Few-Shot Neural Differentiable Simulator: Real-to-Sim Rigid-Contact Modeling
Few-Shot Neural Differentiable Simulator: Real-to-Sim Rigid-Contact Modeling explores A few-shot real-to-sim approach that combines analytical simulators with graph neural networks for accurate and differentiable physics simulation, enabling efficient policy learning in robotic manipulation.. Commercial viability score: 7/10 in Robotics Simulation.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisSim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative PerturbationViability: 4.0
- Builds On ThisStability-Guided Exploration for Diverse Motion GenerationViability: 6.0
- Prior WorkSim-to-Real of Humanoid Locomotion Policies via Joint Torque Space Perturbation InjectionViability: 7.0
- Prior WorkHALO:Closing Sim-to-Real Gap for Heavy-loaded Humanoid Agile Motion Skills via Differentiable SimulationViability: 7.0
- Higher ViabilityDifferentiable Inverse Graphics for Zero-shot Scene Reconstruction and Robot Grasping