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This is a Experimental Study study.

End-to-end robot intelligent obstacle avoidance method based on deep reinforcement learning with spatiotemporal transformer architecture

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This method enhances obstacle avoidance and decision-making in robots, suggesting significant improvement in handling dynamic environments.

The proposed method achieves a higher obstacle avoidance success rate in complex environments, demonstrating effective decision-making.
Integrating deep reinforcement learning with spatiotemporal attention improves environmental modeling and generalization capabilities.
Using a Transformer-based architecture enables high-dimensional feature modeling without manual engineering, streamlining the learning process.
Experiments indicate the method's stability and broad application prospects in real-world scenarios, significantly outperforming traditional approaches.

Zhou et al. (2025) studied this question.

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This method enhances obstacle avoidance and decision-making in robots, suggesting significant improvement in handling dynamic environments.