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PhoneBot mounts an Android smartphone on a robot’s torso and uses it as three things at once: the robot’s outward-facing camera, its body-mounted inertial sensor, and its high-level computing and wireless interface. The phone reads its inertial measurement unit (IMU), processes camera data, runs the walking policy and vision models, then sends joint-position targets over Wi‑Fi to a separate single-board computer (SBC). The SBC communicates with the robot’s 13 actuators. The phone is not wired to the robot hardware.
The important battery qualification is that the published PhoneBot paper does not report smartphone runtime, drain rate, measured power consumption, or power sharing between the handset and robot. It reports only that the complete 1.8 kg platform includes a smartphone, actuators, structure, electronics and a battery. That total mass is not a phone-battery specification.
What the smartphone does in PhoneBot
PhoneBot treats a phone as a robot subsystem rather than as a remote-control accessory. The handset is fixed to the torso, where its camera looks outward and its IMU moves with the robot body. Its processor runs perception and locomotion-policy inference. Wi‑Fi carries commands and telemetry between the handset and a separate SBC, which is responsible for actuator communications.
This arrangement gives the robot a compact sensor-and-computer package without adding a dedicated camera, inertial board, application processor and radio as separate components. It also makes the phone’s physical placement significant: because the phone is attached to the trunk, the measured motion is used as the body motion by the walking controller.
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“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.”
Ruochen Hou, Quanyou Wang, Daniel Koh and Dennis W. Hong, PhoneBot paper, posted to arXiv on October 6, 2026
System roles at a glance
| PhoneBot element | Role | Connection or data |
|---|---|---|
| Smartphone rear camera | Robot vision, object detection, human-pose estimation and visual-inertial localization | Feeds on-device vision pipelines and ARCore visual-inertial SLAM |
| Smartphone IMU | Body angular-velocity and gravity sensing for locomotion | Read by the Android app at the policy rate; the paper identifies a 200 Hz IMU workload label |
| Smartphone CPU or GPU | TensorFlow Lite walking-policy actor and vision inference | Produces joint-position targets and behavior detections |
| Phone Wi‑Fi interface | Wireless command and telemetry link | UDP packets to and from the SBC |
| Onboard SBC | Actuator communications and command application | Forwards targets to the XL430 actuator bus |
| 13 actuators | Robot joint movement | DYNAMIXEL XL430-W250-T actuators are specified for all 13 joints |
How the camera is used
The rear camera is the robot’s visual sensor. The Android application performs object detection and human-pose estimation on the device with ML Kit and MediaPipe. Those detections support behaviors such as following a person and look-at actions. In other words, the camera is not merely recording video for a remote operator; its output is converted into information that changes the robot’s behavior.
Person following and look-at behavior
Object detection can identify a person or another target in the camera view, while pose estimation supplies body landmarks. PhoneBot can use those results to keep a person in view, follow that person, or orient its upper body toward a subject. The paper describes a torso yaw joint that lets the phone and camera rotate independently of the robot’s stance direction. This allows the robot to turn its view while its feet remain oriented elsewhere.
Camera plus IMU localization
The camera and IMU also feed ARCore visual-inertial simultaneous localization and mapping (SLAM). Visual features from the camera are combined with inertial measurements to estimate the phone’s motion and support onboard localization. Because the phone is rigidly mounted to the torso, this estimate is useful for the robot’s body frame. ARCore support is a separate compatibility question from basic camera or IMU access; a phone can complete the application’s other workloads without necessarily providing working ARCore services.
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Why mounting angle matters
The camera’s usefulness depends on where the torso mount points it. A mount that is loose, tilted unexpectedly or able to vibrate changes the relationship between the camera, IMU and robot body. PhoneBot’s design makes that relationship intentional: the handset is fixed to the trunk, and the yaw joint provides controlled reorientation. Anyone reproducing the arrangement should treat the mount as part of the sensor calibration, not as a cosmetic bracket.
How the phone’s sensors control walking
The phone’s IMU supplies the body-motion information used by the locomotion policy. The Android application reads the IMU at the policy rate. The walking-policy input includes angular velocity and projected gravity from the phone, joint state received from the motors, and user commands.
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Meaning of the main inputs
- Angular velocity: how quickly the torso is rotating around its axes.
- Projected gravity: the gravity direction expressed in the phone or body frame, providing an estimate of body attitude relative to vertical.
- Joint state: feedback from the actuators, sent back through the SBC and Wi‑Fi link.
- User commands: the requested locomotion or interaction action.
These inputs are evaluated by a TensorFlow Lite actor running on the phone’s CPU or GPU. The actor produces joint-position targets rather than directly driving motor electronics. The separation matters: the phone decides what positions the joints should seek, while the SBC handles the low-level hardware connection.
What the 200 Hz figure means
The paper’s CPU-usage table labels an IMU workload at 200 Hz. That is a sensing or processing-rate label for the evaluated application workload. It is not a claim that the phone battery lasts a particular number of hours, and it is not a measurement of electrical power. The published CPU-use values for the tested phones should likewise be read as processor utilization figures, not battery-consumption figures.
How commands travel from the phone to the motors
PhoneBot divides high-level computation from actuator communication. The complete loop is:
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- Sense: the phone reads its IMU and captures camera frames. Motor feedback and user commands are also made available to the application.
- Infer: TensorFlow Lite runs the locomotion actor on the handset CPU or GPU. Vision pipelines run object detection, pose estimation and, where supported, ARCore SLAM.
- Generate targets: the application converts the policy output into joint-position targets.
- Transmit: the phone sends those targets over Wi‑Fi using UDP. Telemetry travels back over the same wireless relationship.
- Apply: the SBC receives the packets, communicates with the actuators and applies the requested joint positions.
- Move and report: the motors move, return joint state and close the next sensing-and-control cycle.
The handset has no physical electrical connection to the robot hardware. A Wi‑Fi failure therefore interrupts the command path without creating a direct wired fallback between the phone and actuator bus. The SBC remains the hardware-facing controller in this architecture.
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The paper states that the assembled robot weighs 1.8 kg, including the smartphone, actuators, structural parts, electronics and battery. That is the only quantified battery-related system figure in the material described here. It identifies the battery as part of the complete robot’s mass; it does not identify the battery capacity, voltage, runtime or charging method.
No published smartphone-runtime number
There is no reported phone-battery runtime, drain rate, average or peak handset power, or test showing how long a particular model operates before recharging. The paper also does not provide a battery-life comparison among the four phones it tested. A 200 Hz IMU workload and CPU-use measurements cannot be converted into runtime without battery capacity, display state, radio conditions, thermal behavior and the application’s complete power profile.
No documented power-sharing path
Because the phone has no physical electrical connection to the robot hardware, the published architecture does not document the handset supplying power to the actuators or other robot electronics. The robot’s battery is listed among the assembled platform components, but the reviewed description does not say whether it powers only the robot hardware, how it is regulated, or whether any charging arrangement is shared with the handset. Do not infer phone endurance or electrical power sharing from the 1.8 kg mass figure.
Practical implication for a build
A reproduction plan needs two separate power questions: how the robot’s actuator and SBC electronics are powered, and how the phone is charged during operation. The paper establishes the wireless separation but leaves the power budget and charging procedure for the builder to determine. Any claimed endurance should be measured on the exact phone, camera workload, network conditions and robot battery used.
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Which phones were tested
The researchers ran the PhoneBot Android application on four devices:
| Tested handset | Application result reported | Qualification |
|---|---|---|
| Honor 9 (2017) | IMU sensing, UDP motor communication, policy inference, on-device vision and human-robot interaction completed | ARCore SLAM did not work in the tested United States setup because Google services were unavailable on that Huawei device |
| moto g 2025 | Completed the listed application workloads | One demonstrated device, not an exclusive compatibility guarantee |
| Galaxy A16 5G | Completed the listed application workloads | One demonstrated device, not an exclusive compatibility guarantee |
| moto g 2024 | Completed the listed application workloads | One demonstrated device, not an exclusive compatibility guarantee |
All four examples completed IMU sensing, UDP motor communication, policy inference, on-device vision and human-robot interaction in the reported evaluation. The list is evidence that the application ran on those devices; it is not an exhaustive Android compatibility matrix. The Honor 9 result also shows why ARCore availability must be checked separately from general Android support.
How to choose a handset for reproduction
- Android application support: verify that the phone can run the PhoneBot application and its required TensorFlow Lite, ML Kit and MediaPipe components.
- ARCore availability: check support for the intended region and Google-services configuration if visual-inertial SLAM is required.
- Compute capability: the phone must handle policy inference and vision workloads concurrently at the tested operating rates.
- Camera access: confirm that the rear camera can provide the view and frame behavior needed by the perception pipeline.
- Mechanical fit: the handset must be rigidly secured to the torso and aligned with the intended body frame.
- Measured endurance: test battery life yourself; the paper does not rank the four devices by runtime.
Hardware and software pieces a builder must account for
The documented platform combines a phone with a robot body and a separate controller. The paper specifies 13 DYNAMIXEL XL430-W250-T actuators, a torso yaw joint, an SBC and the phone. It does not name the SBC model in the reviewed sections, so a specific board should not be treated as a published requirement.
Physical integration checklist
- Rigid torso mount for the phone, with the camera facing the intended scene.
- Controlled yaw mechanism so the camera can reorient independently of the stance direction.
- Thirteen actuator channels using the specified XL430-W250-T hardware.
- Separate SBC connection to the actuator bus.
- Robot battery and regulation sized for the actuators and SBC; the paper does not publish the electrical ratings.
- Reliable Wi‑Fi coverage between the handset and SBC.
Software data path
- Android application for IMU sampling, camera processing, policy inference and UDP networking.
- TensorFlow Lite actor executing on the phone CPU or GPU.
- ML Kit and MediaPipe for object detection and human-pose estimation.
- ARCore visual-inertial SLAM when the device and regional services support it.
- SBC software that receives UDP targets, forwards them to the actuator bus and returns joint state.
The paper demonstrates the integrated behavior but does not publish a universal installation recipe, SBC model, network configuration template or battery-management procedure. Those details must be selected and validated for a particular build rather than assumed from the four phone demonstrations.
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Failure modes and useful diagnostics
The robot does not move after a command
First separate wireless and actuator faults. Confirm that the phone and SBC are on the same Wi‑Fi network and that UDP packets are reaching the SBC. Then check whether the SBC is receiving joint feedback and whether it can communicate with the XL430 actuator bus. A phone-side policy result without actuator-bus feedback indicates a problem after inference, not with the camera or IMU.
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Walking is unstable or body attitude is wrong
Inspect the phone mount and its orientation. Since the phone IMU is treated as the body IMU, a loose mount or changed angle alters angular-velocity and projected-gravity readings. Also verify that joint state is arriving at the phone; the locomotion policy uses both inertial inputs and motor feedback.
Following or look-at behavior fails
Check camera framing, lighting and whether ML Kit or MediaPipe detections are being produced. If the phone can run the walking policy but cannot localize, test ARCore support independently. The Honor 9 evaluation demonstrates that general application operation can succeed even when ARCore SLAM is unavailable in a particular regional services configuration.
The phone gets hot or loses charge quickly
Do not interpret the 200 Hz label or CPU-use table as a battery estimate. Record the handset model, battery state, screen and radio settings, camera and vision workload, ambient temperature and operating time in a controlled test. The published paper supplies no runtime baseline against which to compare that measurement.
What PhoneBot demonstrates overall
PhoneBot’s central design decision is to put sensing and high-level intelligence on a device many people already own. The camera supplies visual detections and, with the IMU, localization. The IMU supplies body-motion features to the walking policy. The phone’s processor runs the actor and perception models. Wi‑Fi carries the resulting targets to an SBC that remains responsible for the actuator bus.
The design is therefore best understood as a wireless, phone-based robot-computing architecture—not as a claim that a smartphone battery powers the robot or that every Android handset will support every feature. The published evidence establishes the camera, IMU, computation and networking roles, a 1.8 kg complete-platform mass, and four tested phones. It does not establish handset battery endurance, robot power-sharing details or an exhaustive device-compatibility list.
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