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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description" content="A lean, ROS-free sim-to-real framework for training and deploying Vision-Language-Action (VLA) models and RL agents. Native MuJoCo Gymnasium wrappers with synchronous execution for Franka, UR5e, xArm, and SO101.">
<meta property="og:title" content="Robot Control Stack: A Lean Ecosystem for Robot Learning at Scale" />
<meta property="og:description" content="A Lean Ecosystem for Robot Learning at Scale" />
<meta property="og:url" content="https://robotcontrolstack.github.io/" />
<meta property="og:image" content="static/images/rcs_eye_candy_final.jpg" />
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<meta name="keywords" content="RCS">
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<title>Robot Control Stack</title>
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<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title">
<img src="static/images/rcs_logo_line.svg" alt="Project Logo"
style="height: 80px; vertical-align: middle;">
<br><br>A Lean Ecosystem for Robot Learning at Scale
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
Tobias Jülg<sup>*1</sup>,</span>
<span class="author-block">
Pierre Krack<sup>*1</sup>,</span>
<span class="author-block">
Seongjin Bien<sup>*1</sup>,</span>
<span class="author-block">
Yannik Blei<sup>1</sup>,</span>
<span class="author-block">
Khaled Gamal<sup>1</sup>,</span>
<span class="author-block">
Ken Nakahara<sup>2</sup>,</span>
<span class="author-block">
Johannes Hechtl<sup>1,3</sup>,</span>
<span class="author-block">
Roberto Calandra<sup>2</sup>,</span>
<span class="author-block">
Wolfram Burgard<sup>1</sup> and </span>
<span class="author-block">
Florian Walter<sup>1,4</sup></span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><br><sup>1</sup>University of Technology Nuremberg,</span>
<span class="author-block"><sup>2</sup>TU Dresden,</span>
<span class="author-block"><sup>3</sup>Siemens AG,</span>
<span class="author-block"><sup>4</sup>Technical University of Munich</span>
<span class="eql-cntrb"><small><br><sup>*</sup>Equal Contribution</small></span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<span class="link-block">
<a href="https://arxiv.org/abs/2509.14932" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<span class="link-block">
<a href="https://arxiv.org/pdf/2509.14932" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>PDF</span>
</a>
</span>
<span class="link-block">
<a href="https://github.com/RobotControlStack/robot-control-stack" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<span class="link-block">
<a href="https://robotcontrolstack.org" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-book"></i>
</span>
<span>Docs</span>
</a>
</span>
<span class="link-block">
<a href="https://huggingface.co/RobotControlStack"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<img src="static/images/hf_icon.svg" />
</span>
<span>Data</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<video id="tree" autoplay muted loop height="100%" preload="metadata">
<source src="static/videos/grid.mp4" type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
</h2>
</div>
</div>
<section class="section hero is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Vision-Language-Action models (VLAs) mark a major shift in robot learning.
They replace specialized architectures and task-tailored components of expert policies with large-scale
data collection and setup-specific fine-tuning.
In this machine learning-focused workflow that is centered around models and scalable training,
traditional robotics software frameworks become a bottleneck, while robot simulations offer only limited
support for transitioning from and to real-world experiments.
In this work, we close this gap by introducing <i>Robot Control Stack</i> (RCS), a lean ecosystem
designed from the ground up to support research in robot learning with large-scale generalist policies.
At its core, RCS features a modular and easily extensible layered architecture with a unified interface
for simulated and physical robots, facilitating sim-to-real transfer.
Despite its minimal footprint and dependencies, it offers a complete feature set, enabling both
real-world experiments and large-scale training in simulation.
Our contribution is twofold: First, we introduce the architecture of RCS and explain its design
principles.
Second, we evaluate its usability and performance along the development cycle of VLA and RL policies.
Our experiments also provide an extensive evaluation of Octo, OpenVLA, and Pi Zero on multiple robots
and shed light on the benefits of simulated data for robotic foundation models.
</p>
</div>
</div>
</div>
</div>
</section>
<section class="section" id="video-3min">
<div class="container is-max-desktop">
<h2 class="title is-3 has-text-centered">RCS in 3 Minutes</h2>
<div class="columns is-centered">
<div class="column is-10">
<div class="video-container"
style="position:relative;padding-bottom:56.25%;height:0;overflow:hidden;border-radius:12px;box-shadow:0 10px 30px rgba(0,0,0,0.1)">
<video controls preload="metadata" style="position:absolute;top:0;left:0;width:100%;height:100%">
<source src="static/videos/rcs_3min_overview.mp4" type="video/mp4">
</video>
</div>
<br>
<p>
Traditional robotics is built around hardware, with many interacting parts and specialized AI modules.
With machine learning taking the lead, this relationship flips around: <em> robots are components of a machine learning pipeline</em>.
</p>
<br>
<p>
Many libraries embrace this and adopt a Python- and ML-first approach, but they often lack robust robotics features and hardware support.
Robust policies require careful debugging in both simulation and hardware, which relies on classical robotics tools.
</p>
<br>
<p>
RCS bridges this gap by combining an ML-first design with the essential robotics tools.
It gives you the means to debug interfaces, validate tasks, and test directly on hardware—while remaining a lightweight pip-installable package with minimal dependencies.
</p>
</div>
</div>
</div>
</section>
<section class="section hero is-small">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column">
<div class="hero-body has-text-justified">
<h2 class="title is-3 has-text-centered">Architecture</h2>
<p>
<b>C++/Python API</b>
We provide device APIs in C++ with automatically generated Python bindings, ensuring mirrored functionality in both languages.
A new device can be integrated into RCS in either C++ or in Python, ensuring broad hardware compatibility.
</p>
<br>
<p>
<b>Composable scenes</b>
Higher-level abstractions are built on top of our own device APIs.
They leverage Gymnasium wrappers to enable modular scene creation through composition.
</p>
<br>
<p>
<b>Layered architecture</b>
Because we build upon a minimal low-level device API, you can quickly get up and running with new hardware: implement our interface, benefit from all the wrappers and apps higher up in the stack.
</p>
<br>
<img src="static/images/rcs_architecture_small.svg" alt="RCS Architecture." width="100%">
<div class="content has-text-justified">
Fig. 1: Applications (teleoperation, RL, VLA) interface with the environment (sim or real) through a unified
Gymnasium API. Sensors, actuators, and observers wrap the environment, mutating action/observation
spaces.
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<h2 class="title is-3 has-text-centered">Robot Setups with Digital Twins</h2>
<p>
We evaluate the usability of RCS's hardware oriented features by integrating multiple setups with different robots, grippers, cameras and touch sensors.
In total, four robots, four end-effectors, two cameras and a tactile sensor are implemented, both in simulation and on physical hardware.
</p>
<br>
<style>
/* minimal CSS */
#setup-carousel {
overflow: hidden;
}
/* prevents peeking slides */
#setup-carousel .caption {
/* move caption below dots */
margin-top: 0.5rem;
margin-bottom: 2.75rem;
/* adjust if your dots are taller/shorter */
}
</style>
<div id="setup-carousel" class="carousel">
<div class="item-1 has-text-centered">
<div style="display:grid;grid-template-columns:1fr 1fr;gap:.5rem;align-items:start;justify-items:center;">
<img src="static/images/robots/fr3_setup.jpg" alt="FR3 physical setup"
style="max-width:100%;height:auto;max-height:220px;border-radius:6px;">
<img src="static/images/robots/fr3_twin.png" alt="FR3 digital twin"
style="max-width:100%;height:auto;max-height:220px;border-radius:6px;">
</div>
<p class="is-size-6 caption"><strong>FR3</strong> + Franka Hand; wrist & side cameras.</p>
</div>
<div class="item-2 has-text-centered">
<div style="display:grid;grid-template-columns:1fr 1fr;gap:.5rem;align-items:start;justify-items:center;">
<img src="static/images/robots/xarm7_setup.jpg" alt="xArm7 physical setup"
style="max-width:100%;height:auto;max-height:220px;border-radius:6px;">
<img src="static/images/robots/xarm7_twin.png" alt="xArm7 digital twin"
style="max-width:100%;height:auto;max-height:220px;border-radius:6px;">
</div>
<p class="is-size-6 caption"><strong>xArm7</strong> + Tilburg Hand; side camera.</p>
</div>
<div class="item-3 has-text-centered">
<div style="display:grid;grid-template-columns:1fr 1fr;gap:.5rem;align-items:start;justify-items:center;">
<img src="static/images/robots/ur5e_setup.jpg" alt="UR5e physical setup"
style="max-width:100%;height:auto;max-height:220px;border-radius:6px;">
<img src="static/images/robots/ur5e_twin.png" alt="UR5e digital twin"
style="max-width:100%;height:auto;max-height:220px;border-radius:6px;">
</div>
<p class="is-size-6 caption"><strong>UR5e</strong> + Robotiq 2F-85; wrist & overhead cameras.</p>
</div>
<div class="item-4 has-text-centered">
<div style="display:grid;grid-template-columns:1fr 1fr;gap:.5rem;align-items:start;justify-items:center;">
<img src="static/images/robots/so101_setup.jpg" alt="SO101 physical setup"
style="max-width:100%;height:auto;max-height:220px;border-radius:6px;">
<img src="static/images/robots/so101_twin.png" alt="SO101 digital twin"
style="max-width:100%;height:auto;max-height:220px;border-radius:6px;">
</div>
<p class="is-size-6 caption"><strong>SO101</strong> + built-in gripper; wrist & side cameras.</p>
</div>
</div>
</div>
</section>
<section class="section hero is-small">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-justified">
<div class="column">
<div class="hero-body">
<h2 class="title is-3 has-text-centered">Applications</h2>
<br>
<p>
All implemented robots can be teleoperated with multiple devices and can be used to record data.
We also verify that RCS integrates cleanly into ML pipelines, both in the imitation learning and reinforcement learning settings.
We deploy multiple VLAs, and solve a simple simulated pick-up task with PPO, using proprioceptive and RGB states as observations.
</p>
<br>
<br>
<hr>
<h3 class="title is-4 has-text-centered">Teleoperation & Data Collection</h3>
<div class="columns">
<div class="column">
<h4 class="title is-5">HTC Vive</h4>
<video autoplay muted loop preload="metadata" width="100%">
<source src="static/videos/fr3_teleop.mp4" type="video/mp4">
</video>
</div>
<div class="column">
<h4 class="title is-5">Meta Quest 3</h4>
<video autoplay muted loop preload="metadata" width="100%">
<source src="static/videos/xarm7_teleop.mp4" type="video/mp4">
</video>
</div>
<div class="column">
<h4 class="title is-5">SpaceMouse</h4>
<video autoplay muted loop preload="metadata" width="100%">
<source src="static/videos/ur5e_spacemouse.mp4" type="video/mp4">
</video>
</div>
</div>
<div class="columns is-centered">
<div class="column is-5">
<h4 class="title is-5">Leader-Follower</h4>
<video autoplay muted loop preload="metadata" width="75%">
<source src="static/videos/so101_teleop.mp4" type="video/mp4">
</video>
</div>
<div class="column is-5">
<h4 class="title is-5">Scripted Data Collection</h4>
<video autoplay muted loop preload="metadata" width="75%">
<source src="static/videos/fr3_scripted.mp4" type="video/mp4">
</video>
</div>
</div>
<br>
<hr>
<h3 class="title is-4 has-text-centered">Reinforcement Learning</h3>
<div class="columns is-centered">
<div class="column is-5">
<video autoplay muted loop preload="metadata" width="75%">
<source src="static/videos/fr3_rl_graph.mp4" type="video/mp4">
</video>
</div>
<div class="column is-5">
<video autoplay muted loop preload="metadata" width="75%">
<source src="static/videos/fr3_rl_robot.mp4" type="video/mp4">
</video>
</div>
</div>
<br>
<hr>
<h3 class="title is-4 has-text-centered">VLA Inference</h3>
<div class="columns is-centered">
<div class="column is-5">
<h4 class="title is-5">Real</h4>
<video autoplay muted loop preload="metadata" width="75%">
<source src="static/videos/fr3_vla2.mp4" type="video/mp4">
</video>
</div>
<div class="column is-5">
<h4 class="title is-5">Simulation</h4>
<video autoplay muted loop preload="metadata" width="75%">
<source src="static/videos/fr3sim_vla.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section hero is-small">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column">
<div class="hero-body has-text-justified">
<h2 class="title is-3 has-text-centered">Results</h2>
<p>We demonstrate how RCS supports VLA research by investigating VLA generalization across multiple embodiments and assessing the benefit of simulated data for robotic foundation models.
</p>
<br>
<div class="columns is-vcentered">
<div class="column is-half">
<figure class="image">
<img src="static/images/results/robot_comparison.svg" alt="Descriptive alt text" width="90%">
</figure>
</div>
<div class="column is-half">
<div class="columns is-multiline">
<div class="column is-half">
<video autoplay muted loop playsinline preload="metadata" width="100%">
<source src="static/videos/fr3_vla.mp4" type="video/mp4">
</video>
</div>
<div class="column is-half">
<video autoplay muted loop playsinline preload="metadata" width="100%">
<source src="static/videos/xarm7_vla.mp4" type="video/mp4">
</video>
</div>
<div class="column is-half">
<video autoplay muted loop playsinline preload="metadata" width="100%">
<source src="static/videos/ur5e_vla.mp4" type="video/mp4">
</video>
</div>
<div class="column is-half">
<video autoplay muted loop playsinline preload="metadata" width="100%">
<source src="static/videos/so101_vla.mp4" type="video/mp4">
</video>
</div>
</div>
</div>
</div>
<div class="content has-text-justified">
Fig. 2: We fine-tune Pi Zero on four datasets from different setups.
Each dataset contains fewer than 150 episodes.
The fine-tuned models are deployed on the corresponding setups.
The robots that are more prominent in the base model's data mix achieve better results.
</div>
<img src="static/images/results/sim_real_eval.svg"
alt="Success rate plot over training checkpoints." width="100%">
<div class="content has-text-justified">
Fig. 3: We investigate the impact of simulated data on VLA performance.
Our setup is replicated in simulation and used to generate 500 trajectories using a scripted policy, which is then used to complement our manually collected dataset of 143 trajectories.
The plots show the success rate of the policy, both in the simulated scene and on the hardware, as training progresses.
Success rates in simulation correlate with success rates on the physical robot—consistent with a good evaluation metric.
Adding simulated data to the training mix improves performance in both settings.
</div>
<br>
<hr>
<h3 class="title is-4 has-text-centered">System Performance</h3>
<p>
To support modern, high-frequency VLA architectures (like π<sub>0</sub>), RCS must process data efficiently. We evaluated the recording frequencies across multiple camera and tactile sensor configurations. RCS scales reliably up to 90-120 Hz before encountering hardware IO bottlenecks, proving its suitability for high-frequency RL and VLA deployments despite the synchronous nature of Gymnasium environments.
</p>
<br>
<img src="static/images/system_performance.svg" alt="Configured control vs. measured data frequency during teleoperation" width="100%">
<div class="content has-text-justified">
Fig. 4: Configured control vs. measured data frequency during teleoperation, averaged over more than 1000 steps. The shaded area denotes the standard deviation. <em>FR3 2 Cams</em>: Two RealSense cameras. <em>FR3 4+2 Cams</em>: Four RealSense cameras and two DIGIT sensors. <em>Dual FR3 4+2 Cams</em>: Like <em>FR3 4+2 Cam</em> but with two FR3 robots.
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{juelg2026robotcontrolstack,
title={{Robot Control Stack}: {A} Lean Ecosystem for Robot Learning at Scale},
author={Tobias J{\"u}lg and Pierre Krack and Seongjin Bien and Yannik Blei and Khaled Gamal and Ken Nakahara and Johannes Hechtl and Roberto Calandra and Wolfram Burgard and Florian Walter},
year={2026},
booktitle={Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation (ICRA)},
note={Accepted for publication.}
}
</code></pre>
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