RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning

• URL: http://arxiv.org/abs/2605.11564v1
• Date: Tue, 12 May 2026 05:49:52 GMT
• Title: RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning
• Abstract summary: We present RIO (Robot I/O), an open source Python framework that provides flexible, lightweight components for robot control.<n>We validate RIO on VLA deployment across three morphologies (single-arm, bimanual, humanoid) and four hardware platforms with varying grippers and cameras.<n>We fine-tune state-of-the-art VLAs including $_0.5$ and GR00T on household tasks such as pick-and-place, folding, and bowl scrubbing.
• Score: 19.36610432610357
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to showcase these models on different robot platforms, generalist cross-embodiment robot capabilities remains a largely elusive ideal. Progress is limited by fragmented infrastructure: most robot code is highly specific to the exact setup the user decided on, which adds major overhead when attempting to reuse, recycle, or share artifacts between users. We present RIO (Robot I/O), an open source Python framework that provides flexible, lightweight components for robot control, teleoperation, data formatting, sensor configuration, and policy deployment across diverse hardware platforms and morphologies. RIO provides abstractions that enable users to make any choice and to switch between them, with minimal reconfiguration effort. We validate RIO on VLA deployment workflows across three morphologies (single-arm, bimanual, humanoid) and four hardware platforms with varying grippers and cameras. Using teleoperated data collected with RIO, we fine-tune state-of-the-art VLAs including $π_{0.5}$ and GR00T on household tasks such as pick-and-place, folding, and bowl scrubbing. By open sourcing all our efforts, we hope the community can accelerate their pace of robot learning on real-world robot hardware. Additional details at: https://robot-i-o.github.io

Related papers

• RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization [31.40401674436269]
  We introduce RDT2, a robotic foundation model built upon a 7B parameter VLM to enable zero-shot deployment on novel embodiments for open-vocabulary tasks.<n>We collected one of the largest open-source robotic datasets--over 10,000 hours of demonstrations in diverse families--using an enhanced, embodiment-agnostic Universal Manipulation Interface (UMI)<n>Our approach employs a novel three-stage training recipe that aligns discrete linguistic knowledge with continuous control via Residual Vector Quantization (RVQ), flow-matching, and distillation for real-time inference.
  arXiv  Detail & Related papers  (2026-02-03T09:38:23Z)
• LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator [1.5566524830295307]
  We propose LEO-RobotAgent, a general-purpose language-driven intelligent agent framework for robots.<n>This framework features strong generalization, robustness, and efficiency.<n>Experiments have verified that this framework can be easily adapted to mainstream robot platforms.
  arXiv  Detail & Related papers  (2025-12-11T12:58:36Z)
• Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations [52.29884993824894]
  Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community.<n>AINA enables learning multi-fingered policies from data collected by anyone, anywhere, and in any environment using Aria Gen 2 glasses.
  arXiv  Detail & Related papers  (2025-11-20T18:59:02Z)
• Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale [11.166320712764465]
  Vision-Language-Action models (VLAs) replace specialized architectures and task-tailored components of expert policies with large-scale data collection and setup-specific fine-tuning.<n>Traditional robotics software frameworks become a bottleneck, while robot simulations offer only limited support for transitioning from and to real-world experiments.<n>We introduce Robot Control Stack (RCS), a lean ecosystem designed from the ground up to support research in robot learning with large-scale generalist policies.
  arXiv  Detail & Related papers  (2025-09-18T13:12:16Z)
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots [133.23509142762356]
  General-purpose robots need a versatile body and an intelligent mind.<n>Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy.<n>We introduce GR00T N1, an open foundation model for humanoid robots.
  arXiv  Detail & Related papers  (2025-03-18T21:06:21Z)
• The One RING: a Robotic Indoor Navigation Generalist [58.30694487843546]
  RING (Robotic Indoor Navigation Generalist) is an embodiment-agnostic policy that turns any mobile robot into an effective indoor semantic navigator.<n>Trained entirely in simulation, RING leverages large-scale randomization over robot embodiments to enable robust generalization to many real-world platforms.
  arXiv  Detail & Related papers  (2024-12-18T23:15:41Z)
• Robot Utility Models: General Policies for Zero-Shot Deployment in New Environments [26.66666135624716]
  We present Robot Utility Models (RUMs), a framework for training and deploying zero-shot robot policies. RUMs can generalize to new environments without any finetuning. We train five utility models for opening cabinet doors, opening drawers, picking up napkins, picking up paper bags, and reorienting fallen objects.
  arXiv  Detail & Related papers  (2024-09-09T17:59:50Z)
• Octo: An Open-Source Generalist Robot Policy [88.14295917143188]
  We introduce Octo, a large transformer-based policy trained on 800k trajectories from the Open X-Embodiment dataset. It can be effectively finetuned to robot setups with new sensory inputs and action spaces within a few hours on standard consumer GPU. We also perform detailed ablations of design decisions for the Octo model, from architecture to training data, to guide future research on building generalist robot models.
  arXiv  Detail & Related papers  (2024-05-20T17:57:01Z)
• RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation [77.41969287400977]
  This paper presents textbfRobotScript, a platform for a deployable robot manipulation pipeline powered by code generation. We also present a benchmark for a code generation benchmark for robot manipulation tasks in free-form natural language. We demonstrate the adaptability of our code generation framework across multiple robot embodiments, including the Franka and UR5 robot arms.
  arXiv  Detail & Related papers  (2024-02-22T15:12:00Z)
• 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)

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.