BARD is a self-contained PyTorch implementation of Featherstone's rigid body dynamics optimized for batched GPU evaluation and differentiation, reporting up to 64x throughput gains over Pinocchio on robot models with 7-23 DOFs.
Fast and feature-complete differentiable physics for articulated rigid bodies with contact.arXiv preprint arXiv:2103.16021
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2representative citing papers
In policy gradient RL, careful variance control and simple estimator switching frequently outperform explicit discontinuity detection even when using differentiable simulators.
citing papers explorer
-
Batched Differentiable Rigid Body Dynamics in PyTorch for GPU-Accelerated Robot Learning
BARD is a self-contained PyTorch implementation of Featherstone's rigid body dynamics optimized for batched GPU evaluation and differentiation, reporting up to 64x throughput gains over Pinocchio on robot models with 7-23 DOFs.
-
Does "Do Differentiable Simulators Give Better Policy Gradients?'' Give Better Policy Gradients?
In policy gradient RL, careful variance control and simple estimator switching frequently outperform explicit discontinuity detection even when using differentiable simulators.