Observation-Referenced Tissue Deformation Forecasting with Tool-Driven and Residual Dynamics
cs.RO
Submitted: 2026-10-09
Updated: 2026-10-09
Comments: 8 pages, 6 figures, 1 table. Submitted to ICRA 2027
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- EndoGaussian: Real-time Gaussian Splatting for Dynamic Endoscopic Scene Reconstruction
- Deform3DGS: Flexible Deformation for Fast Surgical Scene Reconstruction with Gaussian Splatting
- SimEndoGS: Efficient Data-driven Scene Simulation using Robotic Surgery Videos via Physics-embedded 3D Gaussians
- EndoGSim: Physics-Aware 4D Dynamic Endoscopic Scene Simulations via MLLM-Guided Gaussian Splatting
- Realistic Surgical Simulation from Monocular Videos
- Learning Mesh-Based Simulation with Graph Networks
- Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
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
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies