Learning and Transferring Closed-Loop Robot Software
cs.RO, cs.AI
Submitted: 2026-09-17
Updated: 2026-09-17
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Code as Policies: Language Model Programs for Embodied Control
- Act-Observe-Rewrite: Multimodal Coding Agents as In-Context Policy Learners for Robot Manipulation
- RHO: Your Coding Agent is Secretly a Roboticist
- Memory Transfer Planning: LLM-driven Context-Aware Code Adaptation for Robot Manipulation
- ASPIRE: Agentic /Skills Discovery for Robotics
- Playful Agentic Robot Learning
- VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
- ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation
- Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought
- Programmatic Imitation Learning from Unlabeled and Noisy Demonstrations
- Iterative Program Synthesis for Adaptable Social Navigation
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- A Few Words Go a Long Way: Language Guided Robot Policy Synthesis
- RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
- RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
- Depth Anything V2
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