Navigating Social Spaces: Robots Learn Human Manners

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Author: Denis Avetisyan

Researchers have developed a new framework that teaches robots to move through crowds with greater social awareness and comfort for those around them.

The RLSLM framework proposes a cyclical decision-making process—driven by reinforcement learning and informed by models of social influence—that enables an agent to navigate shared environments, iteratively updating its actions based on environmental feedback and initiating a new observational cycle with each change in state, acknowledging the inherent dynamism of interaction.
The RLSLM framework proposes a cyclical decision-making process—driven by reinforcement learning and informed by models of social influence—that enables an agent to navigate shared environments, iteratively updating its actions based on environmental feedback and initiating a new observational cycle with each change in state, acknowledging the inherent dynamism of interaction.

RLSLM combines rule-based social locomotion modeling with reinforcement learning to optimize robot navigation for both efficiency and human comfort in virtual reality environments.

Creating socially acceptable navigation for robots remains challenging, as purely rule-based systems lack adaptability while data-driven approaches often sacrifice transparency and intuitive alignment with human expectations. To address this, we introduce RLSLM: A Hybrid Reinforcement Learning Framework Aligning Rule-Based Social Locomotion Model with Human Social Norms, which integrates an empirically-grounded social locomotion model directly into a reinforcement learning reward function. This hybrid approach yields navigation policies demonstrably more comfortable for humans—as verified through immersive VR experiments—while retaining improved interpretability over purely learned methods. Could this framework represent a scalable pathway towards truly human-centered robotic navigation in complex, real-world environments?

The Fragility of Conventional Paths

Conventional navigation systems, frequently built upon pre-defined rules, demonstrate limitations when confronted with the complexities of real-world environments. These systems often falter in dynamic spaces—such as crowded streets or shifting obstacle courses—resulting in jerky, inefficient, or even illogical movement patterns. A rule-based approach, while seemingly straightforward, struggles to account for unforeseen circumstances or the subtle variations inherent in human behavior, leading to navigation that, while technically avoiding collisions, feels unnatural and lacks the fluidity of human motion. This rigidity stems from the system’s inability to adapt to changing conditions in real-time, highlighting the need for more flexible and responsive navigation strategies capable of handling unpredictability.

Conventional navigation systems, while adept at charting paths around static obstacles, often falter when factoring in the subtle choreography of human interaction. These systems typically prioritize geometrical efficiency, neglecting the unspoken rules governing personal space and predictable trajectories that humans instinctively observe. Consequently, a robot adhering strictly to collision avoidance may still induce discomfort or even collisions – not with physical objects, but with the expectations of those it shares space with. Studies reveal that individuals react negatively to robotic movements perceived as erratic, unpredictable, or overly direct, demonstrating that successful navigation requires more than simply reaching a destination; it demands a sensitivity to the social context and a mirroring of human spatial awareness to foster comfortable coexistence.

Human navigation extends far beyond the simple avoidance of obstacles; it’s a complex interplay of anticipating others’ movements and maintaining a comfortable social distance. Studies reveal that individuals don’t merely chart the shortest path, but rather subtly adjust their trajectories to align with social norms and predict the behavior of those around them. This prioritization of social comfort and predictability means that efficient path planning, as defined by robotics, often fails to capture the nuances of human movement. Researchers are discovering that people actively signal their intentions through subtle changes in speed and direction, allowing others to anticipate their path and avoid collisions—a level of social awareness absent in purely obstacle-avoidance systems. Consequently, truly intelligent navigation requires modeling not just the physical environment, but also the intricate web of social interactions that govern human spatial behavior.

Our VR-based evaluation pipeline assesses agent comfort by immersing users in simulated scenarios where they observe and rate the agent’s navigation among virtual humans, providing quantitative comparisons across different models.
Our VR-based evaluation pipeline assesses agent comfort by immersing users in simulated scenarios where they observe and rate the agent’s navigation among virtual humans, providing quantitative comparisons across different models.

A Framework Inspired by Living Systems

The Reinforcement Learning Social Locomotion Model (RLSLM) overcomes limitations of conventional navigation systems by integrating Reinforcement Learning (RL) with a Social Locomotion Model. Traditional methods often prioritize path efficiency without accounting for human social norms; RLSLM addresses this by employing RL to train agents, but constrains the learning process using the Social Locomotion Model. This model provides a detailed representation of human spatial behavior, including proxemics and preferred trajectories, which serves as a behavioral prior for the RL agent. Consequently, RLSLM facilitates the development of agents capable of learning optimal navigation strategies while simultaneously conforming to socially acceptable spatial patterns, resulting in more natural and predictable movement.

The RLSLM framework enables agents to concurrently optimize path planning for efficient navigation and maintain socially compliant behaviors. This is achieved by integrating a Reinforcement Learning component, responsible for learning optimal trajectories, with a Social Locomotion Model that encodes human spatial expectations. The model incorporates principles of proxemics – the study of human use of space – and established understandings of personal and social distances. Consequently, the agent learns to navigate environments not only effectively, but also in a manner that respects perceived boundaries and avoids uncomfortable proximity, leading to more natural and predictable interactions with humans.

RLSLM enhances human-agent interaction by directly incorporating social forces – the unspoken rules governing interpersonal space and movement – into its operational model. These forces, derived from the study of proxemics, are quantified and used to influence agent behavior, preventing collisions and maintaining comfortable distances from humans. The framework calculates repulsive forces based on proximity and attractive forces guiding agents toward open spaces or desired goals, resulting in trajectories that align with human expectations for natural movement. This explicit modeling of social dynamics minimizes awkward or intrusive behavior, leading to a more intuitive and positive experience for individuals interacting with the agent.

The hybrid RLSLM model integrates rule-based insights from controlled behavioral experiments with data-driven reinforcement learning to create and validate policies via a social reward function, ultimately enabling realistic human-agent interaction.
The hybrid RLSLM model integrates rule-based insights from controlled behavioral experiments with data-driven reinforcement learning to create and validate policies via a social reward function, ultimately enabling realistic human-agent interaction.

Quantifying the Nuances of Socially Compliant Motion

Within the RLSLM framework, Multi-Objective Reinforcement Learning (MORL) is utilized to concurrently optimize multiple, often conflicting, performance criteria. This approach moves beyond single-objective optimization by defining a reward function that incorporates metrics for travel time minimization, mechanical efficiency maintenance, and social comfort maximization. The MORL algorithm seeks to find a policy that does not necessarily maximize any single objective, but rather achieves a Pareto-optimal balance across all defined objectives, allowing for tunable prioritization of these factors based on specific application requirements. This is achieved through techniques that explore the trade-off surface between objectives, enabling the generation of trajectories that represent viable compromises between speed, energy consumption, and socially acceptable behavior.

The Social Discomfort metric, utilized within the RLSLM framework, provides a quantifiable assessment of deviations from socially acceptable navigational behaviors. This metric calculates discomfort based on factors such as proximity to other agents, relative velocities, and heading differences, assigning a numerical value representing the magnitude of the social violation. Specifically, it considers both the distance and velocity of nearby agents, weighting these components to reflect the sensitivity of human social norms. The resulting scalar value serves as a direct reward signal for the reinforcement learning algorithm, enabling it to learn policies that minimize social discomfort alongside other objectives like travel time and efficiency. This allows the RL agent to navigate in a manner that is not only efficient but also considerate of surrounding agents, resulting in more natural and human-like motion.

The navigation framework incorporates kinematic data, specifically agent $Velocity$, as a core component in modeling social interactions. Beyond directly considering the relative heading between agents – the heading-relevant component – the system also accounts for heading-irrelevant factors such as proximity and relative speed. This distinction allows for a more nuanced understanding of social behavior; for example, an agent traveling at a similar velocity but with a differing heading will elicit a different response than an agent with the same heading but drastically different speed. By modeling both components, the system aims to generate trajectories that reflect realistic human-like navigation and avoid uncomfortable or unexpected maneuvers for surrounding agents.

Across 24 multi-human scenarios evaluated in VR, RLSLM (green) consistently outperformed n-Body (blue) and COMPANION (orange) in both minimizing trajectory length and maximizing user-rated satisfaction, as indicated by trajectory length distributions and Likert-scale scores.
Across 24 multi-human scenarios evaluated in VR, RLSLM (green) consistently outperformed n-Body (blue) and COMPANION (orange) in both minimizing trajectory length and maximizing user-rated satisfaction, as indicated by trajectory length distributions and Likert-scale scores.

Validating Socially Aware Navigation Through Immersive Experience

The assessment of how convincingly artificial agents navigate shared spaces benefits greatly from the use of virtual reality. This technology offers a uniquely controlled yet immersive environment, allowing researchers to carefully observe human responses to agent behavior. By placing participants in a simulated world, it becomes possible to gauge the naturalness and comfort of an agent’s movements as it interacts with pedestrians – factors difficult to quantify in real-world studies or through purely observational methods. This approach facilitates the systematic evaluation of navigation strategies, enabling precise measurements of subjective experiences like perceived safety and ease of interaction, ultimately driving the development of more believable and user-friendly artificial intelligence.

Quantifying navigational efficiency requires precise measurements beyond simple path length; therefore, researchers utilize metrics like Maximum Lateral Distance to assess how much an agent deviates from a direct route. This measurement, expressed in meters, reveals the furthest distance an agent strays to the side of an ideal straight path while navigating toward a goal. A lower Maximum Lateral Distance indicates a more direct and efficient trajectory, suggesting the agent effectively avoids unnecessary detours or collisions with virtual obstacles or pedestrians. By analyzing this metric across different navigation algorithms – and in conjunction with comfort ratings obtained from human participants – scientists can gain valuable insights into how realistic and intuitive an agent’s movements appear within a virtual environment, ultimately improving the believability of the simulation and the overall user experience.

The creation of convincingly realistic crowds within virtual environments demands more than simply populating a space with moving figures; it requires agents that behave in a manner consistent with established social norms. To address this, researchers integrated the Social Force Model – a computational approach that simulates pedestrian movement based on attractive and repulsive forces between individuals – with the Reinforcement Learning-based Socially Learned Manifold (RLSLM) framework. This synergy allows for the generation of agent behaviors that not only navigate effectively but also exhibit a nuanced understanding of social interactions, such as avoiding collisions and maintaining comfortable personal space. By learning from observed human trajectories, RLSLM refines the social forces guiding each agent, resulting in crowd simulations where actions appear more natural, predictable, and ultimately, believable to human observers within the virtual environment.

Evaluations within a virtual reality environment reveal that the RLSLM framework achieves a notably high mean comfort rating of 4.21 out of 5 during interactions between humans and virtual agents. This performance represents a substantial improvement over baseline models, exceeding them by a difference of 1.12 on the comfort scale; statistical analysis, employing Bonferroni corrected post-hoc comparisons, confirms this difference is highly significant ($P<0.001$). The observed effect is robust, as evidenced by an Eta-squared value of 0.525, indicating that model type accounts for a considerable portion of the variance in comfort levels ($F(2,58)=219.589$, $P<0.001$). These findings suggest that RLSLM effectively generates agent behaviors perceived as natural and comfortable by human participants, offering a promising advancement in socially aware navigation systems.

Statistical analysis robustly confirms the enhanced comfort levels observed when utilizing the proposed model in virtual reality simulations. An Eta-squared ($η^2$) value of 0.525 demonstrates a substantial main effect of model type on participant comfort, meaning that a considerable proportion of the variance in comfort ratings – over 52% – can be attributed to the differences between the models tested. This effect is statistically significant, as evidenced by an F-statistic of 219.589 with 2 and 58 degrees of freedom, yielding a $P$-value less than 0.001. Such results provide compelling evidence that the implemented approach demonstrably improves the subjective experience of interacting with virtual agents, offering a significant advancement over baseline models in creating more natural and comfortable human-agent interactions within VR environments.

Across 25 single-human navigation scenarios in VR, RLSLM (green) and n-Body (blue) consistently outperformed COMPANION (orange) based on both trajectory length distributions and user-rated scores on a Likert scale of 1–5, as indicated by comparative histograms and predicted trajectories.
Across 25 single-human navigation scenarios in VR, RLSLM (green) and n-Body (blue) consistently outperformed COMPANION (orange) based on both trajectory length distributions and user-rated scores on a Likert scale of 1–5, as indicated by comparative histograms and predicted trajectories.

The pursuit of socially compliant robotic navigation, as demonstrated by RLSLM, echoes a fundamental truth about complex systems. Just as structures inevitably evolve and adapt, so too must robots learn to navigate human spaces with grace. Andrey Kolmogorov observed, “The most important things are not those that are easily solved, but those that require a long search.” This resonates with the iterative process of reinforcement learning, where the robot doesn’t simply solve the problem of social navigation, but rather learns to adapt its behavior over time, gradually aligning with nuanced human norms. The framework’s emphasis on multi-objective optimization – balancing efficiency with comfort ratings – suggests an acceptance that perfect solutions are rare, and that sometimes observing the process of adaptation is more valuable than attempting to force a predetermined outcome. The system, like all others, learns to age gracefully within its environment.

What Lies Ahead?

The architecture presented here, while demonstrating a measured advance in socially-aware navigation, merely postpones the inevitable reckoning with systemic complexity. The framework successfully integrates a rule-based foundation with the adaptive capacity of reinforcement learning, yet the very notion of ‘social norms’ remains a moving target. Each iteration of refinement, each improved comfort rating, is a temporary reprieve—a slowing of the entropy inherent in any attempt to model human behavior. The system’s present reliance on virtual reality as a proving ground highlights a critical, if predictable, limitation: the transition to unpredictable real-world scenarios will inevitably expose the brittleness of learned approximations.

Future efforts should not focus solely on achieving higher fidelity in simulated comfort, but on building systems capable of graceful degradation. A truly robust framework will not strive to perfectly replicate social behavior – an impossible goal – but to reliably detect and appropriately respond to deviations from anticipated interactions. The value lies not in predicting the unpredictable, but in cultivating resilience against it.

Ultimately, this work serves as a useful, if transient, landmark. Every delay in achieving full autonomy is the price of understanding the subtle, often contradictory, forces that govern human interaction. The true test will not be whether a robot can navigate a crowded room, but whether it can do so with a measured awareness of its own limitations, and a willingness to adapt to the inevitable imperfections of the world around it.

Original article: https://arxiv.org/pdf/2511.11323.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-17 20:08