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#!/usr/bin/env python
# Copyright 2024 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.
import time
from dataclasses import replace
import torch
from stretch_body.gamepad_teleop import GamePadTeleop
from stretch_body.robot import Robot as StretchAPI
from stretch_body.robot_params import RobotParams
from lerobot.common.robot_devices.robots.configs import StretchRobotConfig
class StretchRobot(StretchAPI):
"""Wrapper of stretch_body.robot.Robot"""
def __init__(self, config: StretchRobotConfig | None = None, **kwargs):
super().__init__()
if config is None:
self.config = StretchRobotConfig(**kwargs)
else:
# Overwrite config arguments using kwargs
self.config = replace(config, **kwargs)
self.robot_type = self.config.type
self.cameras = self.config.cameras
self.is_connected = False
self.teleop = None
self.logs = {}
# TODO(aliberts): test this
RobotParams.set_logging_level("WARNING")
RobotParams.set_logging_formatter("brief_console_formatter")
self.state_keys = None
self.action_keys = None
def connect(self) -> None:
self.is_connected = self.startup()
if not self.is_connected:
print("Another process is already using Stretch. Try running 'stretch_free_robot_process.py'")
raise ConnectionError()
for name in self.cameras:
self.cameras[name].connect()
self.is_connected = self.is_connected and self.cameras[name].is_connected
if not self.is_connected:
print("Could not connect to the cameras, check that all cameras are plugged-in.")
raise ConnectionError()
self.run_calibration()
def run_calibration(self) -> None:
if not self.is_homed():
self.home()
def teleop_step(
self, record_data=False
) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
# TODO(aliberts): return ndarrays instead of torch.Tensors
if not self.is_connected:
raise ConnectionError()
if self.teleop is None:
self.teleop = GamePadTeleop(robot_instance=False)
self.teleop.startup(robot=self)
before_read_t = time.perf_counter()
state = self.get_state()
action = self.teleop.gamepad_controller.get_state()
self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
before_write_t = time.perf_counter()
self.teleop.do_motion(robot=self)
self.push_command()
self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
if self.state_keys is None:
self.state_keys = list(state)
if not record_data:
return
state = torch.as_tensor(list(state.values()))
action = torch.as_tensor(list(action.values()))
# Capture images from cameras
images = {}
for name in self.cameras:
before_camread_t = time.perf_counter()
images[name] = self.cameras[name].async_read()
images[name] = torch.from_numpy(images[name])
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
# Populate output dictionaries
obs_dict, action_dict = {}, {}
obs_dict["observation.state"] = state
action_dict["action"] = action
for name in self.cameras:
obs_dict[f"observation.images.{name}"] = images[name]
return obs_dict, action_dict
def get_state(self) -> dict:
status = self.get_status()
return {
"head_pan.pos": status["head"]["head_pan"]["pos"],
"head_tilt.pos": status["head"]["head_tilt"]["pos"],
"lift.pos": status["lift"]["pos"],
"arm.pos": status["arm"]["pos"],
"wrist_pitch.pos": status["end_of_arm"]["wrist_pitch"]["pos"],
"wrist_roll.pos": status["end_of_arm"]["wrist_roll"]["pos"],
"wrist_yaw.pos": status["end_of_arm"]["wrist_yaw"]["pos"],
"gripper.pos": status["end_of_arm"]["stretch_gripper"]["pos"],
"base_x.vel": status["base"]["x_vel"],
"base_y.vel": status["base"]["y_vel"],
"base_theta.vel": status["base"]["theta_vel"],
}
def capture_observation(self) -> dict:
# TODO(aliberts): return ndarrays instead of torch.Tensors
before_read_t = time.perf_counter()
state = self.get_state()
self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
if self.state_keys is None:
self.state_keys = list(state)
state = torch.as_tensor(list(state.values()))
# Capture images from cameras
images = {}
for name in self.cameras:
before_camread_t = time.perf_counter()
images[name] = self.cameras[name].async_read()
images[name] = torch.from_numpy(images[name])
self.logs[f"read_camera_{name}_dt_s"] = self.cameras[name].logs["delta_timestamp_s"]
self.logs[f"async_read_camera_{name}_dt_s"] = time.perf_counter() - before_camread_t
# Populate output dictionaries
obs_dict = {}
obs_dict["observation.state"] = state
for name in self.cameras:
obs_dict[f"observation.images.{name}"] = images[name]
return obs_dict
def send_action(self, action: torch.Tensor) -> torch.Tensor:
# TODO(aliberts): return ndarrays instead of torch.Tensors
if not self.is_connected:
raise ConnectionError()
if self.teleop is None:
self.teleop = GamePadTeleop(robot_instance=False)
self.teleop.startup(robot=self)
if self.action_keys is None:
dummy_action = self.teleop.gamepad_controller.get_state()
self.action_keys = list(dummy_action.keys())
action_dict = dict(zip(self.action_keys, action.tolist(), strict=True))
before_write_t = time.perf_counter()
self.teleop.do_motion(state=action_dict, robot=self)
self.push_command()
self.logs["write_pos_dt_s"] = time.perf_counter() - before_write_t
# TODO(aliberts): return action_sent when motion is limited
return action
def print_logs(self) -> None:
pass
# TODO(aliberts): move robot-specific logs logic here
def teleop_safety_stop(self) -> None:
if self.teleop is not None:
self.teleop._safety_stop(robot=self)
def disconnect(self) -> None:
self.stop()
if self.teleop is not None:
self.teleop.gamepad_controller.stop()
self.teleop.stop()
if len(self.cameras) > 0:
for cam in self.cameras.values():
cam.disconnect()
self.is_connected = False
def __del__(self):
self.disconnect()
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