TRIP-Bag: A Portable Teleoperation System for Plug-and-Play Robotic Arms and Leaders

• URL: http://arxiv.org/abs/2603.09226v1
• Date: Tue, 10 Mar 2026 05:49:47 GMT
• Title: TRIP-Bag: A Portable Teleoperation System for Plug-and-Play Robotic Arms and Leaders
• Abstract summary: TRIP-Bag is a portable, puppeteer-style teleoperation system fully contained within a commercial suitcase.<n>With a setup time of under five minutes and direct joint-to-joint teleoperation, TRIP-Bag enables rapid and reliable data collection in any environment.
• Score: 7.528471207038859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large scale, diverse demonstration data for manipulation tasks remains a major challenge in learning-based robot policies. Existing in-the-wild data collection approaches often rely on vision-based pose estimation of hand-held grippers or gloves, which introduces an embodiment gap between the collection platform and the target robot. Teleoperation systems eliminate the embodiment gap, but are typically impractical to deploy outside the laboratory environment. We propose TRIP-Bag (Teleoperation, Recording, Intelligence in a Portable Bag), a portable, puppeteer-style teleoperation system fully contained within a commercial suitcase, as a practical solution for collecting high-fidelity manipulation data across varied settings. With a setup time of under five minutes and direct joint-to-joint teleoperation, TRIP-Bag enables rapid and reliable data collection in any environment. We validated TRIP-Bag's usability through experiments with non-expert users, showing that the system is intuitive and easy to operate. Furthermore, we confirmed the quality of the collected data by training benchmark manipulation policies, demonstrating its value as a practical resource for robot learning.

Related papers

• Bridging Handheld and Teleoperated Supervision for Contact-Rich Manipulation via State-Gated Experts [6.058807042621957]
  We study the interaction between handheld and teleoperated supervision for contact-rich manipulation.<n>We propose Bi-modal Routing for Imitation Data via Gated Experts (BRIDGE) to address the mismatch between observed and desired supervision.
  arXiv  Detail & Related papers  (2026-06-25T05:06:38Z)
• ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration [51.69384671495837]
  ActiveGlasses is a system for learning robot manipulation from ego-centric human demonstrations with active vision.<n>A stereo camera mounted on smart glasses serves as the sole perception device for both data collection and policy inference.<n>To enable zero-transfer, we extract object trajectories from demonstrations and use an object-centric point-cloud policy to jointly predict manipulation and head movement.
  arXiv  Detail & Related papers  (2026-04-09T17:59:08Z)
• End-to-End Dexterous Arm-Hand VLA Policies via Shared Autonomy: VR Teleoperation Augmented by Autonomous Hand VLA Policy for Efficient Data Collection [10.217810309422232]
  We propose a framework that divides control between macro and micro motions.<n>A human operator guides the robot's arm pose through intuitive VR teleoperation.<n>An autonomous DexGrasp-VLA policy handles fine-grained hand control using real-time tactile and visual feedback.
  arXiv  Detail & Related papers  (2025-10-31T16:12:02Z)
• ActiveUMI: Robotic Manipulation with Active Perception from Robot-Free Human Demonstrations [32.570602111692914]
  We present ActiveUMI, a framework for a data collection system that transfers in-the-wild human demonstrations to robots capable of complex bimanual manipulation.<n>ActiveUMI couples a portable VR teleoperation kit with sensorized controllers that mirror the robot's end-effectors.<n>By recording an operator's deliberate head movements via a head-mounted display, our system learns the crucial link between visual attention and manipulation.
  arXiv  Detail & Related papers  (2025-10-02T02:44:21Z)
• MV-UMI: A Scalable Multi-View Interface for Cross-Embodiment Learning [3.079859911926098]
  We present MV-UMI (Multi-View Universal Manipulation Interface), a framework that integrates a third-person perspective with the egocentric camera.<n>This integration mitigates domain shifts between human demonstration and robot deployment, preserving the cross-embodiment advantages of handheld data-collection devices.
  arXiv  Detail & Related papers  (2025-09-23T07:53:05Z)
• The Role of Embodiment in Intuitive Whole-Body Teleoperation for Mobile Manipulation [20.65893345441958]
  A strong sense of embodiment combined with minimal physical and cognitive demands helps maintain data quality over extended periods.<n>We evaluate two visual feedback mechanisms: immersive virtual reality and conventional screen-based visualization of the robot's field of view.<n>Our results show that the use of VR as a feedback modality increases task completion time, cognitive workload, and perceived effort of the teleoperator.
  arXiv  Detail & Related papers  (2025-09-03T11:25:36Z)
• Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models [50.19518681574399]
  A central challenge in real-world assistive teleoperation is for the robot to infer a wide range of human intentions from user control inputs.<n>We introduce Casper, an assistive teleoperation system that leverages commonsense knowledge embedded in pre-trained visual language models.<n>We show that Casper improves task performance, reduces human cognitive load, and achieves higher user satisfaction than direct teleoperation and assistive teleoperation baselines.
  arXiv  Detail & Related papers  (2025-06-17T17:06:43Z)
• VITAL: Interactive Few-Shot Imitation Learning via Visual Human-in-the-Loop Corrections [10.49712834719005]
  Imitation Learning (IL) has emerged as a powerful approach in robotics, allowing robots to acquire new skills by mimicking human actions.<n>Despite its potential, the data collection process for IL remains a significant challenge due to logistical difficulties and high costs associated with obtaining high-quality demonstrations.<n>We propose a large-scale data generation from a handful of demonstrations through data augmentation in simulation.
  arXiv  Detail & Related papers  (2024-07-30T23:29:47Z)
• Polybot: Training One Policy Across Robots While Embracing Variability [70.74462430582163]
  We propose a set of key design decisions to train a single policy for deployment on multiple robotic platforms. Our framework first aligns the observation and action spaces of our policy across embodiments via utilizing wrist cameras. We evaluate our method on a dataset collected over 60 hours spanning 6 tasks and 3 robots with varying joint configurations and sizes.
  arXiv  Detail & Related papers  (2023-07-07T17:21:16Z)
• Dexterous Manipulation from Images: Autonomous Real-World RL via Substep Guidance [71.36749876465618]
  We describe a system for vision-based dexterous manipulation that provides a "programming-free" approach for users to define new tasks. Our system includes a framework for users to define a final task and intermediate sub-tasks with image examples. experimental results with a four-finger robotic hand learning multi-stage object manipulation tasks directly in the real world.
  arXiv  Detail & Related papers  (2022-12-19T22:50:40Z)
• Learning Multi-Arm Manipulation Through Collaborative Teleoperation [63.35924708783826]
  Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks. Many real-world tasks require multiple arms, such as lifting a heavy object or assembling a desk. We present Multi-Arm RoboTurk (MART), a multi-user data collection platform that allows multiple remote users to simultaneously teleoperate a set of robotic arms.
  arXiv  Detail & Related papers  (2020-12-12T05:43:43Z)
• Visual Imitation Made Easy [102.36509665008732]
  We present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task.
  arXiv  Detail & Related papers  (2020-08-11T17:58:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.