Learning Geometrically-Grounded Amodal 3D Representations for View-Generalizable Robotic Manipulation
cs.RO
Submitted: 2026-01-30
Updated: 2026-09-27
Terminology
Sources
- PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation
- On Pre-Training for Visuo-Motor Control: Revisiting a Learning-from-Scratch Baseline
- Perceiver IO: A General Architecture for Structured Inputs & Outputs
- DINOv2: Learning Robust Visual Features without Supervision
- DreamFusion: Text-to-3D using 2D Diffusion
- RRL: Resnet as representation for Reinforcement Learning
- Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model
- Masked Visual Pre-training for Motor Control
- DNAct: Diffusion Guided Multi-Task 3D Policy Learning
- Deformable DETR: Deformable Transformers for End-to-End Object Detection
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