CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation

Key points

• Gradient-based planning offers a promising approach, but jointly optimizing motion and contact decisions remains challenging.1
• We present CQDC-Warp, a GPU-accelerated differentiable quasi-dynamic simulator for batched model predictive control (MPC).1
• 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.1
• 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.1

Sources (1)

• [1]CQDC-Warp: GPU-Accelerated Differentiable Quasi-Dynamic Simulation for Contact-Rich Manipulation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 06:30 AM

  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.

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