Vladlen Koltun, Yi-Ling Qiao, Junbang Liang, Ming Lin
We lifted 1 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| yilingqiao/diff_fem | canonical | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| get_loss | Not yet run | yilingqiao/diff_fem/python/demo_baymax.py code served (permissive licence) · get_code("dc59b1fab9598e62") |
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We present a method for differentiable simulation of soft articulated bodies. Our work enables the integration of differentiable physical dynamics into gradient-based pipelines. We develop a top-down matrix assembly algorithm within Projective Dynamics and derive a generalized dry friction model for soft continuum using a new matrix splitting strategy. We derive a differentiable control framework for soft articulated bodies driven by muscles, joint torques, or pneumatic tubes. The experiments demonstrate that our designs make soft body simulation more stable and realistic compared to other frameworks. Our method accelerates the solution of system identification problems by more than an order of magnitude, and enables efficient gradient-based learning of motion control with soft robots.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2205.01758")
get_code_for_paper("2205.01758")
have("2205.01758")
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