mlsanityData to 7 Oct 2026

Navigating the Human Maze: Real-Time Robot Pathfinding with Generative Imitation Learning

arXiv 2408.03807 ↗First posted 7 Aug 2024cs.RO cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Social robot navigation

Abstract (arXiv)

This paper addresses navigation in crowded environments by integrating goal-conditioned generative models with Sampling-based Model Predictive Control (SMPC). We introduce goal-conditioned autoregressive models to generate crowd behaviors, capturing intricate interactions among individuals. The model processes potential robot trajectory samples and predicts the reactions of surrounding individuals, enabling proactive robotic navigation in complex scenarios. Extensive experiments show that this algorithm enables real-time navigation, significantly reducing collision rates and path lengths, and outperforming selected baseline methods. The practical effectiveness of this algorithm is validated on an actual robotic platform, demonstrating its capability in dynamic settings.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Social robot navigation, one of 13 directions in Offline RL and imitation learning from data. Builds on 34 of its 59 references that are in the map. First posted on arXiv 7 Aug 2024.

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34 of 59 references.

References · 34

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22 more
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  1. 2026Diffusion Policies for Short-Horizon Planning in Robot Crowd Navigation
  2. 2025Generative Predictive Control: Flow Matching Policies for Dynamic and Difficult-to-Demonstrate Tasks
  3. 2025SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions
  4. 2026Trajectory Learning with Graph Representations for Social Robot Navigation
  5. 2026Crowd-FM: Learned Optimal Selection of Conditional Flow Matching-generated Trajectories for Crowd Navigation
  6. 2020NaviGAN: A Generative Approach for Socially Compliant Navigation
  7. 2026FLUX: Accelerating Cross-Embodiment Generative Navigation Policies via Rectified Flow and Static-to-Dynamic Learning
  8. 2026Diffusion for Long-Horizon Multi-Robot Path Planning in Human-Shared Environments ↗