Bundled Contact Gradients: Stabilizing Differentiable Simulation for Deployable Dynamic Tasks
cs.RO
Submitted: 2026-09-25
Updated: 2026-09-25
Code: https://github.com/NVIDIA/warp
Terminology
Sources
- Gradients are Not All You Need
- Proximal Policy Optimization Algorithms
- Brax -- A Differentiable Physics Engine for Large Scale Rigid Body Simulation
- Leveraging Reward Gradients For Reinforcement Learning in Differentiable Physics Simulations
- Learning Quadruped Locomotion Using Differentiable Simulation
- First Order Model-Based RL through Decoupled Backpropagation
- Residual Policy Learning for Perceptive Quadruped Control Using Differentiable Simulation
- Dojo: A Differentiable Physics Engine for Robotics
- Augmenting differentiable physics with randomized smoothing
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving