arXiv:2606.03512v1 Announce Type: cross Abstract: Path planning is essential for Autonomous Mobile Robots (AMRs). Conventional methods for incorporating human preferences into planning typically rely on either complex reward engineering or hardware-intensive solutions. Recent state-of-the-art frameworks leverage imitation learning to train behavior-specific path planning models from expert demonstrations. However, these approaches face two key limitations: limited generalization to unseen environments and low robustness in demonstration collection. To address these challenges, this work introd
The proliferation of Autonomous Mobile Robots (AMRs) in diverse environments necessitates more sophisticated and adaptable path planning to overcome the limitations of conventional methods and current imitation learning techniques.
Improved path planning through sketch-guided diffusion models offers a pathway to more robust and generalized robotic autonomy, crucial for widespread adoption and economic impact of AMRs.
This research introduces a novel approach that enhances robot path planning by integrating human preferences through sketches and leveraging diffusion models, potentially simplifying deployment and improving adaptability in new environments.
- · Autonomous Mobile Robot manufacturers
- · Logistics and industrial automation sectors
- · AI researchers in robotics
- · Companies reliant on bespoke or hardware-intensive robot programming
- · Developers of less generalized imitation learning models
More efficient and versatile deployment of AMRs in unpredictable real-world settings.
Reduced operational costs and increased safety in automated environments due to more reliable robot navigation.
Acceleration of robot-as-a-service models and integration of robots into complex, dynamic human-centric spaces.
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Read at arXiv cs.AI