Following the Human Thread in Social Navigation
Authors: Luca Scofano
Alessio Sampieri
Tommaso Campari
Valentino Sacco
Indro Spinelli
Lamberto Ballan
Fabio Galasso
Venue: ICLR 2025 Spotlight (2025)
Keywords: Embodied AI
Social Navigation
Human Trajectories
Primary area: applications to robotics, autonomy, planning

Abstract: The success of collaboration between humans and robots in shared environments relies on the robot's real-time adaptation to human motion. Specifically, in Social Navigation, the agent should be close enough to assist but ready to back up to let the human move freely, avoiding collisions. Human trajectories emerge as crucial cues in Social Navigation, but they are partially observable from the robot's egocentric view and computationally complex to process.

We present the first Social Dynamics Adaptation model (SDA) based on the robot's state-action history to infer the social dynamics. We propose a two-stage Reinforcement Learning framework: the first learns to encode the human trajectories into social dynamics and learns a motion policy conditioned on this encoded information, the current status, and the previous action. Here, the trajectories are fully visible, i.e.,  assumed as privileged information. In the second stage, the trained policy operates without direct access to trajectories. Instead, the model infers the social dynamics solely from the history of previous actions and statuses in real-time.
Tested on the novel Habitat 3.0 platform, SDA sets a novel state-of-the-art (SotA) performance in finding and following humans. 

The code can be found at https://github.com/L-Scofano/SDA.

Published as a conference paper at ICLR 2025
FOLLOWING THE HUMAN THREAD IN SOCIAL NAVIGATION
Luca Scofano
Alessio Sampieri
Tommaso Campari Valentino Sacco
Indro Spinelli*
Lamberto Ballan
Fabio Galasso
Sapienza University of Rome
Fondazione Bruno Kessler 3 University of Padova
ItalAA
ABSTRACT
The success of collaboration between humans and robots in shared environments relies the robot's 's real-time adaptation to human motion. Specifically, in Social Navigation, the agent should be close enough to assist but ready to back up to let the human move freely, avoiding collisions. Human trajectories emerge as crucial cues in Social Navigation, but they are partially observable from the robot's s egocentric view and computationally complex process.
We present the first Social Dynamics Adaptation model (SDA) based on the robot's state-action history to infer the social dynamics. We propose two-stage Rein- forcement Learning framework: the first learns encode the human trajectories into social dynamics and learns motion policy conditioned on this encoded infor- mation, the current status, and the previous action. Here, the trajectories are fully visible, i.e., assumed privileged information. In the second stage, the trained policy operates without direct access to trajectories. Instead, the model infers the social dynamics solely from the history of previous actions and statuses in real-time. Tested on the novel Habitat 3.0 platform, SDA sets a novel state-of-the-art (SotA) performance in finding and following humans.
The code can be found at https://tthab.ccmmm-SSooo//////
INTRODUCTION
Traditional navigation techniques within Embodied Artificial Intelligence (EAI) have marked crucial advancement by introducing robots into real-life environments. However, these techniques have primarily focused on agents traversing vacant spaces. Conversely, the significance of social navigation within EAI has steadily increased. Social navigation entails agents' capacity to navigate human-centric environments while considering human movements and behaviours. These agents need to be able to locate, track, and interact with humans in a safe and socially acceptable manner. Previous studies predominantly characterized Social Navigation
variation of PointGoal Navigation, wherein agents strive to reach specified destinations while considering human movements (Wijmans et al. 2019b] Ye al. 2021 Partsey et 2022). Habitat 3.0 (Puig al., 2024 a significant breakthrough in EAI, introduces a lifelike environment seamlessly incorporating human avatars. This integration enables investigating human-agent interactions within a controlled, risk-free, dynamic environment. What sets Habitat 3.0 (Puig et al., (224)) apart is its ability to replicate complex scenarios where human intentions are constantly changing. Nevertheless, this dynamism also presents particular challenges, such as collision avoidance and achieving success in locating and following humans. Finding person and following them relevant to human-roke robot collaboration. For instance, in rearch-and-reson operations, robot may need to find and reliably follow human responder through unpredictable and hazardous terrain to assist, carry equipment, or relay critical information. Similarly, in assistive robotics, such as elder care, robots must follow caregivers patients across different rooms in home, adapting seamlessly to changes in speed, direction, and environment. The complexity of the task stems from difficult long-term human motion prediction and the possible changes of their intentions, from having to navigate unknown and dynamic environments, and from strict safety requirements.
* Equul contribution. Corresponding authors, email: scofaano @ Auniromalli li. Part of this work has been accomplished while Alessio Sampieri was visiting the Panasonic R&D AT Labs (PRDCA), CA, USA

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Social Dynamies Regnession
π
Uuman Trajectory
Perception
Prev. Actions
Perception History
Perception
Stage
Stage
Figure I: We present novel Social Dynamic Adaptation model (SDA). The framework involves two stages of training that allow the model to infer, given its past observations and actions, another agent's Social Dynamics. In the first training stage, the model embeds the followed agent's trajectory. which, together with sensor perceptions, compose the input to the model's Social navigation Policy (TT). The knowledge obtained from the human trajectory strongly helps the navigation policy in finding and following agent. However, this information is often not available during deployment. In the second stage, SDA learns to adapt past statuses and actions, which are always available, to the first stage's 's Social Dynamics embedding 2. As depicted in the figure, the status contains depth maps and BB detection of the person, observable from the egocentric robot view. is then paired with current observations input the frozen
Despite notable efforts in collision avoidance and safety, most existing methods for Social Navigation either rely privileged information that is not available in real-world scenarios or do not adequately capture the social dynamics and norms of human behaviour. For instance, (Wijmans et al. 2019b) and (Ye al. 2022)) use a GPS and compass sensor provide the agent with perfect localization, which might be unrealistic even if using SLAM (Mur-Artal et al., 2015 Engel et al. (0144) methods, whenever the human not in the line of sight. While (Partsey al. (022) and (Yokoyama al.) 2022) do not account for the social factors that influence human behaviour. Therefore, this limitation hinders their practical applicability and adaptability to dynamic environments. (Cancelli et al. 2023). J instead, models some social factors in the form of Proximity Tasks but fails to account for the cooperative nature that a social agent must possess, restricting itself merely avoiding collisions with them. The current SotA model proposed in (Puig et al. (024) necessitates privileged information, such as humanoid GPS, which offers polar coordinates, detailing the accurate distance and angle of the human from the robot, to attain nigh-performance outcomes. However, this requirement is highly impractical in real-world environments during inference.
This paper proposes novel Social Dynamics Adaptation model (SDA), shown in Fig. that effectively solves the robot's awareness of complex human behaviors, even temporarily losing sight of the person and fast robot motion. Specifically, the first stage trains a base policy considering human trajectories encoded into a latent vector. The latent vector is a low-dimensional, nonlinear projection of the human trajectories, and it trained end-to-end with the base policy extract the social factors that led the robots to choose better actions. The subsequent supervised adaptation stage regresses this latent vector using only the robot's 's state and action history. Unlike previous methods, such as (Wijmans et al., 2019h) and (Yokoyama et al., (2022), which often depend on simulated privileged information or simplified social behavior models, our approach adapts to dynamics resembling real-world conditions in real-time. In summary, SDA adapts and accounts for unpredictable human behaviors by exploiting privileged information during training and recovering this fundamental signal during deployment when it is often impractical to compute. Finally, the deployed robot features the motion policy, learned in the first stage, and the social dynamics, inferred from prior statuses and actions.
Out of extensive benchmarking, SDA outperforms the approach proposed in Habitat 3.0 (uug al. 2024) and second adapted best-performing method (Cancelli et al., 2023) from Habitat
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2.0 (Szot et al., 2021). We conduct thorough experimental evaluation of the core contribution of this work work-nearnng to infer social dynamics from (privileged) information about the person. Although our primary focus is algorithm development, we also seek to improve the robustness of our method for potential real-world applications. To this end, we conduct experiments that bridge the gap real-world scenarios by introducing noise to the input, reducing the refresh rate of the sensors, and modifying the simulator to reflect more realistic human behavior. Our ablative studies reveal that human trajectories are not only strong input information for the robot control policy but also provide better supervision for inferring the social dynamics latent to the same policy. Other oracular information, such as the humanoid GPS (direction and distance from the person to the robot), serves as powerful sensors for the control policy but does not facilitate adaptable social dynamics for inferring human-robot-scene interactions.
2 RELATED WORK
Embodied Navigation. Recently, there has been surge in exploring indoor navigation within an embodied framework (Deitke et al., 2022). This upswing has been facilitated primarily by the availability of large-scale datasets comprising 3D indoor environments (Chang al. 20h7]SShen
[Shen]
et al. [021; [Ramakrishnan et al. 2021) and simulators designed for simulating navigation within these dynamic 3D spaces (Savva al. 2019; Shen al., 202 Kolve al.. 2017). Nevertheless, these simulators are not equipped to handle human entities within the environments, restricting the investigation to navigation tasks in scenarios where the agent functions independently or, at most, alongside humans simulated via static meshes that simulate movement (Xia et al. 2020 Yokoyama et al. 2022). These simulated humans are treated as dynamic obstacles and lack compliance with any social construct. This constraint has been effectively addressed with Habitat 3.0 (Puig et al. 2024). the simulator used for this research. Habitat 3.0 introduces the capability to simulate the behaviours of humans engaging in tasks within dynamic environments, thus overcoming the limitations mentioned above.
Thanks to these simulators the realm of EAI has witnessed the introduction of numerous tasks (Deitke ict al. 2022), including PointGoal Navigation (Wijmans et al./ (201b)), ObjectGoal Navigation (Batra
ct al. 2020 Embodied Question Answering (Wijmans et al. (019a)). and Vision and Language
Navigation (VLN) (Anderson et al. 2018: Krantz et al. (020). Various modular approaches (Campari
al. 2022] Chaplot et al.] 2020b Ramakrishnan et al. [2022] Raychaudhuri et al.) (2024) have been
proposed to address the challenges of navigating through static, single-agent environments. These approaches utilize maps constructed from depth images and conduct path planning directly on these maps. However, these approaches are unsuitable in social settings where dynamic objects (humans) move within the environment. This is because humans are observable only within the agent's 's field of view (FOV). Moreover, the agent must address the additional challenge of tracking and mapping End-to-end Rl--irdd policies (Wani et al. 2020 Partsey et al., 2022 Ye et al., 2020 Campari et al. 2020 Ye et al. (021), should be adapted to learn also social clues similarly to (Cancelli et al., 2023). ). where the agent learned proxemics information about the humans moving in the environments thanks to two proximity tasks. In this paper, instead, we try to learn social behaviours directly by internally modeling the humanoid trajectories in the latent representation of the agent. Furthermore, differently from (Cancelli et al.) 2023), the agent's aim is no longer just avoiding collisions. Still, it involves locating this dynamically acting human and following them for a specified number of steps while maintaining safe distance. This evolution of the task demands heightened level of social comprehension from the agent, requiring the anticipation of the person's 's intentions and the ability to trail them closely without compromising safety.
Socially-Aware Navigation. Research in robotics, computer vision, and the analysis of human
social behavior explored socially aware representations and models (Möller et al., T). Extending
from the realm of collision-free multi-agent navigation (Berg et al. 2011 Van den Berg et al. 2008)
Chen et al.) 2017b Long et al. (017)) and navigation in dynamic environments (Audd et al. 2013).
researchers have further expanded their investigations to encompass scenarios involving human
presence (Guzzi et al. 2013 Ferrer et al. 2013 Chen et al. 2017a Lu al. 2022 Chen et al.
2019).
The approach presented in (Chen et al. 2017a) incorporates collision avoidance algorithms like
CADRL (Chen et al. (017b) while introducing common-seme social rules. This integration aims to reduce uncertainty while minimizing the risk of encountering the Freezing Robot Problem (Trautman
& Krause 2010). Other works (Ferrer al. 2013 Chen et al. 2019) seek to model human-agent
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Human Trajectrry
4--8
Encoder (1)
Policy (f)
Stage
Baynos
Zi, Bru
Knvironment
Stuge
Statodkttio Histury
Adapter(s))
Policy (+)
My Re-3
Stemoo depth
Ribox
Arm depth
Human Trajestury
Figure 2: Pipeline of the novel methodology proposed. First, we jointly learn to encode human trajectories and motion policy. In the next stage, given the previous states and actions, we infer the social dynamics and pass the estimated latent vector to the frozen policy.
interaction by employing techniques such as spatiotemporal graphs (Lu et al. 2022). Typically, these methods undergo testing in minimal number of environments that offer complete knowledge about the human positions and velocities (Chen et al. 2019 2017b), featuring simple obstacles and often assuming collaboration between moving agents agents(Ferrer Ferrer et al. 2013: Chen et al. 2019). In contrast, our focus revolves around SocialNav within expansive indoor environments, characterized by partial knowledge about them, since the agent perceives the environment only through its sensors from egocentric perspective, and no information about the velocity or position the human given to the agent. Our SDA addresses the missing information from the interacting human position, inferring from the robot's history of actions and status.
Modelling dynamic environments. Simulated environments (Li al. 2021 Tsoi al. 2022 Puig
et al. 2018) offer privileged information about the scene whose exploitation can be computationally intense or unfeasible during deployment. While navigating social environments, it is vital take into
consideration human behaviour (Kivrak et al.) (002)) Ideally, one would want to forecast people's position for better path planning (Patle et al. 2019), but forecasting robot-person interactions significantly slower (Rahman et al. (023) than navigation policies, hence being challenging to be considered for training or deployment. To overcome this problem, we leverage literature on system identification (Ahmed & Qin 2009: Guo et al. (016)) to infer the encoded privileged information during robot navigation. Once encoded in latent space during the first training stage and used train the primary policy, it is possible to asynchronously derive that same information from the state-action history (Kumar et al. [202 Loquercio et al. 2023 Zhang et al. 2023 Liang et al. [2024] Kumar al. 2022 Qi et 2023), influenced by the signal want identify. Unlike previous works, we are the first to identily the social dynamics (under the form of human trajectories), with the intuition that modeling human behavior is fundamental for efficient Human--obel collaboration.
METHODOLOGY
This section introduces SDA: novel framework for social navigation that incorporates human trajectories into the sequential cecision-making process. The first stage of our approach focuses on encoding human trajectories into latent social context to represent the social dynamics that are functional to the agent motion policy. In the second stage, we introduce the Adapter module, which enables the estimation social information from the agent's past behavior. Recovering this signal allows the robot to operate without explicitly representing human behavior. We detail the training process, trajectory modeling, and optimization techniques utilized in our approach, highlighting its effectiveness in addressing the challenges of social navigation.
Problem formalization.
The problem requires to locate and follow humanoid in motion within an indoor environment, maintaining distance of to 2 meters for at least steps (Puig al.
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2024). With the status 2t, represent the agent's "perception" at time t. We exploit depth images from different cameras placed on the robot and a preprocessed version containing a humanoid detection bounding box. These perceptions are processed with a ResNet (He et al. 2016) before being fed to the recurrent policy network, selecting the hest action Oe at time I. We use Decentralized Distributed Proximal Policy Optimization (DD-PPO) (Wijmans et al.] 2019b) to iteratively improve the agent's s policy while maximizing rewards derived from interactions with several environments executed in parallel, ensuring stability through controlled policy updates and lower training times. The pipeline described above can be considered baseline implementation that does not contain privileged information but relies only on the robot's 's onboard sensors. To address the social aspect required by the task, we consider additional details on the humanoid, e.g., "social dynamics", defined as PttNut-11 where N refers to the trajectory length. ét-p represents the position n steps before time I, and e refers to the earliest position in the trajectory under consideration. The notation N:- is used as a shorthand to indicate the complete trajectory from N steps in the past to the current time, representing absolute x-y coordinates. In the following sections, describe how exploiting this information at training time can improve performance during deployment when is absent.
Stage 1: Social Policy. Recurrent policies such as DD-PPO take as input the current status, in our case, a collection of depth images processed via a Resnet 2t, and the action at the previous time-step a--1- We add another input to this pipeline, namely a latent vector 2, built by encoding the humanoid privileged information e:-N:11:
24 u(ei-N:-1)
Qr (-t, 01-1,2))
Intuitively, by training everything with the same objective 2, encodes the social dynamics that led the agent to maximize its reward, adapting to human movement patterns. Additionally, including trajectory data allows the agent learn from past interactions and experiences. In our approach, the trajectory encoder (μ²) is implemented as Multilayer Perceptrons (MLPs). The objective retains its usual formulation without any explicit reference the human trajectories:
L CLIP = Ex min (rr())Ar, clip (r: - e, e,
+ A.)]
where (0) is the ratio of the probability of action de under the current policy and the previous one that is being executed for gathering data. A, represents the advantage function at time f, guiding the policy towards actions that yield higher expected rewards. Defining what information considered "privileged" and why is essential. In our context, it refers to detailed knowledge about the humanoid the environment, such as the exact position of humanoids defining a trajectory (traj.), or the relative position with respect to the agent often denoted as humanoid GPS (Puig et al. (hG4)) We can easily gather this information in simulated environments. However, collecting them in real-life scenarios is often impractical. This distinction is crucial as it highlights the challenge of transferring learned policies from simulation to the real world.
Stage 2: Social Dynamies Regression. We aim extract and exploit social cues directly from the robot's perception and eliminate the need for auxiliary devices like GPS trackers on humanoids. Inspired by (Kumar et al. 202T), we introduce the "social dynamics" module (Adapter), parametrized by an MLP & that takes as input the recent history of the robot's ohooss states Nt-Nat1 and actions N:-Na11 to generate a new latent vector &n:
2 =
Gt Ns-N
We obtain the state-action history by deploying the agent in the environment with optimal policy л obtained after the first stage and the latent vector &;
a. n"" (Ke, Oe-11 2x)
During this process, optimize the Adapter, MLP,, with a supervised regression objective, Mean Squared Error (MSE), to recover the original information contained in 2, that we compute relying on the preferential information trajectory, MSB( (5,, 24) = || 21112- Once we finalize the Adapter
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training, instead of relying the privileged information, can depend upon the robot's states Ft-N:--1 and actions a;-N:t- to generate &t, serving as an estimate of the actual latent social dynamics vector 2z. Doing so enables the agent to estimate social dynamics online, improving its performance in dynamic environments and enhancing its social navigation capabilities, freeing it from dependence upon external sensors.
RESULTS
Section 4.1 outlines our findings on Social Navigation, along with an ablation analysis of adaptable information. Additionally, Section [42 offers qualitative examination of our results, and an analysis of the role played by the latent vector &, A more detailed analysis and further qualitative results can be found in the Appendix.
Simulator. We tested SDA Habitat 3.0 (Puig al. 2024). simulation platform designed for human-robot interaction within domestic settings. This platform offers precise humanoid simulation capabilities with focus on collaborative tasks such as Social Navigation and Social Rearrangement offers a vast library of avatars featuring multiple genders, body shapes, and appearances. Furthermore, it employs an oracle policy to generate movement and behavior, enabling programmable control of avatars for navigation, object interaction, and a range of other movements.
Baselines. The baselines we employ in our study are drawn from Habitat 3.0 (Puig et al. 2024 and consist of: (i) Heuristic Expert: heuristic baseline equipped with access to the environment map, employing a shortest path planner to devise a route to the current location of the humanoid. The heuristic expert operates on the following principles: When the agent is beyond distance of 1.5 meters from the humanoid, it employs a "find" behavior, utilizing a path planner to approach the humanoid. Conversely, if the humanoid is within 1.5 meters, the expert executes a backup motion to prevent collision with the humanoid. (ii) Baseline: the current SotA method (Puig et al., 2024), a recurrent neural network policy trained with DD-PPO (Wijmans et al.. 2019b), operates on "ssessoss-o-accion" depth sensors, a humanoid detector, and humanoid GPS coordinates, while the outputs are velocity commands in the robot's 's local frame. Table I compares our model in realistic configuration, where the humanoid GPS data are unavailable. (iit) Proximity tasks: we also adapted the Proximity Tasks defined in (Cancelli et al. 2023) and applied them to the baseline (Puig al. 2024). These tasks were proposed for a different setup of SocialNav, where the agent acts in an environment with multiple humanoids and must navigate from point A to point B while avoiding collisions. We adapted the risk and compass proximity tasks to the SocialNav setting addressed in this article. In this context, the risk has a low value (close to 0) when the agent is within 1 to meters from the humanoid and a value close to when the distance is less than meter or greater than 2 meters. Similarly, given the presence of only one humanoid in the environment, the compass was redefined to predict the angle between the humanoid and the agent. This adjustment aims to assist during the following phase, enabling the agent to follow the humanoid while maintaining a safe distance and staying aligned with the human. The proximity tasks are jointly trained with the policy and detached during evaluation.
paradigm. Inputs to this policy consist of egocentric arm and stereo
Metrics. We used the metrics for the SocialNav task as defined in (Puig et al. 2024). Finding Success (S) is the ratio of the episodes where the agent located and reached the human. Finding Success Weighted by Path Steps (SPS) measures the optimality of the path taken by the agent wrt the optimal number of steps needed to reach the human. Following Rate (F') is the ratio of steps during which the robot maintains a distance of 1-2 meters from the humanoid while facing towards it relatively to the maximum possible following steps. Collision Rate (CR) is the ratio of the episodes that ended with the robot colliding with the humanoid. Episode Success (ES) measures the ratio of the episodes where the agent found the human and followed it for the required number of steps, maintaining safe distance in the 1-2 meters range.
Privileged information. In our work, the privileged information under consideration includes humanoid GPS (hGPS) and human trajectories (traj.). Humanoid GPS is represented in polar coordinates, method of specifying a point's position in plane using two parameters: the distance from the point to the origin (radius) and the angle formed between a reference direction (typically the positive x-axis) and a line connecting the origin to the point (polar angle or azimuth). In our context,

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the origin is defined the robot's position; thus, its position is implicitly known along with that of the human. Conversely, trajectories solely consist of information derived from the human's 's position within the environment.
4.1 QUANTITATIVE RESULTS
In [ we present the results obtained in Habitat 3.0 (Puig et al. 2024) for the Social Navigation task. The table is divided into three sections. The first one displays outcomes achieved by utilizing a heuristic expert endowed with extensive information, including its position, map data, and the humanoid's position, granting it competitive advantage over other methods. The subsequent section features models trained and tested using ground truth (GT) data (Baselines and Stage 1), thus establishing upper limit for techniques utilizing more practical inputs feasible in real-world scenarios. Lastly, the final rows delineate results from methods conducting inference without privileged information. The models that use privileged information (GT) show that S1 has lower performance than the two Baselines, especially in S and SPS. This is to be expected, considering that while trajectories solely depict the movement of the human in the environment, hGPS furnishes crucial details on locating the humanoid, offering insights into the distance and angle between them. In the second stage, despite the absence of human trajectory input, SDA keeps the performance level of Stage I by adapting to the social dynamics. Our model outperforms the baselines in the find task, increasing S and SPS by 6%.
Our approach generally improves performance in the episode success (ES) metric, which occurs when the agent finds the humanoid and follows it for 400 steps. However, we emphasize that the test-time episode ends at 1500 steps or when the agent collides with the humanoid (Puig et al., 2024), not after the 400 follow steps. In this context, S, SP, and F metrics demonstrate how SDA, compared to the Baseline, more frequently locates the humanoid, follows more optimal path (on average 438 vs. 540 steps), and follows it for a longer duration (390 vs. 218 steps). Therefore, as the agent follows the humanoid longer, it has more chances to collide with it, given that the episode does not necessarily end after the required steps. In a scenario where the test-time episode concludes either after 400 follow steps or immediately upon collision between the agent and the humanoid, SDA and the Baseline show a comparable collision rate (C)),, 0.39, and 0.38, respectively.
Table 1: Main results for Social Navigation. Within the table, GT denotes ground truth privileged information and corresponds to reproduced results.
Models
hGPS
traj.
5
SPS f
Ff
CR +
ES
Heuristic Expert Puig al. (2024)
1.00
0.97
0.51
0.32
Baseline Puig et al.
2024
GT
0.97
0.65#0.00
0.44
0.51#0.03
0.55
Basxline-rroximity Cancelli al. 2023
GT
0.97
0.64
0.57
0.5g
0.63
SDA S1
GT
0.92
0.4610.01
0.4410.02
0.6140.02
0.50
Baseline Puig et al.
2024
0.76
0.34#0.01
0.29
0,48+0.00
0.40
Bascline Cancelli al, SDA $2
2023
0.85
0.40
0.3740.01
0.5g
0.41
0.01
0.45+0.01
0.39
0.57+0.02
0.43+00.02
Adaptable information. Table presents the results from Stage 1 (SI) and Stage 2 (S2) with the utilization various privileged information, such as Humanoid GPS (hGPS) and human trajectories (traj.). During S1, particularly in S and notably in SPS, incorporating hGPS input leads to superior results. This advantage is likely attributed to hGPS containing implicit information about human and robot positions, facilitating more efficient path selection, especially in SPS scenarios. However, this pattern is not evident in S2, where trajectories typically provide more adaptable information. As previously mentioned, hGPS inherently encompasses the robot's position, posing challenges in regressing this data during the second stage due to the lack of initial context regarding the robot's location relative to the environment. Furthermore, hGPS may be difficult to adapt when the human is detected and disappears around a wall. Since hGPS comprises polar coordinates, the distance between the robot and the human spans the wall. This issue does not arise when utilizing trajectories, as they do not require any information about the robot and can be adapted solely based on depth images and detection information.
additional
"ooee refactored and adapted from Cancelli al. 2023 Habitat 3.0.
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Table 2: Ablation studies for social dynamics estimation. information, and A indicates the adapted information.
GT denotes ground truth privileged
Models
hGPS
Ira..
4GPSA
Iraj.A
S
SPS 1
Ff
CR
ES
S1
GT
GT
0.94
0
0.45
0.64
0.52
SI
GT
0,93
0.62#t0.0
0.46
0.64
0.48
S1 (Proposed)
GT
0.92
0.46
0.4440.02
0.61 10.02
0,50-40.01
S2
0.57
0
0.055b0.0
0,30
0.02#
$2
0.70
2..02..02
0.05
0,70
0.03 10.01
S2 (Proposed)
v
00.1
0.45
0.39
0.57#0.02
0,43#0.02
4.2 QUALITATIVE RESULTS
We qualitatively demonstrate the results in our proposed SDA. Firstly, we showcase the agent's 's ability locate the humanoid within the environment by moving around, followed by its capability to follow the humanoid within the environment. Subsequently, agent briefly spots the humanoid and one where it moves backward to create space for passage. Fig, shows episode where the agent and the human located in different rooms at the start. Then, the agent begins its search for the humanoid by navigating within the environment until the encounter takes place. After the encounter, the agent then transitions into the follow phase.
present two specific behaviors where the
Humanoid start position
Robot start position Humanoid found
The agent and the humanoid start the episode in separate rooms. The agent navigates through the environment in search of the humanoid, and once found, begins to follow it.
Figure 3:
Latent Analysis. We further investigate the implications of our approach and the role of inferred human behaviors in robot decision-making. In Figure 4 we present a latent space analysis of behaviors, where the t-SNE projection reveals four key bchavioral stages. Find: The robot has not yet detected human in its RGB camera frames and remains outside the zone of interest, typically at a distance greater than 1-2 meters. Seek: The human has been detected but is still beyond the zone of interest. The robot moves toward the human to reduce the distance. Lost: The robot has lost sight of the human it previously detected. However, the person remains within the 1-2 meter range, prompting the robot reorient and attempt reacquisition. Follow: The human is actively tracked and remains within the 1-2 meter zone, allowing the robot to continue following the person. On the left, dots overlaid with a gradient from black to white illustrate a representative experiment, where the color gradient indicates the progression of time. This visualization effectively captures the robot's adaptive behavior as transitions between modes: the robot starts in Find mode, shifts to Seek, and then enters Follow mode upon detecting the person. At times, when the human moves behind an obstacle (e.g., a wall), the robot switches to Lost mode. Eventually, the robot reacquires the person and resumes following until the episode concludes. The cluster-colored scatter plot on the right further emphasizes the overlap between behaviors like Seek and Follow, as both involve observing and responding to the human's 's position, leading to shared characteristics in the latent space. Similarly, the transitions between stages such as Lost and Seek are continuous rather than discrete, aligning with the fluid nature of human-robot interactions.
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Time Progression Scatter
Cluster-Colored Scatter
100
100
Clasters
Find Cluster
Scek Cluster)
75
75
Cluster
0.8
50
0.
Time Progressoon
25
0
-25
-25
-50
-30
02
-75
-75
-30 -25
Figure 4: Latent Analysis
5 TOWARD
SCENARIOS
While simulated environments have advanced significantly, they still fall short in capturing the full unpredictability of real-world interactions. To address this limitation, we take incremental steps toward real-world scenarios by proposing tests that incorporate more realistic social behavior, constrained computational resources, imperfect communication between the adaptation module and the primary policy, and noisy sensors. Although conducted in simulation, our work aligns with standard practices in the Embodied AI literature (Campari et al., 22200] Raychaudhuri et al. 2024, Cancelli et al. 2023; Chaplot al. 2020a by Ye et al. 2020; 2021 Yokoyama et al.) 2022 Majumdar et al. 2024), which utilize simulated environments to rigorously evaluate and iterate on agent behaviors. In contrast, "Sim2Real" studies are typically standalone works (Gervet et al. 2023 Partsey et al. 2022) that focus specifically transferring policies and learned behaviors from simulated environments to real-world applications. The anticipated conclusion is that more realistic human behavior and sensor readings likely contribute to the sim-to-real gaps. However, incorporating realistic training samples can partially recover performance loss. Additionally, the performance gap identified between the Baseline (Puig al. 2024) and our proposed SDA remains consistent across metrics, underscoring the algorithmic novelty of our simulated-only study.
ORCA. We have augmented the motion policy of humanoids in Habitat 3.0 by making them aware of the robot presence, with reciprocal collision avoidance (ORCA) (Snape et al. 2010). When used on two agents (or more), it provides sufficient conditions for collision-free motion by letting each agent take half the responsibility of avoiding pairwise collisions. In our case, however, the humanoid; meanwhile, the robot still relies on its end-to-end policy. This removes unrealistic behaviors where the human sees the robot and goes straight into Table T shows lower collision rate (CR), 37% instead of 57% and higher episode success (ES) 48% vs. 43%; meanwhile, we notice a slight decrease performance in the other metrics.
applied only to
Table 3: Comparison of SDA performances on plain Habitat 3.0 versus the variant with ORCA. SDA
S
SPS +
F1
CR
ES f
Habitat 3.0
0.91
0.45=0.01
E..01
0.57=0.02
0.43=0.02
Habitat 3.0 + ORCA 0.90 10.01
0.43 0.43=0.02
0.38 10.01
0.37 10.01
0.48 10.01
Lower frequency updates. In Table we simulate a scenario where the adaptation module operates at lower frequency (i.e., with a reduced update rate) due to potential computational constraints during deployment. In SDA, the vector is typically updated at each timestep. We tested two alternative settings where updates occur once every two every this results in only a slight performance degradation in the metrics related to the finding aspect the task, while overall performance improves (ES), driven by enhancements in the following (F) task
hundred timesteps. Interestingly,

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itself. This improvement can be attributed to slower sensor update rates encouraging the agent to focus on more stable behaviour avoiding unnecessary short-term adjustments, leading to smoother navigation and more effective long-term strategies.
Table 4: SDA performance considering missing readers on Habitat 3.0.
Update Rate
S
SPS
Ff
CR 4
ES f
(Proposed)
0.91#U.11
0.45=0.01
0.39
0.57=0002
0.43=0.02
1/2 1/100
0.87
0.39
0.44
0.63
0.48
0.85+0.01
0.38=0.01
0.43#0.01
0.64
0.46+0.01
Noisy inputs. We evaluate the addition of noise both the sensor input (depth images and bounding boxes) and actuators. [ the results after fine-tuning both SDA and Baseline policies for IM steps. We used Gaussian noise on the Bounding box human-detector, Redwood noise on the Depth camera (the policy does not use the RGB) (Partsey et al.. 2022 Choi et al. 2015), and Gaussian noise on the high-level actuators (Partsey | 2022; Choi et al. 2015). the agent's s angle and velocity. Analyzing the results in Table 5 we note that the overall largest drop performance regards Finding Success (S), dropping from 91% to 81-83%. As consequence of this, the collision rate (CR) actually improves since the robot needs to follow the humanoid for less time. The performance in follow (F) is mostly affected by noise in the bounding boxes (from 39% to 30%). The performance in episode success is mostly affected by adding both depth camera and bounding box noise (from 43% to 25%). Importantly, the gap between SDA and the Baseline (Puig et al. 2024) remains consistent across all noise types and all metrics, which supports the validity of Tests in simulated environments.
Table 5: Ablation study with RedWood Noise on the Depth Camera, and Gaussian Noise on the Bounding Box and Actuators.
Model
Noisy Input
S
SPS f
F
CR
ES
Baseline
None
0.76
0.34
0.29
0.48=0.03
0.40
SDA
None
0.00
0.45=0.001
0.39+0.01
0.57=0.02
0.43#00.02
Baseline
Depth Camera
0.70
0.32
0.26
0,43
0.20
SDA
Depth Camera
0.83+0.02
0.42=0.03
0.34+0.02
0.30=0.01
0.37+0.01
Baseline
Bounding Box
0.73=0.01
0.30=5.01
0.24
0.44=0.02
0.15#0002
SDA
Bounding Box
0.83+.0.01
0.41#0.02
0.30
0.28=
0.35 :..00
Baseline
Depth Camera + Bounding Box
0.69+:0.01
0.33
0.25
0.44=0.02
#0.02
SDA
Depth Camera Bounding Box
0.824:0.01
0.41
0.48
0.45
0.25
Baseline
Actuators
0.71 10.01
0.29 10.01
0.24
0.42 0,42 10.02
0.31 10.02
SDA
Actuators
0..0
0.41
0.35+0.01
0.42
0.40++002
Limitations. Table illustrates how the performance in stage 2 is affected by the adaptability of information, underscoring its importance in evaluating the model's effectiveness. Additionally, limitation of our approach is that the proposed model has been benchmarked solely in simulation, relying on training information such as trajectories -thtt may not be readily available in real-world settings.
CONCLUSIONS
Our study presents the Social Dynamics Adaptation model (SDA) for Social Navigation. Notably, it is the first to integrate privileged human dynamics information during training while adapting it in the following stage, enabling its application in realistic environments without relying on such privileged information. Our findings underscore the non-trivial nature of adapting information, highlighting the necessity for selective processes. In future research, we aim to extend our model by incorporating diverse human dynamics beyond trajectories, enhancing the robot's comprehension of human movement patterns.
We are grateful to Panasonic for partially supporting this work. We ac- knowledge the financial support from the PNRR MUR project PE0000013-FAIR and from the Sapienza grant RG123188B3EF6AA0 (CENTS). Thanks CINECA and the ISCRA initiative for high-performance computing resources and support.
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A APPENDIX
The content structured as follows:
In this appendix, provide additional information and detailed analyses that complement the main paper. The appendix is organized as follows:
Metries (Sec.A..) A.I): This section expands on the metrics presented in the primary paper and introduces supplementary ones, as exemplified in (Puig et al., 2024). We explain the definitions, motivations, and computation details for each metric used our evaluation.
Training Details (Sec. A.2 Here, we outline the training methodology, including the training stages, the use of DD-PPO, and the hardware setup for training. This section provides further context how the models were optimized.
Results (Sec. A.3): This section includes comprehensive tables listing all performance metrics along with an accompanying explanation of the results. We detail how different experimental conditions affect performance.
Simulated Changing Human Motion Patterns (Sec. A.4]:
This section details experiments where we simulate varying human motion patterns to account for scenarios with abrupt speed changes (c.g., constant, reduced, and random speeds). We compare the performance of SDA under these conditions.
Statistical Significance and Robustness (Sec. A.5 : In this section, present statistical analyses (Welch's 's t-test) to validate the improvements of SDA over the baseline and Can- celli et al. (Cancelli al. 2023). The reported t-statistics and p-values confirm that the performance enhancements are statistically significant.
Adaptability Multi-Human Interactions (S66: This section evaluates the perfor- mance of SDA in environments involving one, two, and three humans. We show how the increased task complexity affects key metrics such as success rate, following frequency, and collision rate, and demonstrate that SDA consistently outperforms the baseline.
We analyze failure cases, identifying key areas where the model encounters challenges such constrained movements and blind spots. The analysis is supported by quantitative and qualitative examples.
Error Analysis (Sec. A.7):
Ablation Studies (Sec. A.8): This section explores the effect of privileged information and trajectory length model performance. We present ablation experiments that isolate these factors.
Visual examples of episodes where the agent successfully tracks or avoids collisions with humans are presented here, accompanied by qualitative analysis to illustrate strengths and failure modes.
Qualitative Results (Sec. A.9):
Moreover, show some generated episodes in the enclosed supplementary video.
A.1 METRICS
In our main paper, we utilize metrics including Finding Success (S), Finding Success Weighted by Path Steps (SPS), Following Rate (F"), Collision Rate (CR), and Episode Success (ES). However, Habitat 3.0 (Pugg et al. 2024) introduces in their supplementary material additional metrics such as Backup- Yield Rate (BY R), Total Distance (TD), and Following Distance (FD), providing further insights into the models. Similarly, in our supplementary material, we also incorporate these metrics. Subsequently, comprehensive explanation will be given for all the former and latter metrics featured.
(1) Finding Success (S): This metric, denoted S, evaluates whether the robot successfully locates the humanoid within the maximum episode steps and reaches within a close range of 1-2 meters while facing toward it. It is represented as:
S = {
if the robot successfully finds and reaches the humanoid, otherwise
This metric provides a binary indication of the robot's 's ability to locate and approach the humanoid within the designated constraints.
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(2) Finding Success Weighted by Path Steps (SPS): The SPS metric, calculated as SPS max((.p) evaluates the efficiency of the robot's path relative to an oracle with complete knowledge of the humanoid's ' trajectory and the environment map. Here, represents the minimum steps an oracle would take find the humanoid, and p denotes the agent's actual path steps. higher SPS value indicates the robot's more efficient path toward finding the humanoid.
(3) Following Rate (F): The following rate F quantifies the ratio of steps during which the robot maintains a distance of 1-2 meters from the humanoid while facing towards it relative to the maximum possible following steps. is calculated as:
W
max E w)
E denotes the maximum episode duration, and w represents the number of steps during which the agent closely follows the humanoid. This metric provides insight into the robot's ability to consistently track the humanoid once it has been located.
(4) Collision Rate (CRR:: The collision rate CR measures the ratio of episodes that end with the robot colliding with the humanoid. It is computed as:
CR Number episodes ending in collision
Total number of episodes
This metric assesses the robot's collision avoidance capabilities during interactions with the humanoid.
(5) Backup-Yiee Yield Rate (BY R): The backup-yield rate BY R quantifies the frequency with which the robot performs backup yield motions to avoid collision when the humanoid is nearby. A "backup motion' refers to a backward movement executed by the robot when the distance between the robot and the humanoid is less than 1.5 meters. Similarly, 'ielld motion' denotes robot's maneuver to avoid collision when the distance between them is less than 1.5 meters and the robot's velocity less than 0.1 m/s. The BY R is computed as:
BYR Number of backup or yield motions
Total number of episodes
This metric provides insights into the effectiveness of the robot's 's collision avoidance strategies.
(6) Total Distance between the robot and the humanoid (T (TD): The TD metric evaluates the average L2 distance between the robot and the humanoid over the total number of episode steps. It is calculated as:
TD L2 distance between robot and humanoid
Total number of episode steps
This metric measures the overall proximity between the robot and the humanoid throughout the episodes, providing insights into the effectiveness of the robot's navigation and tracking capabilities.
(7) Following Distance between the robot and the humanoid after the first encounter (FD): The FD metric assesses the L,2 distance between the robot and the humanoid after the robot initially encounters the humanoid. It quantifies the proximity between the two entities during the following stages interaction. The F should ideally be maintained within 1-2 meters, indicating effective tracking and following behavior. This metric is calculated
Σ L2 distance between robot and humanoid after first encounter
FD
Total number of episode steps
The FD metric provides valuable insights into the robot's 's ability to maintain an appropriate distance from the humanoid target after initial contact, which is crucial for effective interaction and task completion.
(8) Episode Success (F.S) measures the ratio of the episodes where the agent found the human and followed it for the required number of steps, maintaining safe distance in the 1-2 meters range.
A.2 TRAINING DETAILS.
During training, we encode social dynamics using a ResNet, trained from scratch. The trajectory encoder y consists of 3-layer MI.P, and the output 2, has dimensionality of 128. The Adapter
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comprises altermating spatial and temporal MLP layers, with the output & matching the dimensionality of 2t. We jointly train the policy R, the perceptions encoder (ResNet), and the social dynamics (Trajectory Encoder) encoder μ during the first stage. In the second stage, everything is frozen except the Adapter. In Stage I, we utilize DD-PPO (Wijmans et al., 2019b) for 250 million steps across 24 environments, following the training protocol presented in (Puig et al. 2024). Furthemore, each time step is approximately 0.04 seconds, so we consider a trajectory of 0.8 seconds. An entire episode of 1500 steps corresponds almost 1-minute long video. Inputs the policy are egocentric arm depth and a humanoid detector, and outputs are velocity commands (linear and angular) in the robot's s local frame. This policy does not have access a map of the environment. The training process takes approximately four days. In Stage 2, we employ supervised learning for 5 million steps across the same environments. The learning process lasts around 2 hours. Both stages utilize 4 100 GPUs for efficient computation.
A.3 RESULTS
Table 6: Comparative evaluation of Social Navigation on Habitat 3.0 Puig et al. (2024). Within the table, GT denotes ground truth privileged information and corresponds to reproduced results. Beyond what is reported in Table I of the main paper, additionally report here: (1) Backup- Yield Rate (BYR), (2) The Total Distance between the robot and the humanoid (TD), and (3) The
"Following" Distance (in meters) between the robot and the humanoid after the first encounter (FD).
Models
hGPS Iraj.
S
SPS 1
F1
CR +
BYR
TD
FD
ES f
Heuristic Expert Puig al. 2024)
1.00
0.97
0.51
0.52
0.24
2.56
1.72
Baseline Puig et al. [2024)
GT
0.97
0.65
0.44
0.51
0.19
3,43
1.70
0.55*
Baseline+Proxx Cancelli et al.
2023 2
GT
0.97
0.64
0.57
0.58
0.17
3.15
1.66
0.63
SDA S1
GT
0.92
0.46
0.44
0.61
0.18
3.70
1.83
0.50
Baseline Puig et al. [2024)
0.76
0.34
0.29
0.48
0.13
5.18
1.64
0.42*
Baseline Cancelli el al. 2023
0.85
0.41
0.37
0.58
0.14
4.24
1.57
0.41
SDA S2
0.91
0.45
0.39
0.57
0.12
4.24
1.80
0.43
In Table 6 we compare Baseline (Puig et al., 2024), Baseline+Prox. (Cancelli et al.) 2023), and
the novel SDA model on the additional metrics proposed by (Puig et al. (2024). We showcase comparable Backup Yield Rate across the methodologies, meaning that all models suggest an avoidance behaviour. Regarding Following Distance, all models, on average, fall within the ideal 1-2 meters range, with SDA exhibiting conservative behavior, i.e., SDA remains at 1.80 meters further from the humanoid. The Total Distance is lower for SDA and Baseline+ than for Baseline. This due to the ability of the first two models detect the human (SPS) earlier and follow for longer duration (F). In summary, the inclusion of additional metrics such as BY R, TD, and FD leads to analogous conclusions, as previously outlined in the main paper (Sec. 4.1). Specifically, underscores that the comprehensive advantage in adapting to social dynamics stems from following more optimal trajectory, maintaining over an extended period, and ensuring a safe distance.
A.4 SIMULATING CHANGING HUMAN MOTION PATTERNS
We simulate varying motion patterns of humans to account for scenarios where individuals may abruptly slow down due to impediments or reduced mobility. These experiments complement our ablation studies on sensor noise and processing latency (see Section 5). Note: all evaluations are based on policy trained with constant human speed.
Table 7
compares the performance of SDA Stage 2 under three different human motion settings:
constant: Humans move at a fixed speed.
constant/2: Humans move at half the constant speed, simulating reduced mobility.
random: Human speed varies randomly to emulate unpredictable behavior.
The results indicate that:
Code refactored and adapted from Cancelli al. 2023 Habitat 3.0.
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Table 7: Comparison of SDA Stage 2 performances using constant human speed (h.speed) random
human speed in the simulator.
Model
h.speed
$
SPS 1
Ff
CR
ES f
SDA S2
constant
0.91 + 0.01
0,45 = 0.01
0.39 + 0.01
0.57 + 0.02
0,43 = 0.02
SDA S2
constant/2
0.87 0.01
0.57 0.02
0.28
0.01
0.13 : 0.01
0.70 + 0.01
SDA S2
random
0.90 + 0.01
0.45 0.02
0.25 + 0.01
0.48 + 0.01 0.40 = 0.01
Consistency in Navigation Efficiency: Under both constant and random speeds, the Finding Success (S) and SPS scores remain comparable (0.91 vs. 0.90 and 0.45, respectively). demonstrating robust navigation efficiency.
Improved Safety with Reduced Speed:
When humans move at half the constant speed, the agent achieves a Finding Success (S) score of 0.87 along with a significantly lower Collision Rate (CR of 0. 13),, suggesting enhanced safety in slower environments.
Trade-off with Unpredictability: In the random speed scenario, while the Following Rate (F) drops from 0.39 0.25 hig5-hiigliggtting challenges in adapting abrupt motion changes- the Collision Rate also improves (decreases from 0.57 to 0.48), reflecting a more cautious navigational strategy.
A.5
STATISTICAL SIGNIFICANCE AND ROBUSTNESS OF SDA
The proposed SDA method extends the baseline Puig al. (224)) (and not Cancelli al. Cancelli et al. (203)) consistently across all metrics. As shown in Table 6 of the paper, SDA improves the primary tasks of:
Finding the human (Finding Success, S): Improved from 76% 91% (an increase of 15 percentage points).
Reaching the human using an optimal path (SPS): Improved from 0.34 to 0.45 (11 percentage points), and
Following the human (F): Improved from 0.29 to 0.39 (10 percentage points).
While the overall improvement in Episode Success (ES) more modest (from 0.41 to 0.43), this is primarily attributable to an increased Collision Rate (CR). As discussed in line 347 of the main paper, SDA finds the human earlier and follows them for significantly longer-on average, 390 steps instead of 218. This extended following duration introduces greater challenge in avoiding collisions, thereby moderating the improvement observed ES.
To evaluate the statistical significance of these improvements. performed independent two-sample t-tests assuming unequal variances (Welch's 's t-test). The results, presented Table [8] confirm that the improvements of SDA over both the baseline and Cancelli et al. Cancelli et al. (2023) statistically significant.
Table 8: Updated t-statistics and p-values for the comparison of methods.
Metric
Basxiine+Proximity
SDA S2
I-statistic
p-value
Significance
S
0.85 = 0.02
0.91 = 0.01
-8.49
0.00000103
Significant
SPS
0,41 + 0.02
0,45 + 0.01
-5.66
0.0000732
Significant
F
0.37 + 0.01
0.39 + 0.01
-4.47
0.000295
Significant
CR
0.58 + 0.02
0.54
4.47
0,000295
Significant
ES
0.42 + 0.01
0.45 0.02
-4.24
0.000924
Significant
A.6
ADAPTABILITY MULTI- INTERACTIONS AND TASK COMPLEXITY
While current focus is single-human interactions, framework is inherently flexible and capable of adapting to more complex social settings involving multiple humans. To provide insights into performance under such conditions, we evaluated both the SDA method and baseline in
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environments containing one, two, and three humans. Note that, due to the time constraints of this rebuttal, the evaluations are based on policy trained exclusively with a single human.
The results, summarized in Table 9 highlight the growing difficulty of the task the number of humans increases. For example, in single- human scenarios, SDA achieves a success rate (S) of 0.91; however, this decreases to 0.70 in three-human scenarios. Similarly, the frequency of reaching the target (F) declines from 0.39 to 0.12, and the collision rate (CR) rises from 0.57 to 0.78, reflecting the increased complexity of navigating multiple dynamic obstacles while maintaining focus on the target human.
Despite these challenges, method consistently outperforms the baseline across all metrics and scenarios. In single-human settings, SDA outperforms the baseline by 0.15 in S and 0.10 in F. In three-human environments, SDA maintains a higher success rate (0.70 vs. 0.67) and greater frequency of reaching the target (0.12 vs. 0.07). These findings indicate that while task performance degrades with increased complexity, the SDA framework remains robust and adaptable, and the observed degradation is attributable to the inherent task difficulty rather than limitation of our method.
Table 9: Comparison of SDA performances on Habitat 3.0 with single and multiple human scenarios.
St
SPS f
F1
CR (
ES 1
SDA S2 (single-human)
0.91 + 0.01
0.45 + 0.01
0.39 4 0.01
0.57 = 0.02
0.43 4 0.02
Baseline (singlc-human)
0.76
0.34
0.29
0.01
0.48 = 0.03
0.40 + 0.02
SDA S2 (two-humans)
0.80 = 0.01
0.41 0.02
0.18
0.01
0.68 = 0.01
0.20 + 0.01
Baseline (two-humans)
0.76 + 0.01
0.36 0.02
0.09 0.01
0.80 = 0.01
0.11 0.01
SDA S2 (three-humans)
0.70 0.01
0.38 0.00
0.12
0.78 0.01
1.15:0001
Baseline (thrce-humans)
0.67 = 0.01
0,36 0.02
0.07 +
0.83 = 0.01
0.09 + 0.01
A.7 ERROR ANALYSIS
We include in Fig We analyze the distribution of failure causes in social navigation analyzed by watching 100 failed episodes and categorizing the failures into five types:
Constrained Movements (28%): The robot cannot avoid collision due to environmental constraints.
Blindspot (25%): The rohot cannot perceive the human due to blind corners or side collisions.
Not Found (22%): The robot does not detect the humanoid within 1500 simulation steps.
Moving Backwards (22%): The robot yield space backwards but fails avoid collisions. Walking into Humanoid (3%): Frontal collisions with the humanoid are the least common cause.
The failure analysis offers valuable insights into areas needing improvement. Walking directly into the humanoid is very unlikely and represent the worst scenario. As can be seen from Table 3 by including ORCA, we reduce the collision rate by 20 percentage points and this scenario will likely disappear. With ORCA, humans can either slow down or slightly change their direction, particularly for Constrained Movements and moving Backwards. Constrained Movements and Not Found could be improved by high-level planning capabilities that devise better exploration strategies to locate the person and let the robot back off until there enough space to let the human pass. The addition of microphone sensor could also help locate humans in the scenario in which someone could call their robot.
A.8 ADDITIONAL ANALYSIS
In the next section, conduct failure analysis of SDA examining the adaptability of privileged information and performing an ablation study on its design.
Fig. 6 left) illustrates the trend of the First encounter step over episode during Stage 2. The plot confirms that using trajectories facilitates the robot's finding the person, which it achieves after only approx. 450 steps, while the hGPS and hGPS+traj take more than 700 steps. Finding the robot first
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Failure Cases Analysis
Walling Humaniii
Moving Sectsants
Diisdapet
3.0%
22.0%
25.0%
Figure 5: Failure Cases Analysis on 100 episodes
results higher S and SPS metrics, but it may incur larger chances of collision (CR metric), due to having to then follow it for longer, cf. Sec.
Fig. 6 right) illustrates the average distance between the agent and the humanoid after the first encounter. While hGPS and hGPS+traj stay at approx. 4 meters, the proposed trajectory-based approach ranges between 1.5 and 1.9, thus well within the 1-2 meter range, which yields the success in the task of following (larger F metric, but encompassing larger risk of collision- C.R metrie).
First encounter step over tplsede
humeroodd dis. alter over
4.0
200-
3.-
hGPS 1PR.
1GP5 mml
Stage
2.5
2.-
430
3M
20
Training tep
Treining step
Figure 6: (left) Training step (x-axis) Vs the number of steps which it takes the robot to find the humanoid (y-axis), in the finding task; (right) Training step (x-axis) Vs the average distance which the robot manages to keep itself at from the humanoid (y-axis), in the task of following.
Design Analysis. RMA (Kumar et al., 2021) was used to adapt an agent from simulation to real- world deployment. In contrast, SDA encodes human behavior in trajectories, which can be inferred test time to develop socially aware navigation policy. While RMA encodes the environment configuration vector é; with information only time t, feed an entire trajectory encoding the position in the 20 timesteps up time t. As shown Table I0] encoding the human position only at time results in unsatisfactory performance, making the direct application of RMA relatively poor. In fact, it achieves just 3% of Episode Success (ES) against the 43% of SDA.
Given the sequential nature of the states that get encoded, also evaluated Transformers and MLPs and ultimately selected the MLPs for SDA. In Table II] we include this preliminary study. Note how the choice of the new sequence modeling mechanism not trivial and strongly affects performance.
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Published as a conference paper at ICLR 2025
Table 10: Ablation the length of trajectories consider during Stage
# States Considered
S1
SPS
Ff
CR 4
ES
0.55=0.01
0.14
0.03
0.75#0.01
0.03
0.62=0.01
0,25#0.02
0.05#0.01
0.06#0.01 0.64#0.01
20 (Proposed)
0.92
0.46-0..01
0.44
0.61 4:002
0.50
50
0.70=0.02
0.29#5002
0.08=0002
0.78=0.02 0.08
Table 11: Ablation the type of encoder to consider during Stage 1.
Encoder Type
S
SPS
Ff
CR
ES
MLP (Proposed)
0.92
0.46 0.46
EUAUT
0.44=0.02
0.61 20A0
Transformer
0.85
0.15
0.2740.01
0.76
0.12
A.9 QUALITATIVE RESULTS
The episode in Fig. (top) illustrates the agent's capability to track the human even when it is briefly observed. As the agent searches for the target in the bathroom, the humanoid swiftly passes in front of the door (Step 145) and disappears from the agent's ' view again (Step 150). Due to learned social dynamics, the agent exploits the humanoid's behavior (boltom), the agent has already located the human and its objective is to follow it. It is observed that Step 340, the human decides to move backward, prompting the agent move backward as well, creating the necessary space for passage by Step 360. Then, it continues its task of following.
begins to follow it. the episode in Fig.
Strp IGS
Stop TC
201
Figure 7: We showcase different episodes. On the top, the agent successfully follows the human after it swiftly moves front of the door. On the bottom, an episode where the human decides to move backward and the agent steps back to make way.
21


Reviews:
[{"review_id": "jC2mRiaWRn", "reviewer": "Reviewer_Cwf1", "summary": "This paper proposes a new method enabling a robot to search for a human in a simulated indoor environment and then follow them by maintaining a distance of 1 to 2 meters for at least k steps.\n\nThe key challenge is that the robot can only see the human via its first-person egocentric camera, resulting in partial knowledge about the environment, as well as the position and velocity of the human.\n\nTo address this problem, the authors propose to rely on privileged information, e.g., human GPS data and human trajectories, that are only available during training, not deployment. Then, the authors propose to train the robot policy in 2 phases. In phase 1, the robot does RL (DD-PPO) to jointly learn an encoder that maps privileged information to the latent state and a policy that depends on the robot camera image and the latent state. In phase 2, the robot will run the learned policy while doing supervised learning to learn a decoder to map the robot's camera image to the latent state (the data for the latent state comes from the ground truth privileged information, and the learned encoder from phase 1). Then, during online deployment, the robot will first convert the camera image to the latent state using the decoder from phase 2, and then run the robot policy learned from phase 1.\n\nThe result focuses on the Habitat 3.0 simulator. The proposed method is shown to outperform 3 baseline methods. An ablation study was conducted to evaluate the usefulness of each privileged information. Latent analysis shows that the latent state refers to the robot's subtask in find, seek, lost, and follow. The authors also add more realistic components into the simulator to simulate real-world scenarios, such as making the simulated human aware of the robot's presence with reciprocal collision avoidance (ORCA), lowered planning frequency, and increased noise. In such scenarios, the proposed method still outperforms the baseline methods.", "questions": "- Regarding technical novelty, the authors discuss the comparison between the proposed work and the previous work (Cancelli et al., 2023) in Sec.2. It seems that the techniques in both works are similar, but this paper proposes to use a similar structure for the following-human problem, rather than just avoiding collisions with humans as in Cancelli et al., 2023. It would be great if the author could clarify about the difference between these work, or about the extra challenges introduced by following a person rather than just collision avoidance (e.g., perhaps the challenge is more than just changing the reward function of the RL method?).", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The problem is well-motivated.\n- The writing is clear.\n- The method is sound.", "weaknesses": "- As far as I can tell, the training and testing assume the same simulated person. In the real world, this means that to follow one person, the robot has to interact with the user to collect offline data for Phase 1 and online data for Phase 2. This can be very cumbersome. It would be great if the authors could discuss some strategies to scale this method up for different people, such as using meta-learning approaches or using more diverse training data.\n\n- The result shown in Tab.1 shows only marginal improvement over the baseline \"Baseline+Proximity Cancelli et al. (2023)\". Could the authors provide an discussion about the practical implications of these improvements? Another way to demonstrate its significance is to use statistical test.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "afTmDPpQx3", "reviewer": "Reviewer_Naey", "summary": "This paper introduces the Social Dynamics Adaptation (SDA) model, a two-stage reinforcement learning approach for enhancing robot social navigation. SDA aims to improve a robot’s ability to navigate alongside humans in dynamic environments, where human trajectories serve as social cues that guide robot movement. In the first stage, the model learns a policy using privileged information about human trajectories, encoding social dynamics into a latent representation. In the second stage, this policy operates without access to these trajectories, relying on inferred social dynamics from the robot’s own state and action history.\nExperiments conducted on the Habitat 3.0 platform demonstrate that SDA significantly outperforms state-of-the-art baselines in tracking, following, and maintaining appropriate distances from humans. The study includes analyses of SDA’s effectiveness under noisy conditions, different update rates, and with realistic humanoid behaviors, showcasing its potential for adapting to real-world conditions.", "questions": "Are there any plans to test SDA in real-world environments to evaluate how well it transfers from simulation, particularly in handling variable lighting, unexpected obstacles, and diverse human behaviors?\nHow does SDA perform when humans change speed or direction quickly? Does the reliance on past state-action history allow the model to adapt in real-time, or are there latency issues?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "Firstly, the SDA model’s two-stage training framework effectively combines privileged information during training with adaptable inferences during deployment, allowing the model to handle real-world scenarios without needing precise human trajectory data. It outperforms several baselines, showing improved metrics for finding, following, and episode success, which is critical for effective social navigation. Testing on Habitat 3.0, with realistic human motion and environmental noise, strengthens the claim that SDA is robust and adaptable to real-world applications. Comprehensive evaluations, including noise analysis, reduced update frequencies, and alternative metrics, provide a deep understanding of the model’s strengths and weaknesses under various conditions.", "weaknesses": "Despite testing under realistic noise and movement in simulation, SDA’s real-world performance remains untested, raising questions about its transferability from simulated to physical environments. Besides, the model assumes a certain pattern of human movement and interaction, which may limit its effectiveness in scenarios where human behaviors deviate significantly from training data.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bbCgDWQt0f", "reviewer": "Reviewer_nz5S", "summary": "The paper introduces the Social Dynamics Adaptation model (SDA) to enhance social navigation, where robots must adapt in real-time to human movements. It proposes a two-stage reinforcement learning framework to infer social dynamics from the robot’s state-action history. In the first stage, the model trains using privileged information, like human trajectories, to learn an optimal motion policy. In the second stage, it operates without direct access to human trajectories, instead inferring social dynamics from past actions and statuses. \nThe approach was tested on Habitat 3.0 and achieved state-of-the-art results in finding and following humans. The primary contributions include the SDA framework and extensive testing under realistic scenarios, which bridge the gap between privileged training data and real-world conditions.", "questions": "1. Could you clarify why the problem being solved in this work is hard and important in the introduction? Providing motivating examples early on would help the reader grasp the significance before diving into the methodology, related work or implementation details. Could you revise the introduction to include this context more explicitly?\n2. While the paper models sensor noise and signal drops, this may not be enough to claim robust generalization to real-world environments. Could you explain how the model would handle the complexities of real-world social interactions, and consider testing beyond simulated noise scenarios?\n3. The paper does not explore how the model adapts to varying human behavior, such as erratic movements. Have you considered testing with more diverse or unpredictable human behaviors? How do you think the model would perform under such circumstances?\n4. Given the focus on simulation, could you discuss how the model could be deployed in real-world environments? What are the anticipated challenges of transferring the SDA model to physical robots, and have you considered Sim2Real experiments or alternative testing methods?\n5. Could you provide more details on the computational overhead of the two-stage model, particularly during deployment? How do you anticipate the system performing on resource-constrained robots in real-time scenarios, when only stage 2 is run?\n6. The current focus is on following a single human in relatively simple environments. Have you considered expanding the model to more complex social settings, such as multi-human interactions?\n7. In Figure 4, the text suggests that the behavior clusters are well-separated, but visually, they appear to be overlapping. Could you clarify this discrepancy and perhaps provide a better explanation or adjust the visualization to reflect the claim more accurately?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The SDA model cleverly utilizes privileged information (e.g., human trajectories) during training to learn a social navigation policy and then shifts to infer similar dynamics from historical state-action data during deployment. \n2. By modeling human trajectories as latent dynamics, the paper addresses the challenge of partial observability, which is common in robot-centric navigation tasks. The framework effectively models and compensates for missing information, such as losing line of sight of humans, thus proposing to improve the robot's adaptive navigation behavior in dynamic environments.\n3. The SDA model is designed to go beyond just avoiding collisions by recognizing and encoding social dynamics, such as keeping an appropriate distance from humans and yielding space when necessary.", "weaknesses": "1. The introduction dives too quickly into explaining the simulator, methodology, and related work without clearly stating the problem or why it is hard and important. It lacks motivating examples to contextualize the problem, making it difficult for readers to grasp the significance until section 3. \n2. The paper models sensor noise and signal drops to simulate real-world conditions, but this is not sufficient to claim robust generalization to real-world scenarios. Real-world environments involve complex interactions, unpredictability, and physical constraints that are difficult to replicate in simulation, even with added noise.\n3. Although the authors test their model extensively in Habitat 3.0, the paper lacks real-world experiments. Simulation platforms, while powerful, cannot fully capture the complexity and unpredictability of real-world environments. Additionally, real-world computational constraints (e.g., processing speed and memory) were not sufficiently addressed.\n4. The paper does not sufficiently explore how variability in human behavior, such as different walking speeds, erratic movements, or group interactions, affects the model’s performance.\n5. The SDA model focuses primarily on following a single human in simplified indoor environments, without considering more complex scenarios like navigating among multiple humans or handling other social situations.\n6. Figure 4 claims that the behavior clusters are well-separated, but they appear mostly overlapping. \n7. Figure 2 should explicitly define that stage 1 is offline and stage 2 is online. \n8. The paper’s overall presentation could use some work. Many sections of the paper have different writing styles, making the writing really inconsistent, detracting from the message of the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Spotlight)