arXiv (All)
open access
Bundled Contact Gradients: Stabilizing Differentiable Simulation for Deployable Dynamic Tasks
Differentiable simulation provides analytic gradients of robot dynamics, enabling fast and sample-efficient first-order policy optimization. However, obtaining smooth and informative gradients through rigid-body contact typically requires softened contact models, often at the expense of physical fid
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Learning a Resolution-Consistent Jacobian Field for Bio-Inspired Rigid-Soft Finger#1156939
- Optimize, Learn, Refine: Whole-Body Grasping and Pick-and-Throw with a Spiral Soft Robot#1142804
- Elastic ODYN: Differentiable Optimization for Infeasible Control and Learning in Robotics#1141569
- Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation#627671
- SNAPO: Smooth Neural Adjoint Policy Optimization for Optimal Control via Differentiable Simulation#168331
Record · ID 1105510
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive