Teleoperation of mobile bimanual manipulators requires simultaneous control of high-dimensional systems, often necessitating expensive specialized equipment. We present an open-source teleoperation framework that enables intuitive whole body control using readily available commodity hardware. Our system combines smartphone-based head tracking for camera control, leader arms for bilateral manipulation, and foot pedals for hands-free base navigation. Using a standard smartphone with IMU and display, we eliminate the need for costly VR helmets while maintaining immersive visual feedback. The modular architecture integrates seamlessly with the XLeRobot framework, but can be easily adapted to other types of mobile manipulators. We validate our approach through user studies that demonstrate improved task performance and reduced cognitive load compared to keyboard-based control.
Figures & tables
Figure 1. Manipulation system components.
Figure 2. Smartphone VR interface control.
Figure 3. Pack printed part task sequence of actions.
Figure 4. Locker task sequence of actions.
Figure 5. Trash can task sequence of actions.
Control setup
Pack part into a box
Place a bottle into a locker
Throw garbage into a garbage bin
Success rate
Time
Success rate
Time
Success rate
Time
Keyboard setup
65.5%
124 s
43.6%
232 s
75.3%
80 s
Meta Quest 3 + pedals
74.8%
108 s
47.2%
210 s
88.1%
64 s
Smartphone VR + pedals
74.4%
110 s
46.6%
211 s
89.6%
67 s
Table 1. Overall task success rate and completion time across all participants and experiments.
Figure 6. Taskload levels for Keyboard setup, Meta Quest 3 + pedals, and Smartphone VR + pedals.
Robot teleoperation studies how people remotely control robots through interfaces, sensing, feedback, and autonomy, despite distance, delay, and embodiment gaps.
Existing teleoperation systems are often tailored to specific robot hardware and task domains, limiting their scalability and adaptability. We present ModPack, a modular and extensible teleoperation system designed to support diverse robot embodiments and task requirements within a unified framework. At the core of ModPack is a self-contained wearable "backpack" that integrates onboard computation, power, communication, and data storage. Built on top of this shared interface, the system supports plug-and-play capability modules including joint-level teleoperation with haptic feedback, mobile manipulation, and active perception. Experiments across two distinct robot platforms and real-world mobile manipulation tasks demonstrate that ModPack provides a flexible and reusable framework for data collection and policy learning and collects higher quality data than a robot-free alternative. To support future research, we open-source the complete hardware design and software stack. Project website: https://modpack-robotics.github.io/
Executing contact-rich tasks efficiently requires the seamless integration of whole-body coordination and physical compliance regulation. However, existing teleoperation and data-collection frameworks often overlook the joint consideration of multimodal perception and coordinated whole-body operation. This limitation can reduce the efficiency and quality of demonstration collection, thereby affecting the effectiveness of downstream policy learning. In this work, we present M3-Tele: A Unified Multimodal Teleoperational Framework for Compliant Whole-Body Mobile Manipulation, enabling stable physical interaction and capturing aligned visual, tactile, force, and proprioceptive observations during task execution. Extensive experiments demonstrate that the proposed framework significantly improves contact-rich teleoperation performance. The proposed controller reduces the force tracking error from 4.132N to 0.346N, the contact loss from 2.46 to 0.02 events per trial and the tactile deformation error by 65%. User studies across four mobile manipulation tasks also verify the reliability and usability of the proposed system. Furthermore, Diffusion Policy experiments highlight the value of joint tactile and force sensing.
Hengxiang Chen, Shenwen Deng, Yujian Ma +3
Arbeit Gruppe Dexterous Robotics Lab, Shenzhen Technology University, China
We present a bimanual mobile manipulator built on the open-source XLeRobot with integrated onboard compute for less than $1300. Key contributions include: (1) optimized mechanical design maximizing stiffness-to-weight ratio, (2) a Tri-Bus power topology isolating compute from motor-induced voltage transients, and (3) embedded autonomy using NVIDIA Jetson Orin Nano for untethered operation. The platform enables teleoperation, autonomous SLAM navigation, and vision-based manipulation without external dependencies, providing a low-cost alternative for research and education in robotics and robot learning.