SkillWeaver: Agentic Exploration over Neural Interaction Skills for Scalable Robot Data Generation
cs.RO
Submitted: 2026-09-28
Updated: 2026-09-28
Project page: https://katefgroup.github.io/skillweaver
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- InternData-A1: Pioneering High-Fidelity Synthetic Data for Pre-training Generalist Policy
- MolmoB0T: Large-Scale Simulation Enables Zero-Shot Manipulation
- Genie Sim 3.0 : A High-Fidelity Comprehensive Simulation Platform for Humanoid Robot
- GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
- cuRobo: Parallelized Collision-Free Minimum-Jerk Robot Motion Generation
- Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
- Playful Agentic Robot Learning
- ASPIRE: Agentic /Skills Discovery for Robotics
- RoboNet: Large-Scale Multi-Robot Learning
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- ManiSkill2: A Unified Benchmark for Generalizable Manipulation Skills
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- GenSim: Generating Robotic Simulation Tasks via Large Language Models
- MimicGen: A Data Generation System for Scalable Robot Learning using Human Demonstrations
- TidyBot: Personalized Robot Assistance with Large Language Models
- CoPa: General Robotic Manipulation through Spatial Constraints of Parts with Foundation Models
- CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation
- Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
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- 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