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\title{\textbf{Toward a Behavior Layer for Robots}\\[4pt]\large Reusable Interaction Profiles, Human Service References, and Cross-Task Proof on a Humanoid Platform}
\author{Motius Robotics}
\date{May 2026}

\begin{document}
\maketitle

\begin{abstract}
Robots are advancing quickly in navigation, manipulation, and autonomy, yet human-facing behavior is still rarely packaged as reusable software. In practice, behavior differences are often implemented as hidden runtime parameters, scene-specific scripts, and deployment-local tuning. This makes behavior difficult to compare, version, validate, and transfer across deployments. We present a behavior-layer formulation in which interaction style is represented explicitly through reusable profiles that resolve into execution fields such as speed scaling, pause timing, stopping distance, handover cadence, and end-effector smoothing. We instantiate this formulation in a prototype called Motius, which exposes a public profile surface, an adapter boundary above the runtime, and a preliminary reference-and-validation loop grounded in human behavior examples and robot proof clips. We then study a controlled proof asset on a single humanoid platform across three interaction types---object handover, approach-and-stop, and push object---while keeping the robot body, filming environment, and task framing fixed. The current prototype surfaces measurable profile deltas between a Standard and Gentle profile pair, including an 18.0\% reduction in speed scale, a 110.0\% increase in pause duration, an 18.7\% increase in stopping distance, and a 6.7\% increase in end-effector smoothing. Across all three interaction types, these profile changes remain visibly legible. Although the present evidence is preliminary and qualitative, it supports a narrow systems claim: robot behavior can be made inspectable, reusable, and cross-task visible as a software layer without replacing the low-level controller. We argue that this framing opens a path from deployment-local tuning toward explicit behavior software, human reference data, and future preference-based evaluation.
\end{abstract}

\section{Introduction}

Robots can already move, navigate, and complete tasks. That does not mean they already know how to behave in front of humans.

In many human-facing settings, the central question is not only whether a robot finishes a task, but how that task feels while it is being executed. A robot can stop at the correct place and still feel abrupt. It can hand over the correct object and still feel rushed. It can approach a user without colliding and still feel socially awkward. Prior work in human--robot interaction and socially aware navigation shows that users are highly sensitive to social presence, proxemics, motion style, and perceived appropriateness~\cite{sen2024bridging,frontiers2025review,parsnip2025,gaze2024,adaptiveprox2024,comfort2024}. Yet in many real deployments, these differences are still implemented as local scripts, hidden runtime parameters, and scene-specific QA adjustments rather than as reusable software objects.

This creates a persistent gap between robot capability and robot product quality. The hardware can remain the same, the task family can remain the same, and yet the desired interaction style can vary meaningfully by deployment context. A reception robot, a delivery robot, and a public-facing assistance robot may all share locomotion and manipulation primitives, but users will still care about how directly the robot approaches, how long it pauses, how softly it exits, and whether its behavior feels calm, efficient, reassuring, or abrupt.

We argue that this gap should be treated as a software problem. Specifically, we propose a \emph{behavior layer} for robots: a layer above the controller that packages human-facing interaction differences into reusable \emph{interaction profiles}. A profile is not a marketing label. It resolves into explicit execution fields that can be surfaced, compared, versioned, and mapped into robot-specific runtimes while preserving low-level control and safety.

The present paper does not claim a complete benchmark, a finished deployment stack, or generalized cross-platform transfer. Instead, it makes a narrower systems contribution. We instantiate the behavior-layer proposal in a prototype called Motius and study a controlled proof asset on the same humanoid robot across three interaction types: object handover, approach-and-stop, and push object. Within this scope, the paper makes four contributions:
\begin{enumerate}
    \item It formalizes interaction profiles as an explicit software abstraction for robot behavior.
    \item It describes an adapter-based runtime boundary that preserves controller ownership while making interaction style inspectable.
    \item It documents a controlled proof setup in which one profile change remains visible across three interaction types on the same humanoid robot.
    \item It proposes a human reference and validation loop through which behavior can eventually be grounded in structured human examples and preference-aware review.
\end{enumerate}

The intended contribution is therefore not a full autonomy claim, but an early robotics systems claim: behavior can become legible, parameterized, and reusable software.

\section{Related Work}

Our work sits at the intersection of social robot navigation, human--robot interaction, expressive motion design, and data/evaluation infrastructure for human-facing robot behavior.

First, recent review work emphasizes that social robot navigation is still not a solved problem at the level of requirements, datasets, and evaluation criteria~\cite{sen2024bridging,frontiers2025review}. Suitable evaluation targets remain fragmented across navigation efficiency, comfort, proxemics, perceived appropriateness, and human interpretation. Recent work such as PARSNiP explicitly treats perceived appropriateness as a first-class prediction target and incorporates human emotional and attentional features into robot social navigation analysis~\cite{parsnip2025}. Related work on robot gaze and social presence likewise demonstrates that small interaction-level changes can affect how robot behavior is interpreted in shared human space~\cite{gaze2024}.

Second, a growing body of work studies proxemics, emotional adaptation, and interaction-sensitive navigation behavior. Adaptive proxemics conditioned on human emotional signals has been proposed as a way to make robot movement more acceptable to nearby users~\cite{adaptiveprox2024}. Other work has called for a clearer taxonomy of proxemics in robot social navigation, underscoring the need for more explicit and portable behavior descriptions~\cite{taxonomy2025}. These directions align strongly with our argument that robot behavior should be surfaced as a reusable layer rather than buried inside local tuning.

Third, the literature on expressive robot motion argues that movement itself is a major channel for conveying intent, affect, and interaction quality. Recent work explores expressive motion design for both non-anthropomorphic and humanoid robots~\cite{elegnt2025,emotion2025,dmp2025}. More broadly, prior HRI work on legibility and predictability shows that the way a robot moves influences how observers interpret intent even before task completion is evaluated~\cite{dragan2013}. Although our current contribution is narrower than full expressive motion generation, it shares the same premise: the quality of motion matters independently of binary task success.

Finally, our framing is informed by work that uses human demonstrations and human videos as structured inputs for robot behavior design~\cite{armada2025,humanoidpolicy2024}. We do not attempt full one-to-one imitation of human embodiment. Instead, we focus on extracting transferable interaction primitives---approach, pause, handover, and exit behavior---that can be mapped onto robot-executable fields. This positions our contribution closer to an interface and systems abstraction than to a pure learning algorithm. In that sense, our work is complementary to recent end-to-end humanoid systems papers that integrate perception, reasoning, and embodiment into a unified robotics stack~\cite{safeguard2026}.

\section{Problem Formulation}

The problem is not that robots cannot complete tasks. The problem is that the same robot often has to be behaviorally rebuilt for each deployment setting.

Today, many human-facing behavior differences are handled through:
\begin{itemize}
    \item one-off scene scripts,
    \item runtime parameters with weak semantic naming,
    \item local QA and operator tuning,
    \item property-specific deployment adjustments.
\end{itemize}

This leads to a recurring implementation pattern in which the behavior that users actually perceive remains difficult to compare and reuse. The robot may already know how to execute a task, but the deployment team still has no explicit software object for the robot's interaction tone.

We formulate the behavior-layer problem around four requirements:
\begin{itemize}
    \item \textbf{R1: Explicitness.} Behavior differences should be surfaced through named fields rather than hidden local tuning.
    \item \textbf{R2: Runtime compatibility.} The behavior layer should not require replacement of the robot's low-level controller or safety system.
    \item \textbf{R3: Cross-task legibility.} A profile change should remain visible across more than one interaction type.
    \item \textbf{R4: Reference-grounded evaluation.} Behavior quality should eventually be evaluable against structured references and human judgment, not only task success.
\end{itemize}

The present paper addresses these requirements at prototype level. It does not solve full transfer or benchmarking, but it makes the software boundary and the experimental claim explicit.

\section{System Overview}

\subsection{Interaction Profiles}

The core abstraction in Motius is an \textbf{interaction profile}. An interaction profile is a reusable behavior object that specifies how the robot should feel during human-facing tasks. Rather than relying only on broad adjectives, a profile resolves into explicit fields that can be mapped into runtime behavior.

Typical fields include:
\begin{itemize}
    \item approach speed scale,
    \item stopping distance,
    \item pause duration,
    \item hold duration during handover,
    \item finish softness,
    \item motion smoothing,
    \item end-state control preferences.
\end{itemize}

The profile is therefore not the controller itself. It is a structured software interface above the controller.

\subsection{Adapter Layer and Runtime Boundary}

We separate the system into two layers:
\begin{enumerate}
    \item a \textbf{behavior layer}, which defines how the robot should behave in a scene, and
    \item a \textbf{runtime layer}, which still owns low-level execution, planning, actuation, and safety.
\end{enumerate}

This boundary is a key design decision. It allows behavior to become inspectable and reusable without claiming that high-level profile logic replaces the robot's full motion or autonomy stack.

Different robots and runtimes expose different control surfaces. A shared profile therefore needs an adapter layer that maps robot-agnostic behavior fields into robot-specific runtime values. The adapter handles:
\begin{itemize}
    \item normalization of units and timing,
    \item scaling relative to the robot platform,
    \item mapping into runtime-specific fields,
    \item safety-aware fallback behavior.
\end{itemize}

\subsection{Human Reference Network}

The system is not intended to rely only on robot clips. We therefore propose a second input surface: \textbf{human behavior references}. These references do not attempt to capture full human embodiment as a direct imitation target. Instead, they provide scene-specific examples of transferable interaction primitives such as:
\begin{itemize}
    \item approach style,
    \item waiting behavior,
    \item handover rhythm,
    \item corridor etiquette,
    \item exit timing.
\end{itemize}

These references can be paired with labels describing task type, scene context, and behavioral tone. Over time, such a reference corpus can help define what robot behavior should feel like in a way that is more structured than local operator intuition.

\subsection{Validation Workflow}

A behavior layer is only useful if it can be validated. We therefore define a preliminary validation workflow with three inputs:
\begin{enumerate}
    \item human reference clips,
    \item behavior labels and preference annotations,
    \item robot execution proof clips.
\end{enumerate}

This framework is designed to support not only task-completion checks, but also future preference-aware evaluation: for example, whether one profile is perceived as more natural, more professional, or more appropriate than another in a given scene.

\subsection{Current Prototype Surface}

The current Motius prototype already exposes a public profile surface with three named profiles: \textbf{Standard}, \textbf{Gentle}, and \textbf{Attentive}. Among these, Standard and Gentle are the pair directly reflected in the current proof asset.

The public prototype currently surfaces four explicit execution fields in the Standard/Gentle comparison:
\begin{itemize}
    \item \texttt{speed\_scale},
    \item \texttt{pause\_ms},
    \item \texttt{approach\_distance\_m},
    \item \texttt{end\_effector\_smoothing}.
\end{itemize}

These values are not presented here as a final benchmark, but as evidence that the behavior layer is already parameterized rather than purely narrative.

\begin{table}[H]
\centering
\caption{Public prototype parameter surface for the current Standard/Gentle comparison. Relative deltas are computed against the Standard profile.}
\label{tab:param_surface}
\begin{tabular}{>{\raggedright\arraybackslash}p{3.2cm} >{\raggedright\arraybackslash}p{2.0cm} >{\raggedright\arraybackslash}p{2.0cm} >{\raggedright\arraybackslash}p{2.4cm}}
\toprule
\textbf{Field} & \textbf{Standard} & \textbf{Gentle} & \textbf{Relative change} \\
\midrule
speed\_scale & 1.00 & 0.82 & -18.0\% \\
pause\_ms & 200 & 420 & +110.0\% \\
approach\_distance\_m & 0.80 & 0.95 & +18.7\% \\
ee\_smoothing & 0.90 & 0.96 & +6.7\% \\
\bottomrule
\end{tabular}
\end{table}

\begin{figure}[H]
    \centering
    \includegraphics[width=\linewidth]{how-it-works-architecture-board.png}
    \caption{System framing from human reference to runtime execution. The behavior layer does not replace motion control; it makes human-facing behavior inspectable, adjustable, and reusable above the runtime.}
    \label{fig:arch}
\end{figure}

\begin{figure}[H]
    \centering
    \includegraphics[width=\linewidth]{proof_comparison_board.jpg}
    \caption{Current behavior-layer framing: the same robot and the same task family can exhibit visibly different interaction qualities under different profiles.}
    \label{fig:comparison}
\end{figure}

\section{Prototype Implementation and Experimental Protocol}

\subsection{Research Questions}

The present prototype study is designed to answer three narrow research questions:
\begin{itemize}
    \item \textbf{RQ1:} Can explicit profile fields create visible cross-task behavior differences on the same humanoid platform?
    \item \textbf{RQ2:} Can those differences be surfaced without replacing the robot's controller or autonomy stack?
    \item \textbf{RQ3:} Does an explicit profile surface provide a credible basis for future human-reference and preference-based evaluation?
\end{itemize}

\subsection{Platform and Assets}

Our current proof is a raw validation asset recorded on the same humanoid robot. The reviewed source video has the following properties:
\begin{itemize}
    \item duration: 54.1 seconds,
    \item frame rate: 30 fps,
    \item orientation: vertical,
    \item resolution: 720 \(\times\) 1280.
\end{itemize}

The current asset is not a polished benchmark. It is a feasibility proof. To make the comparison more legible, the raw footage was also converted into a 43.0-second side-by-side proof clip in which Standard and Gentle are shown with explicit comparison framing.

\begin{table}[H]
\centering
\caption{Current proof assets used in this paper.}
\label{tab:assets}
\begin{tabular}{>{\raggedright\arraybackslash}p{3.2cm} >{\raggedright\arraybackslash}p{2.4cm} >{\raggedright\arraybackslash}p{2.6cm} >{\raggedright\arraybackslash}p{4.4cm}}
\toprule
\textbf{Asset} & \textbf{Duration} & \textbf{Format} & \textbf{Role in this paper} \\
\midrule
Raw field validation clip & 54.1 s & 720 \(\times\) 1280, vertical, 30 fps & Source proof covering the three interaction comparisons on the same humanoid robot \\
Edited side-by-side proof clip & 43.0 s & 1920 \(\times\) 1080, 30 fps & Comparison artifact used here as a clearer observational layer \\
Public profile surface & live prototype & web implementation & Provides explicit profile names, scene fit, and parameter fields \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Controlled Comparison Design}

The clip contains three interaction comparisons:
\begin{enumerate}
    \item \textbf{Object handover}: the robot approaches a user and hands over an object.
    \item \textbf{Approach-and-stop}: the robot walks toward the user and stops in front of them.
    \item \textbf{Push object}: the robot interacts with a wheeled object or platform.
\end{enumerate}

These interactions span both navigation-related and object-interaction-related behavior. This matters because it reduces the chance that a profile difference is only a one-task artifact.

The current comparison is controlled in a limited but meaningful sense. Across the Standard/Gentle comparison, the following are held fixed:
\begin{itemize}
    \item robot body,
    \item filming environment,
    \item task family,
    \item interaction ordering within each comparison window.
\end{itemize}

What changes is the active profile.

\begin{table}[H]
\centering
\caption{Approximate timing coverage in the current edited side-by-side proof clip.}
\label{tab:coverage}
\begin{tabular}{>{\raggedright\arraybackslash}p{3.0cm} >{\raggedright\arraybackslash}p{3.0cm} >{\raggedright\arraybackslash}p{2.6cm} >{\raggedright\arraybackslash}p{4.0cm}}
\toprule
\textbf{Interaction} & \textbf{Approx. clip window} & \textbf{Approx. duration} & \textbf{Primary visible shifts} \\
\midrule
Object handover & 0--14 s & 14 s & closing pace, hold duration, exit softness \\
Approach-and-stop & 15--24 s & 9 s & braking timing, stop distance, settle calmness \\
Push object & 25--34 s & 9 s & contact smoothness, movement pace, finish control \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Measurement Strategy}

The present paper does not claim a human subject study. Instead, it reports three classes of evidence:
\begin{enumerate}
    \item \textbf{Parameter-level evidence}: explicit numerical differences in surfaced profile fields.
    \item \textbf{Task-coverage evidence}: visibility of the profile change across more than one interaction type.
    \item \textbf{Behavioral-reading evidence}: structured visual interpretation of the side-by-side comparison under fixed platform and task conditions.
\end{enumerate}

The clip is organized around an A/B comparison between two profiles:
\begin{itemize}
    \item \textbf{Standard}: neutral, direct, efficient.
    \item \textbf{Gentle}: warm, patient, soft.
\end{itemize}

At the execution level, the intended difference is reflected in fields such as shorter versus longer pause, tighter versus wider spacing, quicker versus softer completion, and more direct versus more gradual approach.

\begin{table}[H]
\centering
\caption{Interpretive summary of the two interaction profiles used in the current proof.}
\label{tab:profiles}
\begin{tabular}{>{\raggedright\arraybackslash}p{2.1cm} >{\raggedright\arraybackslash}p{5.1cm} >{\raggedright\arraybackslash}p{5.1cm}}
\toprule
\textbf{Profile} & \textbf{Interaction tone} & \textbf{Expected execution traits} \\
\midrule
Standard & Neutral, direct, efficient & Shorter pause, tighter spacing, quicker completion, more operational finish \\
Gentle & Warm, patient, soft & Longer pause, wider stop distance, softer finish, calmer arrival and exit \\
\bottomrule
\end{tabular}
\end{table}

\begin{figure}[H]
    \centering
    \includegraphics[width=\linewidth]{proof_triptych_board.jpg}
    \caption{The current proof spans three interactions on the same humanoid robot: object handover, approach-and-stop, and push object.}
    \label{fig:triptych}
\end{figure}

\section{Preliminary Results}

The present results support a narrow claim: \textbf{one profile change is visibly legible across three interactions on the same robot.}

\subsection{Result 1: The behavior difference is not merely narrative}

The Standard/Gentle comparison is backed by explicit surfaced fields rather than only qualitative labels. Table~\ref{tab:param_surface} shows an 18.0\% decrease in speed scale, a 110.0\% increase in pause duration, an 18.7\% increase in stopping distance, and a 6.7\% increase in end-effector smoothing. Even at this early stage, the behavior layer is therefore parameterized in a way that can be inspected, compared, and versioned.

\subsection{Result 2: The profile difference is visible across multiple interaction types}

The current proof is not limited to a single ``hero'' moment. It allocates approximately 32 seconds of direct comparison across three interactions spanning both navigation-related and object-interaction-related behavior (Table~\ref{tab:coverage}).

In \textbf{object handover}, the visible difference is not only whether the robot reaches the correct position, but how the exchange unfolds. The Gentle variant appears to hold longer and exit more softly, while the Standard variant appears more direct and operational.

In \textbf{approach-and-stop}, the legible difference appears before any object exchange. The Gentle variant shows a calmer arrival with more space, while the Standard variant closes more directly.

In \textbf{push object}, the difference is expressed through movement rhythm, contact smoothness, and finish behavior, which suggests that the profile abstraction is not limited to one single interaction type.

\subsection{Result 3: The profile surface already extends beyond the proof pair}

The current public prototype defines three named profiles---Standard, Gentle, and Attentive---even though only Standard and Gentle are used in the present A/B study. This matters because it suggests that the current system is evolving as a reusable profile surface rather than as a one-off comparison artifact.

\subsection{Result 4: The software boundary remains narrow but credible}

The current prototype does not replace motion control, safety, or planning. Instead, it sits above them. This boundary is important: it keeps the claim narrow enough to be credible while still making a useful research point. The evidence does not imply general robot intelligence or complete deployment readiness. It supports a more specific interpretation: the interaction layer itself can be surfaced as reusable software.

\section{Discussion}

\subsection{Why a behavior layer is a distinct systems contribution}

Existing robotics systems often integrate perception, planning, control, and embodiment, but the human-facing behavior surface is still commonly hidden inside runtime-local decisions. Our contribution differs in emphasis. We treat behavior as an explicit software object that can be named, compared, and mapped above the controller.

This is relevant to both socially aware navigation and expressive motion design. Work on proxemics, social presence, expressive motion, and appropriateness shows that users care deeply about movement quality~\cite{parsnip2025,gaze2024,adaptiveprox2024,dmp2025}. A behavior layer provides one way to operationalize those concerns inside an inspectable software surface.

\subsection{Why human references matter}

Human-facing robot behavior should not be defined only by what is technically executable. It should also be informed by what people already recognize as appropriate, calm, direct, or professional. This is why the reference network matters. Its goal is not full human imitation, but the collection of transferable interaction primitives that can later be used for labeling, preference evaluation, and standard-setting.

\subsection{Why the current scope is still useful}

Although the current proof is small, it is sufficient for an early systems paper because it supports a bounded claim:
\begin{itemize}
    \item behavior can be abstracted into reusable profiles,
    \item those profiles can resolve into explicit fields,
    \item one profile change is visible across three interactions on the same humanoid platform,
    \item a reference-and-validation loop can be defined around this abstraction.
\end{itemize}

This does not replace the need for future quantitative studies, but it establishes a concrete architectural and experimental starting point.

\section{Threats to Validity}

Several threats to validity should be made explicit.

First, the present evidence is based on a single robot and a single proof asset. The results may therefore reflect characteristics of this specific hardware and runtime rather than a fully general behavior-layer property.

Second, the current evidence is primarily qualitative. Although explicit parameter deltas are reported, there is no statistical human preference study yet. Claims about naturalness, professionalism, or reassurance should therefore be interpreted as observational rather than population-level results.

Third, the edited side-by-side clip improves legibility but may also introduce presentation effects. The current paper mitigates this by grounding the study in the underlying raw validation asset, but more controlled future experiments are still needed.

Fourth, the current adapter boundary has not yet been evaluated across multiple robot platforms. The portability claim is therefore architectural rather than empirical at this stage.

\section{Limitations and Future Work}

This work has several clear limitations. We do not yet report a full human preference study. We do not yet evaluate the adapter abstraction across multiple robot platforms. We do not yet publish a completed reference dataset or a formal benchmark for scene-specific acceptance thresholds.

Future work will focus on five areas:
\begin{enumerate}
    \item a controlled human preference study comparing Standard, Gentle, and future profile pairs across the current task set;
    \item structured collection of staged human behavior references for approach, waiting, handover, and exit behavior;
    \item multi-robot adapter experiments that test whether the same profile language remains usable across different runtimes;
    \item profile versioning and deployment workflows that make behavior changes easier to audit and roll back;
    \item scene-specific behavior metrics that connect surfaced fields to downstream user preference and deployment acceptance.
\end{enumerate}

One concrete near-term target is a lightweight rating study in which human raters compare Standard and Gentle on perceived politeness, naturalness, clarity, and appropriateness. Another is the publication of an annotation schema for service-oriented interaction primitives so that reference collection and robot validation can be studied under the same language.

\section{Conclusion}

We presented an early preprint on a behavior layer for robots. The key idea is simple: behavior should become reusable software rather than a hidden collection of local tuning decisions. We instantiated this idea in a prototype called Motius through interaction profiles that resolve into explicit execution fields, an adapter boundary above the runtime, and a proposed reference-and-validation loop built around human examples, labels, and robot proof clips.

Our current evidence is intentionally modest, but sufficient for a narrow claim: on the same humanoid robot, a single profile change can create visible differences across handover, approach-and-stop, and push-object interactions. We argue that this is enough to motivate a broader research direction in which robot behavior becomes explicit, inspectable, and eventually reusable as a software layer.

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\end{document}
