YOR: Your Own Mobile Manipulator for Generalizable Robotics
Manan H Anjaria, Mehmet Enes Erciyes, Vedant Ghatnekar, Neha Navarkar, Haritheja Etukuru, Xiaole Jiang, Kanad Patel, Dhawal Kabra, Nicholas Wojno, Radhika Ajay Prayage, Soumith Chintala, Lerrel Pinto, Nur Muhammad Mahi Shafiullah, Zichen Jeff Cui
TL;DR
YOR presents an open-source, cost-efficient mobile manipulator that combines an omnidirectional base, a telescopic lift, and dual compliant arms to enable whole-body manipulation at under $10{,}000. By integrating a swerve-base, a capable vertical lift, and two PiPER arms with compliant control, YOR supports both teleoperation and autonomous manipulation, and it is paired with a visual-inertial SLAM and voxel-based mapping stack for robust indoor navigation. The paper demonstrates YOR’s capabilities through whole-body teleoperation, an imitation-learning policy for bimanual tasks, and SLAM-based navigation with dynamic obstacle avoidance, highlighting practical performance metrics such as loop-closure accuracy around 12 mm, end-effector stability within about 16 mm during base motion, and replanning within ~1 s in dynamic scenarios. Collectively, YOR offers a scalable, low-cost platform that enables broad mobile manipulation research, with an open-source hardware/software package to accelerate community collaboration and rapid iteration in home-like environments.
Abstract
Recent advances in robot learning have generated significant interest in capable platforms that may eventually approach human-level competence. This interest, combined with the commoditization of actuators, has propelled growth in low-cost robotic platforms. However, the optimal form factor for mobile manipulation, especially on a budget, remains an open question. We introduce YOR, an open-source, low-cost mobile manipulator that integrates an omnidirectional base, a telescopic vertical lift, and two arms with grippers to achieve whole-body mobility and manipulation. Our design emphasizes modularity, ease of assembly using off-the-shelf components, and affordability, with a bill-of-materials cost under 10,000 USD. We demonstrate YOR's capability by completing tasks that require coordinated whole-body control, bimanual manipulation, and autonomous navigation. Overall, YOR offers competitive functionality for mobile manipulation research at a fraction of the cost of existing platforms. Project website: https://www.yourownrobot.ai/
