Residual Modeling Closes the Regression and Generative Policy Gap in Robot Learning
cs.RO
Submitted: 2026-10-08
Updated: 2026-10-08
Project page: https://the-labone.github.io/regression-policy-project
Terminology
Sources
- RT-1: Robotics Transformer for Real-World Control at Scale
- Fast R-CNN
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
- Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
- Evaluating Real-World Robot Manipulation Policies in Simulation
- Flow Matching for Generative Modeling
- What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- Cosmos 3: Omnimodal World Models for Physical AI
- On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks
- Robust Imitation Learning from Noisy Demonstrations
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