", metadata={"help": "Task instruction for the robot to execute"})
# Network configuration
server_address: str = field(default="localhost:8080", metadata={"help": "Server address to connect to"})
# Device configuration
policy_device: str = field(default="cpu", metadata={"help": "Device for policy inference"})
# Control behavior configuration
chunk_size_threshold: float = field(default=0.5, metadata={"help": "Threshold for chunk size control"})
fps: int = field(default=DEFAULT_FPS, metadata={"help": "Frames per second"})
# Aggregate function configuration (CLI-compatible)
aggregate_fn_name: str = field(
default="weighted_average",
metadata={"help": f"Name of aggregate function to use. Options: {list(AGGREGATE_FUNCTIONS.keys())}"},
)
# Debug configuration
debug_visualize_queue_size: bool = field(
default=False, metadata={"help": "Visualize the action queue size"}
)
@property
def environment_dt(self) -> float:
"""Environment time step, in seconds"""
return 1 / self.fps
def __post_init__(self):
"""Validate configuration after initialization."""
if not self.server_address:
raise ValueError("server_address cannot be empty")
if not self.policy_type:
raise ValueError("policy_type cannot be empty")
if not self.pretrained_name_or_path:
raise ValueError("pretrained_name_or_path cannot be empty")
if not self.policy_device:
raise ValueError("policy_device cannot be empty")
if self.chunk_size_threshold < 0 or self.chunk_size_threshold > 1:
raise ValueError(f"chunk_size_threshold must be between 0 and 1, got {self.chunk_size_threshold}")
if self.fps <= 0:
raise ValueError(f"fps must be positive, got {self.fps}")
if self.actions_per_chunk <= 0:
raise ValueError(f"actions_per_chunk must be positive, got {self.actions_per_chunk}")
self.aggregate_fn = get_aggregate_function(self.aggregate_fn_name)
@classmethod
def from_dict(cls, config_dict: dict) -> "RobotClientConfig":
"""Create a RobotClientConfig from a dictionary."""
return cls(**config_dict)
def to_dict(self) -> dict:
"""Convert the configuration to a dictionary."""
return {
"server_address": self.server_address,
"policy_type": self.policy_type,
"pretrained_name_or_path": self.pretrained_name_or_path,
"policy_device": self.policy_device,
"chunk_size_threshold": self.chunk_size_threshold,
"fps": self.fps,
"actions_per_chunk": self.actions_per_chunk,
"task": self.task,
"debug_visualize_queue_size": self.debug_visualize_queue_size,
"aggregate_fn_name": self.aggregate_fn_name,
}
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