让扩散模型实时避开障碍，安全跑得更快
Constraint-Aware Diffusion Guidance for Robotics: Real-Time Obstacle Avoidance for Autonomous Racing
- 在去噪过程中嵌入屏障函数，引导生成满足约束的轨迹
- 仅用少量数据训练即可实现动态环境下的实时避障
- 适用于小型自动驾驶竞速，也适合其他安全关键场景
扩散模型因其捕捉复杂高维数据分布的能力，在机器人领域具有巨大潜力。然而，其缺乏对约束条件的感知，限制了其在安全关键应用中的部署。我们提出约束感知扩散引导（CoDiG），一种数据高效且通用的框架，将屏障函数融入去噪过程，引导扩散采样生成满足约束的输出。CoDiG 在微型自主竞速这一挑战性场景中进行了评估，该场景要求实时障碍物规避。真实世界实验表明，CoDiG 能在动态条件下高效生成安全轨迹，凸显其在更广泛机器人应用中的潜力。演示视频见 https://youtu.be/KNYsTdtdxOU。
原文摘要 · Abstract (English)
Diffusion models hold great potential in robotics due to their ability to capture complex, high-dimensional data distributions. However, their lack of constraint-awareness limits their deployment in safety-critical applications. We propose Constraint-Aware Diffusion Guidance (CoDiG), a data-efficient and general-purpose framework that integrates barrier functions into the denoising process, guiding diffusion sampling toward constraint-satisfying outputs. CoDiG enables constraint satisfaction even with limited training data and generalizes across tasks. We evaluate our framework in the challenging setting of miniature autonomous racing, where real-time obstacle avoidance is essential. Real-world experiments show that CoDiG generates safe outputs efficiently under dynamic conditions, highlighting its potential for broader robotic applications. A demonstration video is available at https://youtu.be/KNYsTdtdxOU.
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