Organizations: National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Nation Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, P.R. China
Navigating social robots in dense, dynamic crowds is challenging due to environmental uncertainty and complex human-robot interactions. While Model Predictive Control (MPC) offers strong real-time performance, its reliance on a fixed prediction horizon limits adaptability to changing environments and social dynamics. Furthermore, most MPC approaches treat pedestrians as homogeneous obstacles, ignoring social heterogeneity and cooperative or adversarial interactions, which often causes the Frozen Robot Problem in partially observable real-world environments. In this paper, we identify the planning horizon as a socially conditioned decision variable rather than a fixed design choice. Building on this insight, we propose an optimal-horizon social navigation framework that optimizes MPC foresight online according to inferred social context. A spatio-temporal Transformer infers pedestrian cooperation attributes from local trajectory observations, which serve as social priors for a reinforcement learning policy that optimally selects the prediction horizon under a task-driven objective. The resulting horizon-aware MPC incorporates socially conditioned safety constraints to balance navigation efficiency and interaction safety. Extensive simulations and real-world robot experiments demonstrate that optimal foresight selection is critical for robust social navigation in partially observable crowds. Compared to state-of-the-art baselines, the proposed approach achieves a 6.8% improvement in success rate, reduces collisions by 50%, and shortens navigation time by 19%, with a low timeout rate of 0.8%, validating the necessity of socially optimal planning horizons for efficient and safe robot navigation in crowded environments. Code and videos are available at Under Review.
Figures & tables
Fig. 1: Navigation success rates under fixed MPC prediction horizons, averaged over 250 random trials. Solid lines indicate the average performance, while vertical error bars represent performance variations across different pedestrian behavior modes. Note that the low-interaction scenario lacks error bars as all pedestrians are consistently non-cooperative, resulting in a single behavior mode.
Fig. 2: Overview of the Optimal-Horizon Social Robot Navigation framework. Stage I: The Spatio-Temporal Transformer infers pedestrian cooperation attributes ctp from trajectory history. Stage II: The RL policy determines the optimal prediction horizon ht based on the inferred social graph Gt . Stage III: The DTCBF-constrained MPC generates safe, socially-compliant control commands ut∗ by adapting its look-ahead depth and safety margins.
Method
Low Interaction Scenarios
Mid Interaction Scenarios
High Interaction Scenarios
SR ↑
CR ↓
OR ↓
ANT ↓
AIR ↓
SR ↑
CR ↓
OR ↓
ANT ↓
AIR ↓
SR ↑
CR ↓
OR ↓
ANT ↓
AIR ↓
ORCA [ 22 ]
66.4
24.4
9.2
12.18
28.19
75.6
15.2
9.2
12.66
25.75
84.8
7.2
8.0
12.21
24.24
SF [ 23 ]
18.0
72.4
9.6
14.69
26.80
30.4
56.4
13.2
14.16
24.23
42.0
45.2
12.8
14.64
22.51
DS-RNN [ 24 ]
51.2
42.4
6.4
12.60
21.54
56.4
36.8
6.8
13.06
20.97
70.0
21.2
8.8
12.42
18.79
GST-RL [ 25 ]
82.8
10.0
7.2
13.78
12.54
84.8
7.2
8.0
13.30
12.09
89.6
5.6
4.8
12.42
11.51
Ours
87.6
11.6
0.8
10.99
25.84
92.0
7.2
0.8
10.33
23.25
96.4
2.8
0.8
9.88
20.40
TABLE I: Comparison of state-of-the-art methods in multiple navigation scenarios with varying degrees of interactivity.
Fig. 3: Trajectories of all methods in low-interaction scenarios. All pedestrians in low-interaction scenarios are non-cooperative. The golden solid circles represent robot trajectories with timestamps. The hollow circles in different colors represent pedestrians, with pedestrian IDs shown at start and end points.
Figure 5
Fig. 4: Illustration of the navigation trajectory of ORCA and our proposed method in mid-interaction scenarios.
Fig. 5: Illustration of the navigation trajectory of GST-RL and our proposed method in high-interaction scenarios.
Social robot navigation studies how robots move safely and comfortably around people while respecting human behavior, preferences, and shared-space norms.
Safe and socially compliant navigation remains a fundamental challenge for autonomous robots operating in human-populated environments. Beyond collision avoidance, robots must anticipate human motion and respect personal space to ensure human comfort. Model Predictive Control (MPC) offers a robust alternative to classical and data-driven methods, although its effectiveness strongly depends on accurate human motion prediction and efficient computation. This paper introduces SFM-NMPC, a Social Force Model-based Non-linear Model Predictive Control framework that embeds human motion prediction directly within the optimization loop. By incorporating the Social Force Model into the dynamic model of surrounding agents, the controller jointly predicts the trajectories of humans and robots over the prediction horizon, thereby enabling socially-aware planning. A tailored set of social cost functions guides the optimization toward human-compliant behaviors. Despite the increased model complexity, the proposed formulation runs in real time at 20 Hz. Extensive simulated testing in crowded environments demonstrates that SFM-NMPC outperforms state-of-the-art baselines in social compliance metrics while maintaining efficient and smooth navigation. Visual trajectory analysis and an ablation study further highlight the contribution of the embedded SFM dynamics and social cost terms, confirming the effectiveness of the proposed approach for real-world social navigation.
Stefano Trepella, Andrea Ostuni, Mauro Martini +5
Department of Electronics and Telecommunications, Politecnico di Torino, 10129, Torino, Italy. · School of Engineering, Pablo de Olavide University, Crta. Utr-era km 1, Seville, Spain
Safe and efficient robot navigation in crowds requires anticipating pedestrian motion despite uncertain and potentially shifting prediction errors. Existing reactive methods can produce oscillatory behavior, while predictive planners often treat forecasts as exact or rely on restrictive error models. Incorporating conservative uncertainty sets as hard constraints can also render model predictive control (MPC) infeasible. We propose CoCoNav, a crowd-navigation framework that combines online conformal calibration with runtime-certified planning. A horizon-specific conformal proportional--integral controller adapts trajectory-error bounds to regulate long-run empirical coverage, enabling the framework to respond to changing prediction errors. A relax-then-verify planner preserves solver feasibility by generating nominal trajectories with soft-constrained MPC and separately certifying them, together with contingency maneuvers, against the calibrated bounds before execution. Simulations and quadruped experiments show that CoCoNav achieves a favorable balance among collision avoidance, task success, and navigation efficiency relative to the evaluated baselines.
Cheng Guo, Mingzhe Ni, Zheng Liang +5
Department of Computer Science, The University of Manchester, Manchester, UK. · Human-Robot Interfaces and Interaction Laboratory, Italian Institute of Technology, Genoa, Italy. · Genisom AI, Shanghai, China. +3
Reinforcement learning enables mobile robots to make fast and scalable navigation decisions in constrained human-shared environments. However, directly extending single-robot approaches to multi-robot settings often fails to ensure safety and efficiency, especially under occlusions and out-of-distribution (OOD) scenarios, where individual robots may overlook potential risks or make inaccurate predictions. Leveraging cooperation effectively for both prediction and navigation remains a major challenge. We propose CoopUQ, a multi-robot social navigation framework that bridges uncertainty-aware cooperative occupancy forecasting and robust decision making. CoopUQ predicts future occupancy grid maps through cross-robot spatiotemporal feature fusion and further leverages forecasts and their uncertainty to guide policy via constrained multi-agent reinforcement learning, where the cost penalizes navigation through regions predicted to be occupied or highly uncertain. This design enables the policy to adapt its risk sensitivity under distribution shifts. Experimental results across multiple Unity3D-based simulation environments with both in-distribution and OOD settings, including diverse layouts, pedestrian densities, and rushing behavior, demonstrate improved prediction performance and safety over representative baselines. Real-robot experiments further demonstrate its robustness across various scenes. Our code and videos are available at https://coopuq.github.io/.
Zehao Wang, Jianpeng Yao, Yuping Wang +9
Georgia Institute of Technology · University of California, Riverside · University of Michigan, Ann Arbor +3