GASP实现实时安全运动规划，毫秒级生成避障机械臂轨迹。
GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling
- 用约束样条+神经网络预测轨迹点，边界点保证起止导数约束。
- GPU并行采样解码多条轨迹，近毫秒完成推理，成功率接近解析解。
- 适合需要实时避障的机器人控制场景，尤其适用于强化学习训练加速。
我们提出 GASP，一种用于已知环境中实时、避障联合空间运动规划的 GPU 加速安全规划器。GASP 将截断样条轨迹参数化与卷积残差神经网络结合，预测自由内部控制点，而分析插入的边界控制点则在非稳态条件下强制执行初始和最终导数约束。一个条件变分自编码器采样多个轨迹候选，这些候选在 GPU 上并行解码与验证，实现批处理的避障耦合关节空间运动规划，推理时间接近毫秒级。我们在在线运动生成模块中验证了 GASP，其成功率接近解析解，具备高避障可行性，并显著降低相对于基于 GPU 的轨迹优化的推理时间。此外，我们将 GASP 部署为竞争性机器人乒乓球任务中的强化学习重置规划器，在匹配基线回报率的同时，训练过程中的碰撞次数减少约一半。
原文摘要 · Abstract (English)
We present GASP, a GPU-Accelerated Safe Planner for real-time, collision-aware joint-space motion generation in known environments. GASP combines a clamped B-spline trajectory parameterization with a convolutional residual neural network that predicts the free interior control points, while analytically inserted boundary control points enforce initial and final derivative constraints for collision-aware planning under non-stationary conditions. A conditional variational autoencoder samples multiple trajectory candidates, which are decoded and validated in parallel on the GPU, yielding a batched planner for collision-aware coupled joint-space motion with near-millisecond inference. We validate GASP as an online motion-generation module, where it achieves analytical-level success rates with high collision-aware feasibility and substantially reduces inference time relative to GPU-based trajectory optimization. We further deploy GASP as a reinforcement-learning reset planner in competitive robotic table tennis, matching the baseline return rate while roughly halving training-time collisions.
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