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<!DOCTYPE html>
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<h1 class="title is-1 publication-title">Learning Decentralized Multi-Biped Control for Payload
Transport</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a target="_blank" href="https://bikcrum.com/">Bikram Pandit</a>,
</span>
<span class="author-block">
<a target="_blank" href="https://ashutosh781.github.io/">Ashutosh Gupta</a>,
</span>
<span class="author-block">
<a target="_blank" href="https://mohitgadde.github.io/">Mohitvishnu S. Gadde</a>,
</span>
<span class="author-block">
<a target="_blank" href="https://hawkmask.github.io/Portfolio/">Addison Johnson</a>,
</span><br/>
<span class="author-block">
<a target="_blank" href="https://idigitopia.github.io/">Aayam Kumar Shrestha</a>,
</span>
<span class="author-block">
<a target="_blank" href="https://www.linkedin.com/in/duanhelei/">Helei Duan</a>,
</span>
<span class="author-block">
<a target="_blank" href="https://sites.google.com/view/jeremydao/home">Jeremy Dao</a>,
</span>
<span class="author-block">
<a target="_blank" href="https://engineering.oregonstate.edu/people/alan-fern">Alan Fern</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
Collaborative Robotics and Intelligent Systems (CoRIS) Institute<br/>Oregon State University
</span>
</div>
<div class="column has-text-centered">
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<span>arXiv</span>
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href="https://arxiv.org/pdf/2406.17279"
class="external-link button is-normal is-rounded is-dark">
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<span>Code</span>
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</section>
<section class="section">
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Payload transport over flat terrain via multi-wheel robot carriers is well-understood, highly
effective, and configurable. In this paper, our goal is to provide similar effectiveness and
configurability for transport over rough terrain that is more suitable for legs rather than
wheels. For this purpose, we consider multi-biped robot carriers, where wheels are replaced by
multiple bipedal robots attached to the carrier. Our main contribution is to design a
decentralized controller for such systems that can be effectively applied to varying numbers and
configurations of rigidly attached bipedal robots without retraining. We present a reinforcement
learning approach for training the controller in simulation that supports transfer to the real
world. Our experiments in simulation provide quantitative metrics showing the effectiveness of
the approach over a wide variety of simulated transport scenarios. In addition, we demonstrate
the controller in the real-world for systems composed of two and three Cassie robots. To our
knowledge, this is the first example of a scalable multi-biped payload transport system.
</p>
</div>
</div>
</div>
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</section>
<section class="section">
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<h3 class="title is-centered has-text-centered">Our goal</h3>
<div class="content has-text-centered">
<p>
Our goal is to transport a payload using an arbitrary carrier,
which can be commanded about any control point on the carrier.
</p>
</div>
<div class="content has-text-centered">
<img src="./static/images/our_goal.png"
class="interpolation-image" alt="Interpolate start reference image."/>
</div>
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<section class="section is-centered">
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<h3 class="title has-text-centered">Our approach</h3>
<div class="content has-text-centered">
<p>We design a <b>decentralized Multi-Biped Controller (decMBC)</b> where:</p>
<ul>
<li>Each robot independently maneuvers the carrier.</li>
<li>There is no inter-communication between robots.</li>
<li>Each robot is aware only of its relative position with respect to the control point.</li>
<li>The same policy can be deployed across any number of robots.</li>
</ul>
</div>
<div class="content has-text-centered">
<img src="./static/images/our_approach.png" class="interpolation-image resized-image"
alt="Interpolate start reference image."/>
</div>
</div>
</section>
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<h3 class="title is-centered has-text-centered">Simulation Training</h3>
<div class="content has-text-centered">
<p>
We train with limited carrier configurations (upto 3 robots) simultaneously by
varying carrier control point, robots' relative position, carrier / payload masses.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video"
controls
muted
preload
playsinline
width="85%">
<source src="./static/videos/decMBC_CoRL_video_website_training_grid.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Re-rendering. -->
<h3 class="title is-centered has-text-centered">Simulation Testing</h3>
<h3 class="title is-4 has-text-centered">Varying Loads and Configurations</h3>
<div class="content has-text-centered">
<p>
We test our policy across variety of carrier configurations,
with arbitrary number of robots, and various carrier and payload weights.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video2"
controls
muted
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playsinline
width="85%">
<source src="./static/videos/decMBC_CoRL_video_website_testing_sim_grid.mp4"
type="video/mp4">
</video>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Re-rendering. -->
<h3 class="title is-centered has-text-centered">Simulation Testing</h3>
<h3 class="title is-4 has-text-centered">Challenging Terrain</h3>
<!-- <h3 class="title is-4 has-text-centered">Testing maneuverability</h3>-->
<div class="content has-text-centered">
<p>
With additional training on additional terrain types, including stairs, bumps, slopes, and dynamic obstacles, the controller is able traverse a range of challenging terrains. Note that the robots are blind and use only proprioceptive sensory input.
Nevertheless they can collectively react to terrain disturbances and collectively recover from instabilities of individual robots.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video3"
controls
muted
preload
playsinline
width="85%">
<source src="./static/videos/decMBC_CoRL_video_website_testing_rough_terrain_sim_grid.mp4"
type="video/mp4">
</video>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Re-rendering. -->
<h3 class="title is-centered has-text-centered">Real hardware Testing</h3>
<h3 class="title is-4 has-text-centered">Testing maneuverability</h3>
<div class="content has-text-centered">
<p>
We test our policy on real robots where same policy is deployed to each of the three robots. We
demonstrate
maneuverability of the carrier by commanding the carrier to follow different user commands.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video3"
controls
muted
preload
playsinline
width="85%">
<source src="./static/videos/decMBC_CoRL_video_website_testing_real_maneuver.mp4"
type="video/mp4">
</video>
</div>
<h3 class="title is-4 has-text-centered">Testing robustness to disturbances</h3>
<div class="content has-text-centered">
<p>
We also test the robustness of our policy against disturbances by applying external forces to the
carrier. Here,
we deploy the same policy to each of the two robots.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video4"
controls
muted
preload
playsinline
width="85%">
<source src="./static/videos/decMBC_CoRL_video_website_testing_real_disturbances.mp4"
type="video/mp4">
</video>
</div>
<h3 class="title is-4 has-text-centered">More hardware tests!</h3>
<div class="content has-text-centered">
<p>
We test our policy for two and three robots with various carrier, attachment points for robots,
and payload weights.
</p>
</div>
<div class="content has-text-centered">
<video id="replay-video5"
controls
muted
preload
playsinline
width="85%">
<source src="./static/videos/decMBC_CoRL_video_website_testing_real_grid.mp4"
type="video/mp4">
</video>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@misc{pandit2024learningdecentralizedmultibipedcontrol,
title={Learning Decentralized Multi-Biped Control for Payload Transport},
author={Bikram Pandit and Ashutosh Gupta and Mohitvishnu S. Gadde and Addison Johnson and Aayam Kumar Shrestha and Helei Duan and Jeremy Dao and Alan Fern},
year={2024},
eprint={2406.17279},
archivePrefix={arXiv},
primaryClass={cs.RO}
url={https://arxiv.org/abs/2406.17279},
}</code></pre>
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