Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
cs.RO
Submitted: 2026-04-15
Updated: 2026-10-13
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
- GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data
- Vision Language Action Models in Robotic Manipulation: A Systematic Review
- ThinkAct: Vision-Language-Action Reasoning via Reinforced Visual Latent Planning
- NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards
- NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks
- OpenVLA: An Open-Source Vision-Language-Action Model
- ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries
- RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks
- MAP-VLA: Memory-Augmented Prompting for Vision-Language-Action Model in Robotic Manipulation
- MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Models
- HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model
- RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
- A Survey on Vision-Language-Action Models for Embodied AI
- Meta-Memory: Retrieving and Integrating Semantic-Spatial Memories for Robot Spatial Reasoning
- MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation
- MemER: Scaling Up Memory for Robot Control via Experience Retrieval
- GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning
- MEM: Multi-Scale Embodied Memory for Vision Language Action Models
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