DetAug: Obstacle-Blind Trajectory Augmentation for Zero-shot Obstacle Avoidance
cs.RO
Submitted: 2026-09-16
Updated: 2026-09-16
Code: https://github.com/newton-physics/newton
Terminology
Sources
- CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidance
- EmbodiSteer: Steering Embodiment-Agnostic Visuomotor Policies with Joint-Space Guidance for Zero-Shot Cross-Embodiment Deployment
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- OmniGuide: Universal Guidance Fields for Enhancing Generalist Robot Policies
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- VLS: Steering Pretrained Robot Policies via Vision-Language Models
- ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies
- From Noise to Control: Parameterized Diffusion Policies
- Planning-Guided Diffusion Policy Learning for Generalizable Contact-Rich Bimanual Manipulation
- Improving Trajectory Stitching with Flow Models
- Classifier-Free Diffusion Guidance
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