Group-based Motion Prediction for Navigation in Crowded Environments
Authors: Allan Wang
Christoforos Mavrogiannis
Aaron Steinfeld
Venue: CoRL2021 Oral (2021)
Keywords: Social Navigation
Group-based Navigation
Applications of Robot Learning in Navigation

Abstract: We focus on the problem of planning the motion of a robot in a dynamic multiagent environment such as a pedestrian scene. Enabling the robot to navigate safely and in a socially compliant fashion in such scenes requires a representation that accounts for the unfolding multiagent dynamics. Existing approaches to this problem tend to employ microscopic models of motion prediction that reason about the individual behavior of other agents. While such models may achieve high tracking accuracy in trajectory prediction benchmarks, they often lack an understanding of the group structures unfolding in crowded scenes. Inspired by the Gestalt theory from psychology, we build a Model Predictive Control framework (G-MPC) that leverages group-based prediction for robot motion planning. We conduct an extensive simulation study involving a series of challenging navigation tasks in scenes extracted from two real-world pedestrian datasets. We illustrate that G-MPC enables a robot to achieve statistically significantly higher safety and lower number of group intrusions than a series of baselines featuring individual pedestrian motion prediction models. Finally, we show that G-MPC can handle noisy lidar-scan estimates without significant performance losses.

Group-based Motion Prediction for Navigation in Crowded Environments
Allan Wang
Christoforos Mavrogiannis
Robotics Institute
Paul G. Allen School of Computer Science Engineering University of Washington
Carnegie Mellon University
Aaron Steinfeld Robotics Institute
Carnegie Mellon University
Abstract: We focus on the problem of planning the motion of a robot in a dy- namic multiagent environment such as a pedestrian scene. Enabling the robot navigate safely and in socially compliant fashion in such scenes requires a representation that accounts for the unfolding multiagent dynamics. Existing ap- proaches this problem tend to employ microscopic models of motion prediction that reason about the individual behavior of other agents. While such models may achieve high tracking accuracy trajectory prediction benchmarks, they often lack an understanding of the group structures unfolding in crowded scenes. In- spired by the Gestalt theory from psychology, we build a Model Predictive Control framework (G-MPC) that leverages group-based prediction for robot motion plan- ning. We conduct an extensive simulation study involving series of challenging navigation tasks in scenes extracted from two real-world pedestrian datasets. We illustrate that G-MPC enables robot to achieve statistically significantly higher safety and lower number of group intrusions than series of baselines featuring individual pedestrian motion prediction models. Finally, we show that G-MPC can handle noisy lidar-scan estimates without significant performance losses.
Introduction
Over the past three decades, there has been vivid interest in the area of robot navigation in pedes- trian environments [1, 2, 3, 4, 5]. Plamning robot motion in such environments can be challenging due to the lack of rules regulating traffic, the close proximity of agents and the complex emerging multiagent interactions. Further, accounting for human safety and comfort as well as robot efficiency add to the complexity of the problem.
To address such specifications, a common [3. 4, 6, 7, 8] paradigm involves the integration behavior prediction model into planning mechanism. Recent models tend to predict the individual interactions among agents to enable the robot to determine collision-free candidate paths [3, 4, 9]. While this paradigm is well-motivated, it tends to ignore the structure of interaction in such environments. Often, the motion of pedestrians coupled as a result of social grouping. Further, the motion of multiple agents can often be effectively grouped as result of similarity in motion characteristics. Lacking a mechanism for understanding the emergence of this structure, the robot motion generation mechanism may yield unsafe uncomfortable paths for human bystanders, often violating the space of social groups
Motivated by such observations, we draw inspiration from human navigation to propose the use of group-based prediction for planning in crowd navigation domains. We argue that humans not employ detailed individual trajectory prediction mechanisms. In fact, our motion prediction capa- bilities are short-term and do not scale with the number of agents. We do however employ effective grouping techniques that enable us to discover safe and efficient paths among motions of crowd networks. This anecdotal observation is aligned with gestalt theory from psychology [10] which suggests that organisms tend to perceive and process formations of entities, rather than individual
5th Conference on Robot Learning (CoRI. 2021), London, UK.

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8
Based on representation of social grouping [13], we build group behavior prediction model to empower a robot to perform safe and socially compliant navigation in crowded spaces. (a) Example of our representation overlayed top of a scene from real-world dataset [14]. (b) A model predictive controller equipped with our prediction model is able to navigate around the group socially (right) opposed the baseline that cuts through the group (left).
Figure 1:
components. Such techniques have recently led to advances in computer vision [11] and computa- tional photography [12]. Similarly, envision that a robot could reason the formation of effective groups in a crowded environment and react to their motion as an effective way to navigate safely.
In this paper, propose group-based representation coupled with prediction model based on the group-space approximation model of Wang and Steinfeld [13]. This model groups crowd into sets of agents with similar motion characteristics and draws geometric enclosures around them, given observation of their states. The prediction module then predicts future states of these enclosures. We conduct an extensive empirical evaluation over 5 different human datasets [14, 15], each with flow following and a crossing scenario. We further conduct a same set of evaluations with agents powered by ORCA [16] that share the start and end locations in the datasets. Last but not least, we conducted evaluation given inputs in the form of simulated laser scans, from which pedestrians only partially observable even completely occluded. We compare the performance of our group-based formulation against three individual reasoning baselines: a) a reactive baseline with no prediction; b) constant velocity prediction baseline; one based on individual S-GAN trajectory predictions [17]. We present statistically significant evidence suggesting that agents powered by our formulation produce safer and more socially compliant behavior and potentially able handle imperfect state estimates.
2 Related Work
Over the recent years, a considerable amount of research has been placed to the problem of robot
navigation in crowded pedestrian environments [3, 4, 8, 18, 19, 20, 21, 22, 23). Such environments often comprise groups of pedestrians, navigating as coherent entities. Sochman and Hogg [24] suggests that 50-70% of pedestrians walk in groups. Many works exist in group detection. One popular area in such domain is static group detection, often leveraging F-formation theories [25]. However, dynamic groups often dominate pedestrian-riet environments and exhibit different spatial behavior [26]. Among dynamic group detection, one common approach is to treat grouping probabilistic process where groups are a reflection of close probabilistic association of pedestrian trajectories [27, 28, 29, 30, 31]. Others use graph models to build inter- inter-peeettrian relationships with strong graphical connections indicating groups [32, 33]. The social force model [34] also inspires Mazzon et [35], Sochman and Hogg [24] to develop features that indicate groups. Clustering is another common group of technique group pedestrians with similar features into groups [36, 37, 38, 39]. For our formulation, proposed by Chatterjee and Steinfeld [39]. Other grouping methods will simply result in different
sufficient to employ a simple clustering- grouping method group membership assignments.
Applications on groups often focus on specific behavior aspect. For example, one focus in this area how robot should behave as part of the group formation [40]. On dyad groups involving single human and a robot, some researchers examined socially appropriate following behavior [41, 42, 43, 44] and guiding behavior [45, 46, 47]. In works that do not include robots as part of pedestrian groups, some research teams studied how robot should guide group of pedestrians [48, 49, 50]. From navigation perspective, Yang and Peters [26] leverage groups as obstacles, but their group space only involves occasional O-space modeling from F-formation theories. Without the engineered occurrence of O-space, their representation reduces to one of our baselines. Katyal et al. [51] introduce an additional cost term that leverages robot's distance to the closest group in

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reinforcement learning framework. They model groups using convex hulls directly generated from pedestrian coordinates instead of taking personal spaces into consideration. In our work, we additionally explore the capabilities of groups in handling imperfect sensor inputs. While our focus on analysing the benefits of groups, our group based formulation can easily incorporated into the work of Katyal et al. [51]'s framework.
3 Problem Statement
Consider a robot navigating in workspace W C RR2 amongst n other dynamic agents. Denote by E W the state of the robot and by s' E W the state of agent E N = [I.....n.) The robot is navigating from state so towards destination ST by executing a policy я : W x l + that maps the assumed fully observable world state S = Us=1:1 s to control action E U. drawn from a space of controls 24 _ R2. We assume that the robot is not aware of agents' destinations s or policies m. W x li + Ut, i E W. In this paper, our goal to design a policy æ that enables the robot to navigate from 80 to safely in socially compliant fashion.
4 Group-based Prediction
We introduce group representation building on prior work [13] and a model for group-based pre- diction that is amenable for use in decentralized multiagent navigation.
4.1 Group Representation
Define 8² [0. 2T) the orientation of agent .V' which assumed to be aligned with the direction of its velocity u', extracted via finite differencing of its position over a timestep dt and denote by o² = ||='|| IR+ its speed. We define an augmented state for agent as q² (s', 8 u²)..
We treat a social group as a set of agents who are in close proximity and share similar motion characteristics. Assume that
of J groups, g' E J a label indicating the group membership agent i. We then define group j E J as a set (G' jiE
{1,-- {1,...,,J) navigate in scene. Define by
GO {i N I g" = j} and collect the of all groups in a scene into a
Extracting Group Membership. We define the combined augmented state of all agents as = U==1mnq'. To obtain group memberships for set of agents N, we apply the Bensity-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) [52] on agent states:
G レ
DBSCAN(q cg. Eg, Ev)
Where Eas Eg, E are respectively threshold values on agent distances, orientation and speeds.
Extracting the Social Group Space. For each group G j E J, we define a social group space as geometric enclosure G around agents of the group. For each agent E GO, we define a personal space P as a two -io-dimensional asymmetric Gaussian based on the model introduced by Kirby [53].
Refer Appendix for detailed descriptions.
Given the personal spaces Pi, i E G., of all agents in a group j, we extract the social group space of the whole group as convex hull:
g (on l )
The shape described by G represents obstacle space representation of a group containing agents in close proximity with similar motion characteristics. For convenience, let us collect the spaces of all groups in scene into a set G (Ga E J}.
4.2 Group Space Prediction Oracle
Based on the group-space representation of Sec. 4.1, we describe prediction oracle that outputs an estimate of the future spaces occupied by set of groups Geay up to a time ły = + f. where is a future horizon given a past sequence of group spaces Geget from time ta = t where h is a window of past observations:
Gu., O(9 Uf=1. O,
(3)
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Table 1: Prediction Model Performance
Metric
ETH
HOTEI.
ZARAI
ZARA2
UNIV
mloU (%)
83.52
90.37
88.04
89.30
85,32
Baseline
floU (%)
76.32
85,38
82.14
83.88
77.24
Encoder-decoder Network
mloU
86.66
92.10
89.97
90.94
87.52
floU (%)
78.64
86.83
83.77
85.09
78.55
where O2 is model generating group space prediction for group GO, Refer Appendix B for detailed description of partial input handling.
We implement the oracle Oj of eq. (3) using simple encoder-decoder network. The encoder follows the 3D convolutional architecture [54] whereas the decoder mirrors the model layout of the encoder. The mncodrr-decode network takes input sequence' G1, and outputs a sequence G1+1:1, which we pass through sigmoid layer. We supervise the encoder-decoder network's 's output using the binary cross entropy loss.
We verified the effectiveness of our encoder-decoder network on the scenes of our experiments by conducting a cross- vross-validation comparison against baseline. The baseline predicts the future shapes by linearly translating the last social group shape using geometric center velocity. We use Intersection over Union (IoU) as our metric. Between the ground truths and the predictions, this metric divides the number of overlapped pixels by the number of pixels occupied by either one of them. As shown in Table I, our encoder-decoder network outperforms the baseline.
5 Model Predictive Control with Group-based Prediction
We describe G-MPC, model predictive control (MPC) framework for navigation in multiagent environments that leverages the group-based prediction oracle of Sec. 4.
We describe our group-prees prediction informed MPC, or G-MPC. At planning time given (possibly partial) augmented world state history Q Qtg:- we first extract a sequence of group spaces G based
on the method of Sec. 4.1. Given these, the robot computes an optimal control trajectory u² = 4 of length K by solving the following optimization problem:
(s u²) arg min MI.K
J(s++1, 1154+1.k41155 9k+1,
(4)
8.1. 92 h:l Ginat
(5)
81 t St
(6)
9 gk+1sky 0gg
Uy E U
(8)
Sk+1 = Sy uk dt.
(9)
where y is the discount factor and J represents cost function, eq. (5) initializes the group space history (k = - is the timestep displaced a horizon h in the past from the first MPC-internal timestep k 1), eq. (6) initializes the robot state to the current robot state 8t, eq. (7) is update rule recursively generating predicted future group sequence up to timestep ky = k + f given history from time kn k up to time k, O represents a group-space prediction oracle based on Sec. 4, and eq. (9) is the robot state transition assuming a fixed time parametrization of step size dt.
We employ weighted sum of costs Jy and Ja, penalizing respectively distance to the robot's goal and proximity to groups:
(skk 9 ST) (sk ST) + (ske GR), where À is weight representing the balance between the two costs and
(10)
00 [| -1l1--
if G
Jy Jy(sk)
(11)
else,
'The oracle input sequence is first converted into image-space coordinates using the homography matrix of the scene. We also preprocess inputs to have normalized scale and group positions. The autoencoder output is converted back into Cartesian coordinates using the inverse homography transform.

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Table 2: Number of trials per task and scene.
Task
ETH
HOTEL
ZARAI
ZARA2
UNIV
Flow
58
43
25
127
106
Cross
58
44
28
129
114
penalizes a rollout according to the distance of the last collision-free waypoint to the robot's goal. Further, we define Ja as:
Ja(sk- 9k) exp(-D (SK++I
(12)
where
minjez D (s* 9%) minjer D 8k - 0% else,
if $ o
D(ss, 9k)
(13)
where D(sk- (i)) returns the minimum distance between the robot state and the space occupied by group j at time Using D, function D computes the minimum distance to any group for a given time. In most cases, the robot lies outside of groups, i.e., Sk & 9% -therefore, the cost Ja tries to maximize the distance D. Sometimes, the robot might end up entering the group space G -i those cases, Ja tries to minimize DD to steer the robot towards the direction of quickest escape from the group. In case that the robot is inside group begin with, we shrink the group sizes in Sec. 4.1 until the robot is outside the groups again.
To solve eq. (4), search over finite set of control trajectories of horizon K. With the assump- tion that the robot is holonomic and is not under any kinematic constraints, we use a set of control rollouts Uz= = {u' .... u u with three levels of tangential speeds and a set of turning speed, i.c.,
2Tr R
2 Umnns 3 Uuaaz Umar } w E (0. (
" i (o cos v sin vb, w), v
U E
(14)
To ensure compatibility between our group-based prediction model and our MPC formulation, we set the control rollout time horizon be the prediction model's prediction horizon, K f.
6 Evaluation
We evaluate our framework through a simulation study in which the robot performs a navigation task (a transition between two points) within crowds of dynamic agents in set of scenes.
6.1 Experimental Setup
We consider a set of realistic pedestrian scenes, drawn from the [14] (ETH and HOTEL scenes) and UCY [15] (ZARA1, ZARA2 and UNIVERSITY scenes) datasets, which often serve as bench- marking testbeds the motion prediction and social navigation literature [17, 55, 56, 57]. In each scene, we define two navigation tasks (see Fig. 2): Flow: in which the robot navigates along the crowd flow and Cross in which the robot intersects vertically with the traffic flow. For each task, we generate set of trials by segmenting the scene recording into blocks involving challenging interac- tions. We define a challenging interaction to be a segment involving at least 5 pedestrians inside the test region drawn in black in Fig. 2. This process provided us with distribution of trials as shown in table Table 2. Across all trials, we keep the robot's 's maximum speed at 1.75m/s.
We consider two experimental conditions: an Offline and an Online one. In the Offline one, the robot navigates among a crowd moving according to a recording of a human crowd. Under this condition, pedestrians act as dynamic obstacles that do not react to the robot, situation which could arise in cases where robots are of shorter size and could thus be easily missed by navigating pedestrians. In the Online one, the robot navigates among crowd? moving by running ORCA [16], a policy that is frequently used as a simulation engine for benchmarking in the social navigation literature [8, 57, 58].
To investigate the value of G-MPC, we develop three variants of it. group-pred is a G-MPC in which the encoder-decoder network has history h = 8 and horizon f 8. group-nopred is
è For consistency, the agents in the crowd start and end at the same spots as the agents in the recorded crowd from the Offline condition.
5

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ETH HOTEL ZARA1
ZARA2
UNIV
10
10
5
0
01
10
0
5
10
15
15
Figure 2: Trajectories of all pedestrians in the datasets. The red dots represent the task start and end locations. The red lines represent the task paths. The black box represents the test region to check for non-trivial tasks.
a variant that features no prediction at all itt just reacts to observed groups at every timesteps and it is equivalent to the framework of Yang and Peters [26]. Finally, paser-group-pree is identical to group-pred but instead of using ground-truth pose information, it takes input noisy lidar scan readings. We simulate this by modeling pedestrians as Im-diameter circles and lidar seans as rays projecting from the robot. We refer the spec sheet of SICK LMS511 2D lidar for simulation parameters. We further inject noise into the readings according to the spec sheet. Under this simu- lation, pedestrians may only be partially observable or even completely occluded from the robot.
We compare the performance of these policies against set of MPC variants using mechanisms for individual motion prediction. ped-nopred is vanilla MPC that reacts to the current states of other agents without making predictions about their future states. ped-linear is a vanilla MPC that estimates future states of agents by propagating agents' current velocities forward. This baseline motivated by recent work showing that constant-velocity models yield competitive performance in pedestrian motion prediction tasks [59]. Finally, ped-sgan ís an MPC that uses S-GAN [17] to extract a sequence of future state predictions for agents based on inputs of their past states. We selected S-GAN because it is recent highly performing model. To ensure fair comparison, all the MPC policy variants are integrated with the same MPC controller evaluated at dt = 0.1.
We measure the performance of the policies with respect to four different metrics: a) Success rate, defined as the ratio of successful trials over total number of trials; b) Comfort, defined as the ratio of trials in which the robot does not enter any social group space over the total number of trials; c) Minimum distance to pedestrians, defined as the smallest distance between the robot and any agent per trial; d) Path length, defined as the total distance traversed by the robot in trial.
To track the performance of G-MPC, we design hypotheses targeting aspects of safety and group space violation which we investigate under both experimental conditions, i.e., offline and online:
HI: To explore the benefits of group based representations alone, hypothesize that group-nopred is safer than ped-nopred while achieving similar success rates but worse efficiency.
H2: To explore the full benefit of group based formulation, we hypothesize that group-pred is safer than ped-linear and ped-sgan while achieving similar success rates but worse efficiency.
H3: To explore how our formulation handles imperfect inputs, we hypothesize that laser-group- pred achieves similar safety to group-pred while achieving similar success rate and efficiency.
H4: To check that our formulation is socially compliant, we hypothesize that group- group- pred and laser-group- prre violate agents' group space less often than the baselines.
6.2 Results
Quantitative Analysis. Fig. and Fig. 4 contain bar charts representing the performance of G-MPC compared with its baselines under Offline and Online settings respectively. Bars indicate means, emrorbars indicate standard deviations, "F" and "C" are flow and cross scenarios respectively, and the number of asterisks indicates increasing significance levels: 0.05, 0.01, 0.001 according to two-sided Mann- Whinney U-tests.
H1: We can see from both Fig. 3 and Fig. 4 that G-MPC achieves statistically significantly larger minimum distances to pedestrians across all scenarios, often with p < 0.001. This illustrates that the group representation is in itself capable of upgrading a simple MPC with no prediction. As expected, we observe that the price G-MPC pays for that a larger average path length. We also see that success rates are comparable. Overall, we conclude that H1 holds.

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ped-nn
ped-linear
pedisgan
group-nopred
croup-pred
laser-group pred
Success rates (%)
100
50
Percentage trials without group intrusions (%)
100
50
Minimum distance pedestrians (m)
3
2
Path length (m)
40
30
Figure 3: Performance per scene under the Offline condition.
Horizontal lines indicate statistically
significant results corresponding to different to hypotheses.
ped nopred
pee-llnear
sgan Success rates (%)
napred
group pred
group
50
trials without group Intrusions
Minimum distance pedestrians (m)
lorgth (m)
10
ETH-F
Hotel-F
ZARAI-F
ZARAZ-F
UNIV-F
ETH-C
HOTEL-C
ZARAI-C
ZARA2-C
UNIV-C
Figure 4: Performance per scene under the Online condition (simulated pedestrians powered by ORCA [16]). Horizontal lines indicate statistically significant results corresponding to different to hypotheses.
t=0s
t=
t=2s
t=3s
t=
t=3s
Figure 5: Qualitative performance difference between approaches leveraging pedestrian-based (top) and grsep (bottom) representations. Left: non-reactive agents. Right: reactive agents. In all cases, the robot starts from the top and attempts to navigate downward. The green line shows the robot's traversed trajectory.
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When future state predictions are considered, G-MPC obtains statistically sigmificant results in most scenes supporting its attributes of being safer the cost of worse efficiency. Thus H2 is par- tially confirmed. In offline scenarios, G-MPC has lower success rates in crossing scenarios. Upon closer inspection, most failure cases are due to timeouts from G-MPC's conservative behavior. How- ever, in online scenarios where pedestrians react to the robot, G-MPC achieves high success rates. In real-world situations, to cross dense traffic, the robot needs to plan its actions with expectations of reactive pedestrians, Otherwise, the robot will most probably run into the freezing robot problem [4].
H2:
H3: Group- based representations have the potential to robustly account for imperfect state- estimates. Overall, we observe that with simulated imperfect states, G-MPC does not perform statistically significantly worse in terms of safety, but in dense crowds of the UNIV scenes it has worse efficiency and worse success rates in online cases. This shows that H3 holds in terms of safety and, in moderately dense human crowds, holds in terms of efficiency. Future work on bet- ter group representation is needed to achieve better efficiency in high-density human crowds given imperfect states.
H4: From Fig. 3 and Fig. 4, we can see that G-MPC often has fewer group-space intrusions than its baselines. While this relationship is not always statistically significant, we do see a general trend of the group-based approaches to respect group spaces more often than individual ones. Thus, we conclude that H4 is partially confirmed.
We additionally observe a general trend that group-pred is better than group-nopred in terms of higher success rates, lower chances of group intrusions, longer minimum distances pedestrians and shorter path lengths. This shows that our group prediction model offers benefits to the robot's navigation. However, in a few scenarios group-nopred performs better. We largely attribute this to the finite inaccuracies of future group predictions and the freezing robot problem that accompanies the robot's more conservative behavior group-pred than in group-nopred.
Qualitative Analysis. Qualitatively, it is a more common occurrence for regular MPC to perform aggressive and socially inappropriate maneuvers than G-MPC. As shown in the two examples in Fig. 5 executed by ped-sgan and group-pred agents, we can see that in offline conditions, the MPC agent aggressively cuts in front of the two pedestrians the cluster of pedestrians, only managing to avoid the deadlock by escaping through the narrow gap that opens up. While for G-MPC, it tracks the movements of the two pedestrian groups coming from the left. When the two pedestrian groups merge, the agent turns around and reevaluates its approach to cross. In the online condition, we observe that the MPC agent cuts through a pedestrian group to reach the other side, forcing a member of the group to stop and yield as indicated by the pedestrian's 's shrinking personal space, which is proportional to its speed. In the same situation, the G-MPC agent chooses to circumvent behind the social group.
the left before proceeding headlong into
7 Conclusion
We introduced methodology for generating group-based representations and predicting their future states. Through an extensive evaluation over the flow and crossing scenarios drawn from 10 different real-world scenes from 2 different human datasets with both reactive and non-reactive agents, demonstrate the value of group- prediction in enabling safe and socially compliant navigation. Through experimentation with simulated laser scans, model displays promising potential to process noisy sensor inputs without much performance downgrade.
Several improvements to framework are possible. For example, we could incorporate state- of-the-art oracles in the form of advanced video prediction models [60] or incorporate inter-group interaction modeling. Additional considerations such as the set of rollouts the cost functions could possibly increase performance. We could also integrate our prediction model into alternative control frameworks such reinforcement learning policies.
Finally, we plan on validating our findings on real-world robot to fully test the capability of G- MPC to handle noisy sensor inputs. We also plan to investigate ways to improve computation time to enhance our approach's 's real-world applicability. These include simplifying group representation geometry and predicting future group states in metric space instead of in image space.
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Acknowledgment
This work was funded by grant (II--1744311 from the National Science Foundation and Honda Research Institute USA.
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12


Reviews:
[{"review_id": "jU0QZ1oOzx4", "reviewer": "Reviewer_qTAW", "summary": "The authors seek to demonstrate the benefits of modeling group structures in social navigation settings.\n\nTheir results clearly demonstrate the benefits of modeling groups over individuals for social navigation problems. They also show that their method is also robust to sensor noise.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "The paper is clear and well written. It is easy to understand the contributions of the authors, and they clearly demonstrate their contributions. \n\nI appreciated the way the authors clearly laid out their hypothesis (H1-H4) in the evaluation section and then clearly laid out their analysis with respect to those same hypothesis(H1-H4). That organization style made it very easy to understand the questions they were asking, and immediately put the results into context.\n\nAnother hypothesis to explore that would be of interest would be comparing group-nopred and group-auto. This would directly highlight the impact of modeling future group motion using the auto encoder. I would hypothesize that group-auto would be “better across the board”: e.g. higher success rate, maintain a higher min-dist-to-pedestrian, have fewer trials with group intrusion, shorter path length. The charts seem to mostly suggest that this is the case, but there are some cases where group-nopred is “better” (e.g. Online, UNIV-F, Success rate). It would be nice to get an in depth explanation of some of these cases and what is going on there.\n\nIt would have been nice to see a sim2real deployment. It would have been nice to see how this system behaves in a couple real human scenarios where the environment is much less controlled than this simulation. It would also be nice to understand what are the main limitations of transferring this system to a real robot (i.e. compute bottlenecks, sensor failure modes, etc.)", "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": "I think the contribution in this paper is clearly demonstrated and with a with a bit more analysis (see above), it could be strengthened.", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Excellent: Enjoyable to read.", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "issues": "See above\n1. Experiments on a real robot\n2. More analysis of group-nopred vs group-auto", "reviewer_expertise": "Very good: Comprehensive knowledge of the area"}, "extra_text": {}}, {"review_id": "PVZVHzmLcR", "reviewer": "Reviewer_UWWw", "summary": "The authors approach the problem of planning a robot trajectory through a dense pedestrian crowd. They cluster pedestrians into groups, and consider the combined personal space of such groups. Using past group-space image masks, future group-space masks are predicted by a 3D Convolutional Neural Network (CNN). A receding-horizon Model Predictive Control (MPC) approach is used to navigate the robot through the crowd.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n\n- Considering pedestrian crowds as larger groups than individuals is good insight, this research should encourage others in the field to consider \"macroscopic\" models.\n- The evaluation of method work is significant, testing on a variety of public datasets. They also propose a method of testing via an interactive \"Online\" approach using ORCA; such a testing environment is very important when modelling interactive environments. In 4 cases, they prove their results are statistically significant.\n\nWeaknesses\n\n- From eq. (3), the learned model operates on each group individually. As such, there is no way for the model to capture inter-group interactions. This addition could be an interesting extension to this work.\n\n", "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": "The authors take an unconventional \"macroscopic\" approach to crowd prediction, and prove it is superior to state-of-the \"microscopic\" methods for online navigation. The authors clearly explain their approach, and back it up with an extensive evaluation section.\n", "technical_quality": "Excellent: Very convincing and thorough.", "clarity_of_presentation": "Very Good:  Well organized and clear.", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "4: The work contains interesting new ideas that will potentially have major impact in robotics or machine learning.", "reviewer_expertise": "Excellent: Expert knowledge on the topic of the paper"}, "extra_text": {"issues": "- The group space prediction oracle is repeatedly referred to as an \"autoencoder\". As the model predicts the *future* from the *past*, the input is not the output: therefore the network is not strictly an *auto-* encoder. Consider changing the language, for example to \"encoder-decoder framework\".\n- Footnote 1 mentions a homographic transform between \"cartesian\" coordinates and \"image-space\" coordinates. The purpose and details of this transform could be elaborated in the supplementary details.\n- (optional, stylistic) There are no less than 5 variables associated with the letter 'g' in various fonts, 4 of them capital 'G'. It might aid understanding if the distinction between group *membership* and group *space* was clearer.\n- (note) This reviewer knows of only one other work with this macroscopic space-time-image representation of pedestrian crowds that the authors should consider as part of the related work: “Dynamic Crowd Prediction for Social Navigation”, S. H. Kiss, K. Katuwandeniya, A. Alempijevic, and T. Vidal-Calleja, published to ICRA 2021."}}, {"review_id": "94tWZ_wuhUq", "reviewer": "Reviewer_6KBt", "summary": "This paper tackles the problem of socially compliant navigation. Specifically, it clusters the pedestrians into groups, finds the convex hull of each group, and then included in the cost of an MPC controller for the robot. The method is validated to achieve better safety and somewhat better \"comfort\" on simulated scenarios, with injected noise.  ", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Overall, the propoed method is intuitive and sound. The method and its motivation is fairly intuitive: we want the robot to take into account the group formation it is in, for more socially compliant navigation. \n\nA main weakness is the simplicity of the robot model that is compatible. Lines 165-169 indicate that the method requires a fairly smalll number of discrete actions to be made. This suggests that in practice the proposed method is too slow, i.e. we cannot solve for continuous steering angle/ velocity. This begs the question, is it worthwhile to consider group dynamics? Would it be better to instead have a MPC controller running at high frequencies and adapt to simpler predictive models. It would be good if this could be investigated in the experiments -- it is unclear whether the MPC controllers used as comparison are also restricted to discrete actions, and are running at the same frequency.\n\nAdditionally, there are other methods that merit mentioning. Particularly, POMDP-based solutions DESPOT and its variants (HyP-DESPOT) (Adhiraj Somani et al., 2013, neurips; Panpan Cai et al., 2021 IJRR); and other control-based social navigation approaches (Weiming Zhi et al., 2021, ICRA).\n\nDespite the concern about the simplicity of the robot model, the proposed method is fairly novel and solid. The paper is also accompanied by sufficient empirical evaluation. ", "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": "See above", "technical_quality": "Very Good:  Convincing.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "reviewer_expertise": "Very good: Comprehensive knowledge of the area"}, "extra_text": {"issues": "Need clarification on the simplicity of the robot dynamics. Discussion or ideally empirical evaluations on how much considering social groups add, instead of just having a controller that can be evaluated fast."}}, {"review_id": "1S8mDdfWv9x", "reviewer": "Reviewer_rGtm", "summary": "This paper investigates motion planning among pedestrians, particularly focusing on the fact that pedestrians can travel in groups that have different dynamics/interactions than individual pedestrians. The technical contributions include a method for grouping/representing groups of pedestrians, an autoencoder to learn the group dynamics from a dataset, and an MPC formulation to plan collision-free paths with respect to the groups. The results include comparisons of various algorithms on fixed datasets and with simulations where agents follow ORCA policies.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths:\n- Groups are an important component of social robot navigation but most of the recent works on this topic focus on modeling individual pedestrians\n- The paper is well organized, enjoyable to read, and the problem is well-motivated and explained.\n- The approach of learning a group dynamics model and folding that into an MPC is interesting and makes sense, as MPC comes with collision guarantees, and it would be difficult to specify a group dynamics model without learning from data\n\nWeaknesses:\n- For groups, hardware experiments seem even more important than other social navigation problems, since the group dynamics are difficult to model and thus simulate. However, the authors designed insightful experiments in simulation and the challenges of running physical experiments (particularly with pedestrian groups) are totally understood here. Are there other ways to simulate group behaviors, other than ORCA (which is inherently single-agent) and playing back pre-recorded motions?\n- The figures could be improved. I don't really understand Fig 5. Figs 3 and 4 are hard to see, especially since the tiny differences between algorithms are scrunched (e.g. for success rate, would it help to show failure rate instead?).", "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": "The paper lays out a principled way for group dynamics to be adopted into social navigation algorithms. Looking ahead, real-world testing of the idea will be important to determine the long-term impact of this type of modeling, though it seems like a promising direction.", "technical_quality": "Excellent: Very convincing and thorough.", "clarity_of_presentation": "Good: Minor revision needed to improve organization or clarity.", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"impact": "3: The work is incremental and unlikely to have much impact, even though it may be technically correct and well executed.", "issues": "n/a", "reviewer_expertise": "Excellent: Expert knowledge on the topic of the paper"}, "extra_text": {}}]

Author rebuttal:

Meta-review: Summary: This paper proposes a method for modeling pedestrian agents in groups as opposed to as individuals. The proposed technique designates the groups using a neural network, and the groups’ motion is used within an MPC framework. The proposed method is supported by simulation results. 

Originality: The paper presents an interesting and original idea. Drawing inspiration from psychology, the paper proposes a macroscopic view of the world and show that this reduced fidelity can have benefits to improved navigation behavior. Reviewers commented positively on the appeal behind this idea. 

Clarity: The paper is very clear in presenting the problem, the intuition and technical approach to the solution, and organizing the analysis of the experimental results. Reviewers seemed generally convinced and interest in the idea of using group-based reasoning to induce socially-aware navigation. 

Quality: Reviewers commented on the thoughtful set-up of the experiments to approximate real-world conditions through using public datasets. However, a number of reviewers gave suggestions to further improve the experimental analysis. 

Significance: Reviewers generally saw the potential for impact for this work, but were mixed on the applicability of the proposed technique to real-world scenarios. (See “opportunities for improvement” for more details.) 

Pros: 
This work presents a compelling and novel framing of the navigation problem, a clearly described solution, and thoughtful analysis of experiments. 

Main areas of improvement: 

Reviewers broadly responded very positively to this paper. The major source of mixed feedback was whether or not the paper did enough to show that the proposed approach is practical. In particular, Reviewers rGtm, qTAW, and 6KBt suggest more discussion or simulation experiments to give insight into what it would take to deploy this technique on a real robot. I would suggest real-work applicability as an area of focus in the revision and rebuttal. 

Thank you for considering our feedback, and we look forward to seeing the updated paper. 

================
Final Decision

This paper seems like a clear accept - all reviewers recommended accept, even prior to revisions. Where I am not 100% certain is the boundary between oral v poster. I ultimately recommended oral because two reviewers recommended strong accept, all reviewers gave very good or above scores for technical quality, 3/4 gave very good scores for originality, and other scores were positive by consensus. 
Decision: Accept (Oral)