Petoi | Research

Research Spotlight

Advancing Robotics Education and Innovation

Petoi Bittle X (robot dog) and Nybble Q (robot cat) - both powered by the open-source OpenCat framework - have been deployed in university robotics labs and featured in peer-reviewed academic research worldwide. Studies using Petoi robots cover bio-inspired locomotion and gait control, edge AI and autonomous systems, sim-to-real reinforcement learning, and robotics education accessibility. Institutions including Carnegie Mellon University, Harvard University, the University of Colorado Boulder, and research groups in Europe and Latin America have published work built on Petoi hardware. Petoi robots are ROS2-compatible, Python API accessible, and support Raspberry Pi and Nvidia Jetson Nano - making them one of the most capable sub-$300 research platforms for legged robot studies.

Researchers choose Petoi for three reasons: cost (Bittle X from $249, compared to $75,000+ for industrial quadrupeds), openness (all firmware and CAD files available on GitHub under the OpenCat framework), and capability (9–11 high-performance servo joints, real quadruped gaits, onboard IMU, and expansion ports for Raspberry Pi, Nvidia Jetson Nano, and custom sensor arrays). For research enquiries, lab pricing, and multi-unit institutional orders, contact Us.

Focus on using bio-mimetic neural networks, like Central Pattern Generators (CPGs), and reinforcement learning to generate efficient, natural robotic movements such as walking, bounding, and fall recovery

Bio-Inspired Locomotion & Gaits

Center on deploying efficient machine learning (TinyML) & autonomous control software directly onto resource-constrained robots for complex, real-world tasks like semantic navigation & structural health monitoring

Edge AI & Autonomous Systems

Highlight the use of ultra-low-cost, open-source hardware and global educational networks to make hands-on robotics and machine learning accessible to a wider audience

Robotics Education & Accessibility

Bio-Inspired Locomotion & Gaits

Quadruped locomotion, how four-legged robots walk, bound, recover from falls, and adapt to terrain - is one of the most active areas of robotics research. Petoi Bittle X and Nybble Q provide a low-cost, high-DOF platform for testing bio-mimetic control strategies including Central Pattern Generators (CPGs), reinforcement learning-based gait optimization, and event-driven sensorimotor systems. The following published studies used Petoi hardware to develop and validate locomotion algorithms that would otherwise require $50,000+ industrial platforms.

Accessibility-Based Clustering for Efficient Learning of Locomotion Skills

Chong Zhang, Wanming Yu, Zhibin Li

RoboShape: Using Topology Patterns to Scalably and Flexibly Deploy Accelerators Across Robots

Sabrina M. Neuman, Radhika Ghosal, Thomas Bourgeat, Brian Plancher, Vijay Janapa Reddi

Robot Locomotion through Tunable Bursting Rhythms using Efficient Bio-mimetic Neural Networks

Vijay Shankaran Vivekanand, Samarth Chopra, Shahin Hashemkhani, Rajkumar Kubendran

Symmetry-Guided Reinforcement Learning

Jiayu Ding, Zhenyu Gan, Xulin Chen, Garrett E. Katz

Robot Locomotion Control Using Central Pattern Generator with Non-linear Bio-mimetic Neurons

Vijay Shankaran Vivekanand, Shahin Hashemkhani, Shanmuga Venkatachalam, Rajkumar Kubendran

Toward autonomous event-based sensorimotor control with supervised gait learning and obstacle avoidance

Shahin Hashemkhani, Vijay Shankaran Vivekanand, Samarth Chopra, Rajkumar Kubendran

Edge AI & Autonomous Systems

Deploying machine learning directly on resource-constrained robots, without cloud connectivity, is the defining challenge of embedded AI robotics. Researchers have used Petoi Bittle X as a physical testbed for TinyML, sim-to-real transfer, GPT-4 semantic planning, and autonomous structural health monitoring. The platform's ESP32 BiBoard and Raspberry Pi expansion port make it viable for edge inference workloads that typically require larger, more expensive hardware. The following studies demonstrate Petoi-powered autonomous systems operating in real-world environments.

Closing the Sim-to-Real Gap for Ultra-Low-Cost, Resource-Constrained, Quadruped Robot Platforms

Jason Jabbour, Sabrina M. Neuman, Mark Mazumder, Colby Banbury, Shvetank Prakash, Brian Plancher, Vijay Janapa Reddi

CONTROL DE UN ROBOT CUADRÚPEDO DESDE M2OS

Héctor Bringas López

Mobile structural health monitoring using quadruped robots

K. Smarsly, M. Worm, K. Dragos, J. Peralta, M. Wenner, O. Hahn

Mobile Agent Learning in a Simulation Environment Using Supported Learning and Applications in A Real Environment - Sim-to-real Reinforcement Learning with Nybble Robot Cat

Bruno ZahirovićBruno Zahirović

Semantic Intelligence: Integrating GPT-4 with A* Planning in Low-Cost Robotics

Jesse Barkley, Abraham George, Amir Barati Farimani

Mobile structural health monitoring using quadruped robots

K. Smarsly, M. Worm, K. Dragos, J. Peralta, M. Wenner, O. Hahn

Robotics Education & Accessibility

Accessible robotics education - giving students hands-on experience with real legged robots rather than simulations, requires hardware that is affordable, open-source, and genuinely capable. Petoi Bittle X appears in published research on expanding access to machine learning hardware for underserved student populations, Tiny Robot Learning curriculum design, and global STEM robotics program deployment. The following studies examine how Petoi and OpenCat enable research-grade robotics education at significantly reduced cost.

Highlights the use of ultra-low-cost, open-source hardware (like the Petoi Bittle) and global educational networks to make hands-on robotics and machine learning accessible to a wider audience

Gabriel Mourad

Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots

Sabrina M. Neuman, Brian Plancher, Bardienus P. Duisterhof, Srivatsan Krishnan, Colby Banbury, et al.

Tiny Robot Learning: Expanding Access to Edge ML as a Step Towards Accessible Robotics

Brian Plancher

Institutions Using Petoi in Research

Carnegie Mellon University

Robotics Institute. Used Bittle X for real-time object detection research (YOLO on Raspberry Pi)

University of Colorado Boulder

Robotics STEM Camp. Bittle X deployed in K-12 and undergraduate robotics education programs.

Chinese University of Hong Kong

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Harvard University

Edge AI and TinyML research. Published work on closing the sim-to-real gap for ultra-low-cost quadruped platforms using Petoi hardware.

University of Pittsburgh

Bioengineering department. Published research on bio-mimetic locomotion using CPG neural networks on Petoi hardware.

Additional institutions

Published papers cite use at institutions in Germany, Spain, Brazil, and Bosnia. Petoi robots have been shipped to universities in 60+ countries.

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Citing Petoi in Academic Work

If your research uses Petoi hardware or the OpenCat framework, please use the citation:

Li, R. & Petoi LLC. (2017–present). OpenCat: Open-Source Quadruped Robotic Pet Framework. Petoi LLC. Retrieved from https://github.com/PetoiCamp/OpenCat

Research Enquiries & Institutional Pricing

Petoi supports academic research with institutional pricing, multi-unit lab bundles, technical documentation, and direct developer support. We work with university procurement offices and can supply official quotes for departmental purchase orders.Contact us with your institution name, intended use case, and quantity. We respond within 2 business days.

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