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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<meta name="keywords" content="MULE, Adaptive control, Legged Robots, Reinforcement Learning, Quadruped Robot">
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<div class="column has-text-centered">
<h1 class="title is-2 publication-title">MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://scholar.google.com/citations?user=tHjGrMoAAAAJ&hl=en">Vamshi Kumar Kurva</a>,
</span>
<span class="author-block">
<a href="https://www.shishirny.com/">Shishir Kolathaya</a>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"> Indian Institute of Science (IISc), Bangalore </span>
</div>
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<div class="publication-links">
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<span class="author-block"> (Preprint under review) </span>
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</section>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Quadrupedal robots are increasingly deployed for
load-carrying tasks across diverse terrains. While Model Pre-
dictive Control (MPC)-based methods can account for payload
variations, they often depend on predefined gait schedules or
trajectory generators, limiting their adaptability in unstruc-
tured environments. To address these limitations, we propose
an Adaptive Reinforcement Learning (RL) framework that
enables quadrupedal robots to dynamically adapt to both
varying payloads and diverse terrains. The framework consists
of a nominal policy responsible for baseline locomotion and an
adaptive policy that learns corrective actions to preserve stability
and improve command tracking under payload variations. We
validate the proposed approach through large-scale simulation
experiments in Isaac Gym and real-world hardware deployment
on a Unitree Go1 quadruped. The controller was tested on
flat ground, slopes, and stairs under both static and dynamic
payload changes. Across all settings, our adaptive controller
consistently outperformed the controller in tracking body height
and velocity commands, demonstrating enhanced robustness
and adaptability without requiring explicit gait design or
manual tuning.
</p>
<br>
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<h2 class="title is-3"> Background </h2>
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<div class="column is-four-fifths">
<div class="content has-text-justified" style="padding-top: 3%">
<p>
Traditional load adaptation methods for quadrupeds are model-based force controllers that regulate GRFs at the stance feet based on desired height and payload variations. They typically model quadrupeds as an SRB and compute optimal GRFs at contact points, relying on gait or trajectory generators to predefine foot contact schedules based on gait and velocity. A switch-based controller applies force control to stance legs and PD control to swing legs, making the system sensitive to early or delayed contacts on unstructured terrains, which can cause instability. In contrast, RL-based controllers directly output desired joint positions tracked by a PD controller, eliminating phase-based switching and enabling implicit adaptation to terrain and contact variations. Domain randomization is commonly used in RL-based methods to improve robustness to disturbances by introducing parameter variations during training; however, an excessive range of randomization often leads to conservative policies that prioritize robustness over optimal performance. This highlights the need for adaptive policies that dynamically adjust to payload changes and terrain variations.
</p>
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<h2 class="title is-3"> Training Architecture </h2>
</div>
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<div class="column is-four-fifths">
<img src="images/Adapt_RL_latest_new.png" alt="PIP-Loco Training Architecture" width="80%">
<div class="content has-text-justified" style="padding-top: 5%">
<p>
We introduce an Adaptive RL framework for locomotion under varying payload conditions by augmenting the nominal policy with an adaptive corrective policy.
The framework is trained in a two-phase process: the
nominal policy is first trained under normal conditions,
followed by an adaptive policy that provides corrective
actions without the need for explicit payload parameter
estimation.
</p>
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</section>
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<h2 class="title is-3"> Comparison with the baseline </h2>
</div>
</div>
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<div class="content has-text-justified" style="padding-top: 3%">
<p>
We compared the tracking performance against DreamWaQ, an RL-based controller, with the base mass randomized in the range [0, 10] kg, which we refer to as the Baseline controller.
Since our proposed method is also RL-based, this allows for a fair comparison of adaptive performance under mass variations.
</p>
</div>
</div>
<div class="column has-text-centered">
<div class="columns is-centered has-text-centered">
<div class="column">
<h3 class="title is-4"> Baseline </h3>
<img src="images/results/baseline_failure_2x-ezgif.gif" width="100%">
</div>
<div class="column">
<h3 class="title is-4"> Adaptive (ours) </h3>
<img src="images/results/adaptive_success_2x-ezgif.gif" width="100%">
</div>
</div>
<div class="content has-text-justified" style="padding-top: 3%">
<p>
The GIFs show the baseline versus the adaptive controller under a progressive increase of payload up to 10 kg.
The baseline controller struggles to maintain stable locomotion, exhibiting noticeable foot scuffing and instability at higher payloads.
In contrast, the adaptive controller successfully compensates for the added weight, maintaining balance and coordination throughout.
</p>
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<h2 class="title is-3"> Results </h2>
</div>
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<div class="columns is-centered">
<div class="column is-four-fifths">
<div class="content has-text-justified">
<p>
The proposed Adaptive RL framework was successfully deployed on a Unitree Go1 robot and evaluated across diverse terrains,
including flat ground, slopes, and stairs, under both varying static payloads and dynamic payload scenarios, such as freely
moving iron balls placed in a traymounted on the robot’s base. Across all tested conditions, our policy consistently outperformed
the baseline controller in terms of accurately tracking both the commanded body height and velocity.
</p>
</div>
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