Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence
cs.RO
Submitted: 2026-09-28
Updated: 2026-09-28
Terminology
Sources
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
- VisualPatchWorld: Code World Models as Latent Structured Representations for Planning
- Qwen3-VL Technical Report
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RoboScript: Code Generation for Free-Form Manipulation Tasks across Real and Simulation
- GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
- Evaluating Large Language Models Trained on Code
- RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
- Code World Model: Coding Agent as World Brain
- CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
- Vision-Language Models as Success Detectors
- XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations
- LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- LoRA: Low-Rank Adaptation of Large Language Models
- GenSim2: Scaling Robot Data Generation with Multi-modal and Reasoning LLMs
- Agent-Driven Autonomous Reinforcement Learning Research: Iterative Policy Improvement for Quadruped Locomotion
- RoboCoder: Robotic Learning from Basic Skills to General Tasks with Large Language Models
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