PhoneBot: A Low-Cost Open Humanoid Robot Platform Reusing Smartphones

Abstract: The adoption of humanoid robots in education and research remains limited by high hardware costs, complex sensing systems, and substantial computational requirements. This paper presents PhoneBot, a low-cost, open-source humanoid robot platform that repurposes commodity smartphones as its primary sensing and computing unit. By using a smartphone's integrated inertial measurement unit (IMU), camera, wireless connectivity, and onboard processing capabilities, PhoneBot reduces hardware costs and simplifies the system architecture. The robot combines a modular lower-body structure driven by 13 low-cost actuators with a torso-mounted smartphone that supports perception, control computation, and user interaction. We describe the mechanical design, software architecture, and real-time communication framework that support stable locomotion and capabilities including vision-based human following, conversational interaction, filming, and mobile telepresence. Experimental evaluations demonstrate reliable walking, perception-driven interaction, and straightforward deployment using off-the-shelf consumer smartphones. With fully open-source hardware and software designs, PhoneBot provides an affordable, reproducible platform for education, research, and rapid prototyping. More details are available at https://phonebot.dev.

• The paper introduces PhoneBot, a low-cost humanoid robot that leverages a smartphone as its primary sensor, computation, and communication hub, resulting in a final cost of approximately $400 (excluding the smartphone).
• PhoneBot achieves stability and effective locomotion on flat terrain through a combination of torque-aware simulation and sagittal mirroring, which substantially improves gait symmetry and reduces learning time.
• The robot's primary cost component is the servos, which are necessary to maintain precise actuation and support human-sized leg structures despite having a consistent mass of up to $400 and sensing mechanisms.

Platform rationale and system contribution

PhoneBot addresses a specific bottleneck in small-scale humanoid robotics: the integration cost of sensing, onboard computation, wireless communication, and user interaction. Rather than treating these capabilities as dedicated robot subsystems, the platform reuses a commodity smartphone as the torso-mounted sensing and computing unit. The smartphone supplies the camera, IMU, wireless interface, display, microphone, speaker, and CPU/GPU resources, while a small onboard SBC provides the electrical interface to the actuator bus. The resulting architecture separates the phone from the robot electrically and exchanges commands and telemetry over Wi-Fi.

This design extends the smartphone-as-robot-computer concept previously demonstrated for wheeled platforms in OpenBot [OpenBot, arXiv identifier not specified in the supplied manuscript] to a bipedal humanoid. The technical claim is not merely that a phone can run perception software, but that its IMU and processor can participate directly in learned locomotion control while its camera supports interaction and localization.

The authors report an assembled height of $483~\mathrm{mm}$, a mass of $1.8~\mathrm{kg}$, and an approximate robot cost of $$400$$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$$3{,}500$to$$13{,}800$$. Its lower mass and reduced actuator count also imply more limited payload and dynamic capability; the cost reduction is therefore achieved partly through a deliberately constrained mechanical envelope.</p> <p><img src="https://images.emergentmind.com/paper-images/2610-08737/Hero2.png" alt="Figure 1" title="" class="markdown-image" loading="lazy"></p> <p><p class="figure-caption">Figure 1: PhoneBot uses a commodity smartphone as its onboard sensing, computing, communication, and interaction device.</p></p> <h2 class='paper-heading' id='mechanical-and-electronic-architecture'>Mechanical and electronic architecture</h2> <p>PhoneBot has 13 actuated DoFs: six per leg and one torso-yaw joint. Each leg contains hip yaw, hip roll, hip pitch, knee pitch, ankle pitch, and ankle roll. This arrangement provides sufficient foot pose control for three-dimensional bipedal locomotion while retaining a relatively simple serial structure. The torso-yaw joint decouples camera orientation from the lower-body heading, allowing the robot to visually track a person or orient its camera without necessarily rotating the legs.</p> <p>All joints use Dynamixel XL430-W250 servos. The manufacturer-rated stall torque is$$1.4~\mathrm{N\,m}$, the no-load speed is$57~\mathrm{rpm}$, and the actuator mass is$57.2~\mathrm{g}$$. The actuator is the principal cost component, with a reported unit price of$$$23.90$1.8~\mathrm{kg}$0[-90^\circ,90^\circ]$, while the hip-pitch joints have asymmetric extended ranges to support crouching, larger sagittal leg excursions, and recovery from non-upright poses.

The center-of-mass configuration and joint topology are important because the smartphone is not a passive payload: it is the principal inertial and sensing body of the robot. Its fixed torso mounting makes the phone IMU the body-reference IMU used by the locomotion policies, eliminating the calibration and synchronization burden associated with an additional inertial module.

Figure 2: The symmetric six-DoF-per-leg structure and torso-yaw joint define the principal kinematic and center-of-mass configuration.

The electronic architecture intentionally avoids a physical connection between the phone and the robot. The Android application computes policy actions and reads the phone IMU; UDP packets transmit joint targets and receive telemetry through Wi-Fi; the SBC forwards the commands to the XL430 bus. This arrangement improves smartphone interchangeability and avoids phone-specific connectors, but it introduces dependence on wireless latency, packet delivery, and the SBC communication layer.

Figure 3: Smartphone, SBC, wireless communication, actuator bus, and application-level functions form the integrated PhoneBot system.

Torque-aware locomotion formulation

The locomotion contribution is a reinforcement-learning pipeline designed around the actuator limitations of the XL430 rather than an idealized position-controlled model. The policy outputs 13-dimensional joint-position residuals at $1.8~\mathrm{kg}$1. These residuals are converted into target positions and then into PD torques. The simulator applies an asymmetric torque clamp whose acceleration-side limit decreases with joint speed, while braking torque is allowed to reach the higher stall value.

This distinction is consequential. A position actuator that supplies a fixed torque independent of speed can produce policies that depend on forces unavailable on the physical hardware. PhoneBot instead models the torque-speed envelope, passive damping, friction, rotor armature, gearbox inefficiency, position offsets, and load-dependent encoder backlash. The training randomization further varies gains, torque limits, speed breakpoints, braking torque, and the passive-active ratio. The result is a simulation model that exposes the policy to actuator uncertainty and saturation during training.

The actor receives only deployment-available quantities: noisy angular velocity, projected gravity, velocity commands, joint positions and velocities, previous actions, and gait phase. Its observation dimension is 52. The critic receives a privileged 120-dimensional state containing clean proprioception, base height and velocity, actuator forces, foot contacts, foot velocities, and swing airtime. This asymmetric actor-critic structure improves value estimation without giving the deployed policy access to simulation-only variables.

The reward combines command tracking with stability, gait, and actuator regularization. Linear and yaw-velocity tracking each have weight $1.8~\mathrm{kg}$2. Orientation, foot slip, torque utilization, and foot-parallelism penalties discourage policies that achieve velocity tracking through unstable or mechanically expensive behaviors. In particular, the torque-utilization penalty activates above $1.8~\mathrm{kg}$3 of the directional torque limit. This embeds an explicit preference for operating below saturation, although the resulting behavior remains dependent on the selected reward weights and actuator model.

Sagittal mirroring and gait symmetry

The paper’s central methodological claim is that sagittal mirroring improves both gait symmetry and optimization efficiency on this torque-limited platform. Without mirroring, the authors observed asymmetric local optima in which one foot remained forward, the other provided most of the support, and the load distribution became strongly unilateral. Because PhoneBot is mechanically left-right symmetric, this asymmetry is interpreted as a training artifact rather than a required consequence of the hardware.

For each collected transition, the trainer constructs a mirrored actor sample by reflecting the IMU-frame state, swapping the legs and gait phases, negating the relevant angular quantities, and applying the same transformation to the action and policy mean. The critic is trained only on real trajectories because its privileged state contains world-frame quantities that are not directly mirrored under the proposed transformation. Thus, mirroring augments actor learning without treating the synthetic sample as an independently simulated transition.

This implementation provides two actor samples per physics transition while requiring only one simulation rollout. The intended effects are symmetry regularization, reduced seed dependence, and improved sample efficiency. The training comparison uses five independent seeds, identical environments and rewards, and runs to $1.8~\mathrm{kg}$4 environment steps.

Figure 4: Sagittal mirroring produces earlier and more consistent improvement in return, command tracking, survival, swing-foot behavior, and support-foot stability.

The mirrored policies achieve higher episode return earlier in training, with narrower across-seed variation. The component curves indicate earlier improvement in linear- and yaw-velocity tracking, increased alive reward, more reliable foot lift, better phase tracking, reduced foot slip, flatter foot orientation, and closer retention of the home pose. The action-rate cost is temporarily worse early in training, indicating a transient increase in action variation, but later becomes lower than the non-mirrored baseline.

The deterministic symmetry evaluation is more direct. After approximately $1.8~\mathrm{kg}$5 environment steps, mirroring reduces the joint-angle symmetry error from $1.8~\mathrm{kg}$6 to $1.8~\mathrm{kg}$7, the relative left-right mean absolute torque difference from $1.8~\mathrm{kg}$8 to $1.8~\mathrm{kg}$9, and the relative mechanical-power difference from $$0 to $$1.

Figure 5: Mirroring sharply reduces left-right differences in joint configuration, torque, and mechanical power in deterministic simulation.

These results support the strong claim that symmetry augmentation is not only a data-efficiency device but also a mechanism for suppressing mechanically undesirable unilateral gaits. The evaluation, however, is performed in nominal deterministic simulation with noise, delay, backlash, domain randomization, joint offsets, and pushes disabled. It therefore establishes controlled-policy symmetry more clearly than robustness under the full deployment distribution.

Stand-up, heading, and interaction policies

PhoneBot uses separate learned policies for stand-up and heading control. The stand-up policy is trained to recover from sitting and face-down configurations to a target standing height of $$2. Its reward emphasizes height, upward velocity, upright orientation, stable standing, and avoidance of trunk-floor contact. Pose-to-home terms are deliberately weak during recovery so that the policy is not rewarded for folding the limbs into a nominal configuration while remaining on the ground.

The heading policy maintains an upright stance and aligns the camera with a target look direction. It uses the torso-yaw joint and invokes stepping only when trunk yaw exceeds a threshold, with hysteresis between a $$3 activation threshold and a $$4 stopping threshold. This design avoids unnecessary stepping for small visual reorientation tasks while permitting whole-body turning when torso yaw is insufficient.

The physical demonstrations cover bipedal walking, front-camera human tracking, and recovery from a face-down pose. These demonstrations establish system-level integration rather than isolated actuator or perception benchmarks.

Figure 6: Physical demonstrations include walking, camera-based human tracking, and stand-up recovery from a prone configuration.

Smartphone application and computational evaluation

The Android application performs IMU acquisition, TensorFlow Lite policy inference, UDP communication, object detection, human-pose estimation, ARCore visual-inertial SLAM, and user interaction. Vision uses ML Kit and MediaPipe; speech interaction is split between the phone and a laptop backend running speech-to-text, a LLM, and text-to-speech. The architecture therefore provides local locomotion and perception while retaining an external backend for conversational processing.

The application was tested on four phones: a 2017 Honor 9 and three low-cost contemporary devices priced approximately between $$550$$6$$7. The reported CPU utilization shows that all tested phones support the principal onboard functions. Policy inference uses $$8--$$9 CPU, IMU acquisition at $$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$0 uses $$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$1--$$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$2, and vision detection uses $$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$3--$$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$4. SLAM consumes $$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$5--$$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$6 on the three phones that support it. The Honor 9 cannot run ARCore SLAM in the reported configuration because Google services are unavailable on that Huawei device, demonstrating that software ecosystem compatibility is an operational constraint independent of raw computational capacity.

Function	Reported CPU usage range
IMU at $$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$7	$$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$8--$$, excluding the smartphone. PhoneBot consequently occupies a substantially lower-cost regime than the comparison platforms listed in the manuscript, whose prices range from approximately$$9
Policy inference	$3{,}500$0--$3{,}500$1
Vision detection	$3{,}500$2--$3{,}500$3
ARCore SLAM	$3{,}500$4--$3{,}500$5
Human-robot interaction	$3{,}500$6--$3{,}500$7

The results support the paper’s claim that inexpensive or older smartphones can execute the complete CPU-bound onboard stack without a separate vision computer. They do not establish uniform real-time latency across devices, quantify Wi-Fi round-trip delay, or provide a sustained thermal analysis. Those omissions matter because locomotion stability depends on timing as well as average CPU utilization.

Limitations and open questions

PhoneBot’s demonstrated locomotion is limited to flat terrain, and the platform has no arms. The 13-DoF lower body and $3{,}500$8 stall-torque actuators constrain payload, manipulation, and dynamic behavior. The physical results are presented primarily as demonstrations; the manuscript does not report quantitative hardware walking speed, fall rate, endurance, latency distribution, battery life, or sim-to-real performance under controlled perturbation trials.

The smartphone architecture also leaves several system questions open. Wireless communication introduces a timing path between policy inference, the phone, SBC, and actuator bus, but the paper does not characterize packet loss, jitter, or worst-case delay. Similarly, the phone IMU is used as the body IMU without a detailed analysis of mounting compliance, camera-IMU calibration, thermal drift, or device-to-device sensor variation. The four-phone evaluation demonstrates compatibility, but not equivalence of control performance across handsets.

The sagittal-mirroring study compares only mirrored and non-mirrored policies under otherwise identical training conditions. It shows strong improvements in nominal deterministic symmetry and across-seed learning behavior, but does not isolate whether the gains arise primarily from symmetry regularization, doubled actor samples, altered gradient statistics, or interactions with the torque-utilization reward. The authors also leave open whether the transformation remains valid for asymmetric morphology, uneven terrain, external pushes, or manipulation tasks involving non-symmetric contacts.

Conclusion

PhoneBot presents a coherent low-cost humanoid architecture in which a commodity smartphone consolidates sensing, computation, communication, perception, and interaction. Its principal technical contribution is the combination of torque-aware actuator simulation, asymmetric actor-critic PPO, and sagittal mirroring for a 13-DoF biped. Across five training seeds, mirroring improves learning consistency and reduces nominal gait asymmetry from $3{,}500$9 to $$0 in joint-angle error, while lowering torque and mechanical-power imbalance by substantial factors. Physical demonstrations and four-phone evaluations show that the architecture supports locomotion, recovery, visual interaction, and SLAM without dedicated high-end onboard computing. The remaining questions concern timing guarantees, robustness beyond flat terrain, hardware-scale quantitative evaluation, and extension to manipulation.