外观
论文arXiv 2610.2134913 天前
CQDC-Warp：用于接触丰富操作的 GPU 加速可微准动态仿真
原文链接：CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation（arXiv 2610.21349 · 发布日期：2026-10-15）
CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation
CQDC-Warp面向接触丰富操作轨迹合成，提供GPU可微准动态仿真用于批量MPC，并在Allegro手内旋转等实验验证。
核心要点
- 接触丰富操作轨迹合成对机器人学习重要，但联合优化运动与接触决策仍具挑战。
- 方法在规划前指定期望接触并用软代价鼓励，由仿真决定接触几何和摩擦响应。
- 在17物体Allegro手内旋转基准的17408次试验中，成功率达79.8%。
- 相比CFree和IMPACT，平均轨迹生成时间分别降低27.0倍和50.2倍。
验证范围与局限：验证包括17物体Allegro手内旋转基准、LEAP和Shadow手等仿真实验，以及Sharpa手真机轨迹回放。
英文摘要 (Abstract)
Efficient synthesis of contact-rich manipulation trajectories is essential for downstream robot learning. Gradient-based planning offers a promising approach, but jointly optimizing motion and contact decisions remains challenging. Meanwhile, many existing planners run on CPUs with limited throughput. We present CQDC-Warp, a GPU-accelerated differentiable quasi-dynamic simulator for batched model predictive control (MPC). We use it to study contact-guided trajectory optimization, in which intended robot--object contacts are specified before planning and encouraged through soft costs, while the simulated dynamics determine contact geometry and frictional response without a prescribed contact-mode schedule. Across $17{,}408$ trials on the $17$-object Allegro in-hand rotation benchmark, our framework achieves $79.8%$ success, compared with $73.7%$ for CFree and $65.2%$ for IMPACT, and reduces mean trajectory-generation time by factors of $27.0$ and $50.2$, respectively. On a single NVIDIA RTX 4090 with $2{,}048$ parallel worlds, forward and backward throughput reach $2.06\times10^5$ and $2.25\times10^5$ world-steps per second, respectively. Further experiments cover manipulation with LEAP and Shadow hands, environmental contact exploitation, bimanual manipulation, whole-body control, and real-world trajectory replay on a Sharpa hand.
作者：Yanchen Ding, Jiayi Chen, Jianfeng Xu, Wei Zhao, Li Yi, He Wang
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