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AnyTeleop: A General Vision-Based Dexterous Robot Arm-Hand Teleoperation System
Yuzhe Qin, Wei Yang, Binghao Huang, Karl Van Wyk, Hao Su, Xiaolong Wang, Yu-Wei Chao, Dieter Fox
TL;DR
Vision-based teleoperation is low-cost but existing systems often scale poorly because they are tied to particular robots and deployment environments. AnyTeleop provides a unified framework spanning robot morphologies, realities, camera configurations, and remote users, while outperforming specialized baselines in real-world and simulated evaluations. Its modular, learning-free components and broad deployment support make it a flexible foundation for dexterous teleoperation and demonstration collection.
Problem
Existing vision-based teleoperation systems are often engineered for particular robot models or deployment environments, limiting scalable demonstration collection as robots and operating settings diversify.
Method
AnyTeleop combines a general motion-retargeting library, learning-free collision avoidance, modular software interfaces, browser-based visualization, and support for diverse robots, realities, cameras, and users.
Results
AnyTeleop achieves higher success rates on 8 of 10 real-world tasks and 5 of 6 simulated tasks than systems specialized for the corresponding hardware or simulator.
Takeaways & Limitations
AnyTeleop offers a single flexible framework for dexterous teleoperation across multiple robot arm-hand systems, environments, camera setups, and operator locations.
Abstract
from arXiv · showhide
Vision-based teleoperation offers the possibility to endow robots with human-level intelligence to physically interact with the environment, while only requiring low-cost camera sensors. However, current vision-based teleoperation systems are designed and engineered towards a particular robot model and deploy environment, which scales poorly as the pool of the robot models expands and the variety of the operating environment increases. In this paper, we propose AnyTeleop, a unified and general teleoperation system to support multiple different arms, hands, realities, and camera configurations within a single system. Although being designed to provide great flexibility to the choice of simulators and real hardware, our system can still achieve great performance. For real-world experiments, AnyTeleop can outperform a previous system that was designed for a specific robot hardware with a higher success rate, using the same robot. For teleoperation in simulation, AnyTeleop leads to better imitation learning performance, compared with a previous system that is particularly designed for that simulator. Project page: https://yzqin.github.io/anyteleop/.
I. INTRODUCTION
AnyTeleop addresses the poor scalability of vision-based teleoperation systems tied to particular robots or environments by providing a unified system for diverse hardware, realities, cameras, and users. Its flexible design retains strong real-world and simulation performance.
- Vision-based teleoperation is a low-cost alternative to specialized VR, gloves, controllers, haptic sensors, and motion-capture systems for dexterous robot systems.
- Prior systems often depend on robot-specific models or deployment environments, limiting scalability as robot pools and operating settings expand.
- AnyTeleop supports diverse arm-hand models, simulators or real-world settings, camera configurations, and geographic locations through browser-based remote feedback.
- Its software interface standardizes and decouples modules, while learning-free motion retargeting and collision avoidance adapt to new robots from kinematic models.
- AnyTeleop achieves higher success rates on 8 of 10 real-world tasks and 5 of 6 simulated tasks than systems specialized for the corresponding hardware or simulator.
- A containerized design simplifies installation by reducing users’ need to manage software dependencies.
II. RELATED WORK
Related work establishes vision-based teleoperation as a low-cost, low-intrusion approach, while prior simulated systems often support simpler gripper tasks. AnyTeleop targets dexterous, multi-morphology teleoperation across real and virtual environments.
- Vision-based hand tracking is favored over gloves, motion capture, inertial sensors, and VR headsets because it is low-cost and minimally intrusive.
- The proposed paradigm includes independent and collaborative operation, flexible camera configurations, local or remote visualization, and multiple robots in shared spaces.
- The system is designed for arbitrary camera counts and configurable retargeting and motion-generation modules, enabling adaptation across robot arms, hands, and environments.
- Prior simulation teleoperation frameworks using inertial sensors are limited mainly to parallel-gripper robots and simple tasks such as pick-and-place.
- AnyTeleop supports a wide range of dexterous tasks with robots of different morphologies in both virtual and real worlds through one framework.
III. SYSTEM OVERVIEW
AnyTeleop is organized around broad hardware, reality, remote-operation, and camera flexibility. Its modular architecture compares favorably with prior systems in robot support and collaborative use cases.
- System Features: AnyTeleop supports arbitrary dexterous arm-hand systems rather than a specific robot type, and remains decoupled from particular hardware drivers or physics simulators.
- System Features: The system provides browser-based visualization for monitoring teleoperation and simulation remotely, and accepts RGB or RGB-D input from single or multiple cameras.
- System Features: Table I compares systems across sensor requirements, robot-related support, and afforded use cases.
- System Features: Among the compared systems, AnyTeleop is the only one supporting different robot arms and collaborative teleoperation, and one of two supporting different dexterous hands.
- System Architecture: The teleoperation server receives camera streams, detects hand poses, converts them into joint commands, and sends commands over a network to simulated or real robots.
IV. TELEOPERATION SERVER
The teleoperation server converts RGB or RGB-D observations from one or more cameras into smooth, collision-free robot control commands. Its modular detection pipeline supports both minimal and richer sensing configurations.
- The server combines hand-pose detection, multi-camera fusion, retargeting, and motion generation under standardized interfaces.
- Hand-pose detection accepts RGB or RGB-D input from single or multiple cameras, using additional depth or views when available while remaining functional with one RGB camera.
- MediaPipe detects 3D coordinates for 21 hand-knuckle keypoints and corresponding 2D image keypoints in real time on a CPU.
- For RGB-D input, aligned depth values and camera intrinsics provide 3D keypoints used with Perspective-n-Point estimation for wrist pose.
- For RGB-only input, an additional network predicts weak-perspective scale to approximate 3D wrist position, though with larger error than depth cameras.
- The camera driver captures hand pose data, while the server processes it and converts detections into commands for real or simulated robots.
B. Detection Fusion
Detection fusion addresses multi-camera self-occlusion by calibrating camera frames and selecting the most confident relative hand motion. The retargeting stage then maps human hand poses to robot joint positions while enforcing limits and temporal smoothness.
- Multiple cameras alleviate hand self-occlusion, but their detections require alignment because each camera estimates pose in its own coordinate frame.
- Relative camera rotation is estimated from the operator’s first N hand-detection frames using the hand as a natural calibration marker.Relative motion is treated as more robust than absolute RGB-only wrist position.
- Shape-parameter prediction errors provide an approximate confidence score for choosing which camera’s relative motion to forward.The operator spreads their fingers during the first N frames to establish a reference shape, with N = 50 in implementation.
- Hand-pose retargeting minimizes human–robot keypoint differences while constraining robot joints and penalizing temporal changes.The module uses forward kinematics, joint limits, hand-size scaling, and a smoothness weight β; mappings must be specified manually for different morphologies.
D. Motion Generation
AnyTeleop generates smooth, reactive, collision-free robot-arm motion from detected end-effector poses and supports browser-based remote visualization. Its motion generation uses GPU-accelerated CuRobo rather than acceleration fields alone.
- The system’s real-time motion goal is smooth robot-arm movement toward the target Cartesian end-effector pose.The supplied pose is generated from detected wrist and hand pose.
- CuRobo generates natural, reactive, collision-free robot-arm motion in real time from low-frequency Cartesian end-effector poses.The hand-detection module supplies end-effector poses at 25 Hz, while CuRobo is highly parallelized and GPU accelerated.
- The web-based visualization module enables remote and collaborative teleoperation through browser access and synchronized views of the shared scene.
- Table II profiles teleoperation-server modules on desktop and laptop while all modules run simultaneously on one computer.
VI. SYSTEM EVALUATION
System evaluation covers real-world teleoperation and simulated imitation learning, using replicated tasks and profiling on desktop and laptop hardware. AnyTeleop improves or matches the relevant baselines across most reported tasks.
- System Profiling: Desktop and laptop profiling evaluates all teleoperation modules running simultaneously, with hand-pose detection identified as the most time-consuming module.The hand-pose detector is designed for a maximum frequency of 25 Hz, which both systems meet.
- A. Imitation Learning: The simulated evaluation uses three manipulation tasks with floating-hand and arm-hand variants, and evaluates success over 100 trials.Table III reports means and standard deviations across three random seeds.
- B. Real Robot Teleoperation: The real-world evaluation replicates ten manipulation tasks from prior work and compares AnyTeleop with a similar teleoperation system.
- B. Real Robot Teleoperation: 8/10 tasks achieved higher success rates and 2/10 matched the baseline in replicated real-robot experiments.The comparison used the same XArm6 robot, Allegro hand, and similar objects as the baseline study.
- B. Real Robot Teleoperation: AnyTeleop outperformed the hardware-specific baseline especially on thin-walled-object tasks through more stable fingertip-closing grasps.The cited tasks include cup-stack, two-cup-stacking, and cup-into-plate.
VII. APPLICATIONS
AnyTeleop supports demonstration collection for imitation learning in simulated manipulation tasks and enables collaborative teleoperation across operators and locations. Its smoother, collision-free demonstrations outperform baselines on most tasks.
- A. Imitation Learning: The simulated comparison uses Relocate, Flip Mug, and Open Door tasks with randomized initial objects and targets where applicable.Each task includes floating-hand and fixed-base arm-hand variants.
- Collaborative Teleoperation: Two operators can control different robots collaboratively, including a handover in which one robot picks up an object and passes it to the other.
- A. Imitation Learning: AnyTeleop demonstrations outperform baseline and reinforcement-learning demonstrations on most simulated tasks, with one exception.Smoother trajectories provide more consistent state-action pairs for the learning network.
- A. Imitation Learning: Arm-hand teleoperation explicitly supports the mounted-arm setting and guarantees no self-collision, unlike the baseline’s translated arm trajectories.The baseline system natively handles floating hands and uses a translation pipeline for arm-hand demonstrations.
- Collaborative Teleoperation: Operators in different locations can use local cameras and computers while viewing a synchronized simulation through a web browser.
B. Collaborative Manipulation
AnyTeleop extends teleoperation to collaborative manipulation by coordinating multiple operators through a modular system and web-based visualization. The paper demonstrates this setting with human-to-robot handover and reports broader performance gains over prior systems.
- Collaborative Manipulation: AnyTeleop supports collaborative manipulation by allowing multiple operators to coordinate robot and human hands, even from different physical locations.The demonstrated human-to-robot handover assigns one operator to a robot hand and another to a human hand.
- Collaborative Teleoperation System Design: The collaborative architecture comprises teleoperation units with cameras and human operators plus a central server running the physical simulation and web visualization.
- Failure Modes: Fast hand motion can cause tracking loss and pause-and-redetection, while self-occlusion can make hand poses unreliable.The paper suggests slowing the operator for tracking loss and adding multiple cameras when substantial hand rotation causes self-occlusion.
APPENDIX
The appendix provides supplementary implementation details, visualizations, and experimental material for AnyTeleop. It illustrates hand-pose detection across representative cases and hand-pose retargeting across gestures and robot hands.
- Supplementary Material: The supplementary material adds details on the teleoperation server, including detection and retargeting modules, the web viewer, and system-evaluation results.
- Visualization of Hand Pose Detection: Hand-pose detection visualizations cover initialization, top-down grasp preparation, precision grasping, power grasping, and a vertical-camera failure case.
- Visualization of Hand Pose Retargeting: Hand-pose retargeting visualizations show seven gestures performed with four different dexterous robot hands.
C. Web-based Teleoperation Viewer
The web-based viewer prioritizes accessible, simulator-agnostic teleoperation with lightweight browser rendering and multi-view support. Camera experiments show that depth and additional cameras improve Play Piano efficiency and accuracy, while visualizations cover supported tasks and retargeting examples.
- Web-based Teleoperation Viewer: The web viewer provides lightweight browser-based visualization for five IsaacGym tasks and can run across multiple browser windows.
- Lightweight Rendering vs High Visual Quality: The viewer favors accessibility over simulator-level visual quality, while saved simulation states can be rendered offline with a ray tracer.
- Multi-View Support for Teleoperation: Multi-view rendering gives operators additional views of spatial relationships by opening as many browser windows as needed.
- Camera Configurations: AnyTeleop supports RGB, RGB-D, single-camera, and multi-camera configurations, including a single RGB camera as a minimal setup.
- Camera Configurations: The Play Piano evaluation measures completion time and the percentage of incorrect key presses across camera configurations.
- Camera Configurations: Additional depth information and cameras enable faster task completion with fewer errors, allowing users to trade efficiency against system cost.
- Additional Visualizations: Hand-pose detection visualizations include five cases, while retargeting visualizations show seven gestures across four dexterous hands.