New Ostrich simulator enables faster, more memory-efficient differentiable dynamics

Researchers have developed Ostrich, a novel GPU-accelerated rigid-body simulator designed for differentiable dynamics. Ostrich addresses limitations in existing simulators by enabling gradient-based optimization through contact at significantly larger timesteps. It achieves this using a non-smooth Newton iteration and differentiating the converged residual via the implicit function theorem, with memory usage that is constant per timestep. AI

IMPACT This new simulator could accelerate research and development in robotics and reinforcement learning by enabling more efficient gradient-based optimization through contact.

RANK_REASON The cluster contains a research paper detailing a new simulator. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arXiv
• graphics processing unit
• Hugging Face
• MuJoCo
• Newton Semi-Implicit

• paper
• infra

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