PhoneBot mounts an Android smartphone on a robot torso and uses it as the robot’s camera, inertial-sensing unit, wireless interface, and high-level computer. The rear camera handles on-device detection, pose estimation, and visual-inertial localization; the phone’s IMU supplies motion data to the walking policy; and the handset runs policy inference before sending joint targets over Wi-Fi to a separate single-board computer (SBC).
The PhoneBot paper does not establish smartphone battery runtime, drain, or power sharing. It reports a 1.8 kg assembled robot that includes a battery, but the phone has no physical electrical connection to the robot hardware, so that figure must not be interpreted as a phone-battery specification.
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What the smartphone does in PhoneBot
PhoneBot treats the handset as an onboard robot subsystem rather than as a remote-control screen. It is fixed to the torso, where its camera and inertial measurement unit (IMU) move with the robot body. An Android application reads those sensors, runs machine-learning workloads, exchanges data with the motor controller, and presents the user-command interface.
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A separate SBC remains responsible for low-level actuator communication. The phone and robot hardware are not connected by a cable: the Android application sends commands and receives telemetry over Wi-Fi. This division lets the phone provide perception and high-level decision-making while the SBC handles the timing-sensitive actuator bus.
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| Phone function | How PhoneBot uses it | What remains outside the phone |
|---|---|---|
| Rear camera | On-device object detection, human pose estimation, visual tracking, and input to ARCore visual-inertial SLAM | Mechanical camera aiming is provided by the robot’s torso yaw joint |
| IMU | Supplies angular velocity and projected gravity to the locomotion policy | Joint state comes from the actuators through the SBC |
| CPU or GPU | Runs the TensorFlow Lite walking-policy actor and vision workloads | Actuator-bus communication and command forwarding are performed by the SBC |
| Wi-Fi radio | Carries UDP joint-position targets and telemetry between the phone and SBC | The SBC forwards targets to the motor bus |
| Battery | The paper does not quantify phone runtime, drain, or power sharing | The assembled robot includes a battery, but its relationship to the handset battery is not specified |
How commands and sensor data travel through the robot
The control loop is easier to understand as a sequence of handoffs. The phone does not directly drive a motor winding or replace the actuator controller.
- Sense the body: The Android application samples the phone’s IMU at the policy rate. The phone is attached to the torso, so the measured motion represents the robot body rather than a handheld operator’s motion.
- Collect robot state: Motor-related joint state arrives from the actuator system through the SBC and Wi-Fi link. The walking policy therefore receives both phone motion data and robot-joint feedback.
- Add commands: User commands are included with angular velocity, projected gravity, and joint state as policy inputs.
- Infer a motion action: A TensorFlow Lite actor runs on the phone’s CPU or GPU and produces joint-position targets.
- Transmit targets: The app sends those targets to the SBC using UDP over Wi-Fi.
- Drive the actuators: The SBC forwards the commands to the DYNAMIXEL XL430-W250-T actuator bus used by all 13 joints.
This architecture puts perception and policy inference close to the sensors while preserving a dedicated controller for actuator communications. A Wi-Fi interruption therefore affects command and telemetry exchange, whereas the phone’s camera and IMU can still be operating locally.
How PhoneBot uses the camera
On-device detection and pose estimation
The rear camera runs object detection and human pose estimation on the handset. The paper identifies Google ML Kit and MediaPipe as the software used for these workloads. The resulting detections support behaviors such as following a person and looking toward a person or other target.
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Visual-inertial localization
The camera and IMU also feed ARCore visual-inertial simultaneous localization and mapping (SLAM). Combining images with inertial measurements allows the system to estimate the robot’s motion and position relative to its surroundings while it operates onboard.
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SLAM support depends on both the Android device and the software environment. In the tested setup, ARCore SLAM did not work on the 2017 Honor 9 because Google services were unavailable on that Huawei device in the United States. That result identifies a compatibility failure in the tested environment, not a universal statement that every function of the phone application fails on that model.
Independent camera reorientation
PhoneBot includes a torso yaw joint. The joint can rotate the mounted phone and its camera independently of the robot’s stance direction. That mechanical degree of freedom lets the robot keep looking at a person or another target while its feet and body remain oriented for locomotion. It also allows deliberate camera reorientation for active visual tracking.
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How the phone’s IMU contributes to walking
The IMU is not an accessory used only for logging. Since the handset is fixed to the trunk, its inertial data represents the body frame used by the locomotion controller. The authors state: “The phone is fixed to the trunk, so its IMU is the body IMU used by the locomotion policies, and its camera is the robot camera.”
The policy input includes two IMU-derived quantities:
- Angular velocity: rotational motion of the torso.
- Projected gravity: the gravity direction expressed in the policy’s body frame, which helps indicate body orientation and tilt.
Those signals are combined with joint state received from the motors and with user commands. The policy can therefore react to body movement and actuator configuration rather than generating motion from a command alone.
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The paper’s CPU-usage table labels an IMU workload at 200 Hz. That is a sensing-rate label for the application workload, not a measurement of battery runtime, charging interval, or electrical power draw. A 200 Hz entry should not be used to estimate how long any phone will operate.
What the battery information actually shows
Battery claims require the most careful qualification. The paper reports that the assembled PhoneBot weighs 1.8 kg, including the smartphone, 13 actuators, structural parts, electronics, and a battery. It does not provide the battery’s capacity, voltage, robot runtime, recharge time, measured power consumption, or phone-battery drain.
The handset has no physical electrical connection to the robot hardware. Consequently, the published description does not establish that the phone battery powers the actuators, SBC, or any other robot component. It also does not establish that the robot battery charges the handset. The 1.8 kg total is a platform-mass figure, not evidence of a shared power system.
For a reproduction, treat the two power questions separately:
- Robot power: Select and wire the battery required by the actuator bus, SBC, and other robot electronics according to the builder’s electrical design. PhoneBot’s paper does not publish enough electrical detail to calculate that system from the 1.8 kg figure.
- Phone power: Provide whatever charging and runtime arrangement is needed for the chosen Android handset. The PhoneBot paper gives no endurance comparison among the tested phones.
CPU-use numbers in the paper are also not substitutes for battery measurements. CPU utilization varies with workload, thermal state, operating-system scheduling, camera resolution, radio conditions, and device hardware. Without a measured drain rate and test protocol, it is not valid to rank the four phones by endurance.
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Which phones were tested
The authors ran the Android application on four handsets. All four completed the core set of functions reported in the paper: IMU sensing, UDP motor communication, policy inference, on-device vision, and human-robot interaction.
| Tested handset | Reported result | Qualification |
|---|---|---|
| 2017 Honor 9 | Core application functions completed | ARCore SLAM did not work in the tested United States setup because Google services were unavailable on that Huawei device |
| moto g 2025 | Core application functions completed | One tested device, not a guarantee for every regional variant |
| Galaxy A16 5G | Core application functions completed | One tested device, not an exhaustive compatibility claim |
| moto g 2024 | Core application functions completed | One tested device, not an exhaustive compatibility claim |
These examples demonstrate that the application ran across four Android phones; they are not an exclusive supported-device list. Model names can conceal regional, firmware, camera, or Google-service differences, so a different variant should be checked rather than assumed equivalent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to specify when building a similar setup
A phone-based build should be planned around the interfaces documented by the PhoneBot authors.
- Mounting: Fix the handset securely to the torso so the camera view and IMU body frame remain consistent with the locomotion policy.
- Android support: Verify that the phone can run the application, TensorFlow Lite inference, ML Kit, MediaPipe, and any required ARCore features.
- Camera: Confirm that the rear camera is available to the app and has the desired field of view for detection and tracking.
- Compute: Check that the CPU or GPU can execute the policy and vision workloads concurrently at the intended rates.
- Networking: Provide a reliable Wi-Fi path for UDP commands and telemetry between the handset and SBC.
- Actuation: Use a controller capable of forwarding position targets to the 13-joint actuator system; the paper names DYNAMIXEL XL430-W250-T actuators.
- Power: Design the robot battery system independently from the phone’s charging arrangement because the paper does not document electrical sharing.
- Mechanical yaw: If active camera tracking is important, account for the torso yaw joint that lets the camera turn independently of the stance.
Failure modes and practical checks
ARCore does not initialize
Check Google-service availability, ARCore support for the exact device and region, camera permissions, and the phone’s firmware. The Honor 9 result shows why a phone can run the main application while still failing the SLAM portion in a particular environment.
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Verify that the phone and SBC are on the intended Wi-Fi network, that the UDP destination and port match the controller, and that the SBC is receiving telemetry. The phone sends joint-position targets; it does not replace the SBC’s actuator-bus forwarding.
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Walking becomes unstable
Confirm that the handset has not shifted in its mount, because a changed phone orientation changes the relationship between measured gravity, angular velocity, and the policy’s assumed body frame. Also check that joint state is arriving correctly and that the IMU is sampled at the intended policy rate.
The phone overheats or loses charge quickly
Do not infer a cause from the 200 Hz workload label or from CPU-use figures. Those values are not battery measurements. Measure the selected phone under the complete camera, IMU, inference, and Wi-Fi workload, and provide a separate charging plan if the robot’s operating session exceeds the handset’s measured endurance.
What is established—and what is not
The documented design establishes a clear division of labor: the torso-mounted phone supplies camera perception, body IMU data, high-level policy inference, and wireless communication; the SBC supplies actuator-bus control; and the robot’s mechanical system supplies the joints and torso yaw motion.
It does not establish a phone-battery runtime, a battery-sharing circuit, a universal Android compatibility list, or an endurance ranking among the four tested models. Those details require device-specific electrical and performance measurements that are not reported in the PhoneBot paper posted by Ruochen Hou, Quanyou Wang, Daniel Koh, and Dennis W. Hong on October 6, 2026.
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