StateSkill-Agent: An Effective and Efficient Embodied Agent via State-conditioned Skill
cs.RO
Submitted: 2026-10-10
Updated: 2026-10-10
Sources
- ACE-Brain-0.5: A Unified Embodied Foundational Model for Physical Agentic AI
- RT-1: Robotics Transformer for Real-World Control at Scale
- Agent-R1: A Unified and Modular Framework for Agentic Reinforcement Learning
- RHO: Your Coding Agent is Secretly a Roboticist
- NORA: A Small Open-Sourced Generalist Vision Language Action Model for Embodied Tasks
- Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
- ECG-R1: Protocol-Guided and Modality-Agnostic MLLM for Reliable ECG Interpretation
- RLDX-1 Technical Report
- ExToken: Structured Exploration for Efficient Vision-Language-Action Reinforcement Fine-tuning
- MolmoAct: Action Reasoning Models that can Reason in Space
- ASPIRE: Agentic /Skills Discovery for Robotics
- SkillGen: Verified Inference-Time Agent Skill Synthesis
- Trace2Skill: Distill Trajectory-Local Lessons into Transferable Agent Skills
- DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
- AtomVLA: Scalable Post-Training for Robotic Manipulation via Predictive Latent World Models
- SkillX: Automatically Constructing Skill Knowledge Bases for Agents
- WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens
- RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
- Co-Evolving LLM Decision and Skill Bank Agents for Long-Horizon Tasks
- SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning
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- ARC: A Reasoning Recipe for Robot Foundation Models
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- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies