PAPRLE (Plug-And-Play Robotic Limb Environment): A Modular Ecosystem for Robotic Limbs
Obin Kwon, Sankalp Yamsani, Noboru Myers, Sean Taylor, Jooyoung Hong, Kyungseo Park, Alex Alspach, Joohyung Kim
AI summary
Problem
Current teleoperation systems are typically rigid and designed for fixed device-robot pairings, limiting adaptability and hindering scalable data collection across diverse robotic configurations.
Approach
The authors introduce a modular, device-agnostic architecture featuring a pluggable kinesthetic puppeteer and a unified control pipeline that dynamically maps commands from diverse leaders to arbitrary follower robots in real time.
Key results
- A pluggable, 3D-printed puppeteer with interchangeable mounting interfaces
- A device-agnostic teleoperation pipeline supporting joint-space and task-space control
- Bilateral force feedback across heterogeneous leader-follower configurations
- Open-source hardware and software validated across diverse real-world setups
Why it matters
Provides a flexible, extensible platform that allows researchers to rapidly prototype, test, and collect scalable teleoperation data across any robot embodiment without hardware-specific re-engineering.
Abstract
We introduce PAPRLE (Plug-And-Play Robotic Limb Environment), a modular ecosystem that enables flexible placement and control of robotic limbs. With PAPRLE, a user can change the arrangement of the robotic limbs, and control them using a variety of input devices, including puppeteers, gaming controllers, and VR devices. This versatility supports a wide range of teleoperation scenarios and promotes adaptability to different task requirements. We also introduce a pluggable puppeteer device that can be easily mounted and adapted to match the target robot configurations. PAPRLE supports bilat- eral teleoperation through these puppeteer devices, agnostic to the type or configuration of the follower robot. The modular design of PAPRLE facilitates novel spatial arrangements of the limbs and enables scalable data collection, thereby advancing research in embodied AI and learning-based control. We validate PAPRLE in various real-world settings, demonstrating its versatility across diverse combinations of leader devices and follower robots. The system will be released as open source, including both hardware and software components, to support broader adoption and extension. Teleoperation, Data Collection, Human-Robot Interaction