Initial Corpus Batch 01

This batch creates the first real paper corpus for the wiki. It is intentionally mixed: classical autonomous driving, planning-oriented unified stacks, robotics VLA papers, and foundation-model papers that transfer into autonomy research.

Count

27 source-summary pages were added in this batch.

Autonomous driving core

• End To End Learning For Self Driving Cars
• End To End Driving Via Conditional Imitation Learning
• Carla An Open Urban Driving Simulator
• Chauffeurnet Learning To Drive By Imitating The Best And Synthesizing The Worst
• Learning By Cheating
• Vectornet Encoding Hd Maps And Agent Dynamics From Vectorized Representation
• Learning Lane Graph Representations For Motion Forecasting
• Lift Splat Shoot Encoding Images From Arbitrary Camera Rigs By Implicitly Unprojecting To 3D
• Nuscenes A Multimodal Dataset For Autonomous Driving
• Bevformer Learning Birds Eye View Representation From Multi Camera Images Via Spatiotemporal Transformers
• Transfuser Imitation With Transformer Based Sensor Fusion For Autonomous Driving
• Planning Oriented Autonomous Driving
• Vad Vectorized Scene Representation For Efficient Autonomous Driving

Robotics / VLA / language-grounded autonomy

• A Generalist Agent
• Rt 1 Robotics Transformer For Real World Control At Scale
• Palm E An Embodied Multimodal Language Model
• Rt 2 Vision Language Action Models Transfer Web Knowledge To Robotic Control
• Openvla An Open Source Vision Language Action Model
• Drivelm Driving With Graph Visual Question Answering
• Lmdrive Closed Loop End To End Driving With Large Language Models
• Drivevlm The Convergence Of Autonomous Driving And Large Vision Language Models

Foundation model canon

• Attention Is All You Need
• Bert Pre Training Of Deep Bidirectional Transformers For Language Understanding
• Language Models Are Few Shot Learners
• Scaling Laws For Neural Language Models
• Training Compute Optimal Large Language Models
• Learning Transferable Visual Models From Natural Language Supervision

Notes

• AlphaXiv-specific tooling is not available in this session, so these pages were seeded from primary paper pages and canonical conference or arXiv records.
• The next pass should deepen a subset of these pages with benchmark details, assumptions, and explicit contradictions.