CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation
Yanchen Ding, Jiayi Chen, Jianfeng Xu, Wei Zhao, Li Yi, He Wang · 2026 arXiv · PDF
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.
TL;DR
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.