AI 中文总结
本研究提出GASP这一GPU加速安全规划器，结合隐式轨迹采样，可实现毫秒级碰撞感知关节空间运动生成，在机器人乒乓球任务中表现优异且大幅降低训练碰撞次数。
AI 中文摘要
我们提出了GASP（GPU加速安全规划器），用于在已知环境中实现实时、碰撞感知的关节空间运动生成。GASP将 clamped B样条轨迹参数化与卷积残差神经网络相结合，该网络可预测内部自由控制点，同时通过解析插入的边界控制点来强制执行初始和最终导数约束，以应对非平稳条件下的碰撞感知规划。条件变分自编码器会采样多个轨迹候选，这些候选在GPU上并行解码和验证，从而形成用于碰撞感知耦合关节空间运动的批量规划器，推理时间接近毫秒级。我们将GASP作为在线运动生成模块进行验证，其达到了解析级别的成功率，具备高碰撞感知可行性，且与基于GPU的轨迹优化相比大幅缩短了推理时间。我们还将GASP部署为强化学习重置规划器，用于竞技机器人乒乓球任务，其表现与基线返回率相当，同时训练期间的碰撞次数减少了约一半。
英文摘要
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.
