arXiv AI

SUSD: Structured Unsupervised Skill Discovery through State Factorization

arXiv:2602. 01619v2 Announce Type: replace-cross Abstract: Unsupervised Skill Discovery (USD) aims to autonomously learn a diverse set of skills without relying on extrinsic rewards.

arXiv Machine Learning

Jul 14

Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning

arXiv:2410. 11251v2 Announce Type: replace Abstract: A hallmark of intelligent agents is the ability to learn reusable skills purely from unsupervised interaction with the environment.

By Jiaheng Hu, Zizhao Wang, Peter Stone, Roberto Mart\'in-Mart\'in

agents reinforcement-learning

arXiv Machine Learning

Jun 2

COLLIE: Guiding Skill Discovery in Semantically Coherent Latent Space

arXiv:2606. 00950v1 Announce Type: new Abstract: Unsupervised skill discovery (USD) aims to learn diverse behaviors without reward functions, but often results in task-irrelevant or hazardous behaviors due to uniform exploration.

By Yao Luan, Ni Mu, Hanfei Ge, Yiqin Yang, Bo Xu, Qing-Shan Jia

diffusion

arXiv Machine Learning

Jun 2

World-Task Factorization for Robot Learning

arXiv:2606. 02027v1 Announce Type: cross Abstract: Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments.

By Eduardo Sebasti\'an, Adrian Pfisterer, Vito Mengers, Oliver Brock, Amanda Prorok

robotics

Hugging Face Trending Papers

Jun 1

World-Task Factorization for Robot Learning

Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments. To achieve this, we must structurally factor the policy, which is a choice that dictates what generalizes, what requires retraining, and what remains entangled.

robotics

arXiv AI

Jun 16

LatentGym: A Testbed For Cross-Task Experiential Learning With Controllable Latent Structure

arXiv:2606. 15306v1 Announce Type: cross Abstract: We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared across those tasks and use it to improve future decisions.

By Daksh Mittal, Tommaso Castellani, Thomson Yen, Naimeng Ye, Fangyu Wu, Minghui Chen, Tiffany Cai, Emmanouil Koukoumidis, William Zeng, Hongseok Namkoong

llms agents

arXiv Machine Learning

Jul 7

Learning Task-Sufficient World Models by Synergizing Agentic Exploration and Structured Modeling

arXiv:2607. 04409v1 Announce Type: new Abstract: Learning and planning in imagination using world models provides an effective paradigm for training agents for decision-making.

By Fan Feng, Yujia Zheng, Minghao Fu, Yongqiang Chen, Guangyi Chen, Kevin Murphy, Biwei Huang, Kun Zhang

rag agents robotics benchmarks

arXiv Machine Learning

Jul 30

SkillRise: Agentic Reinforcement Learning for Cross-Task Skill Evolution

arXiv:2607. 26784v1 Announce Type: new Abstract: Large language model agents often encounter related yet distinct tasks that share reusable solution patterns.

By Zhiyuan Yao, Yuxin Chen, Zhengxi Lu, Zishan Xu, Yueqing Sun, Yifu Guo, Yuquan Lu, Zhengzhou Cai, Kangning Zhang, Zhuowen Han, Zi-Han Wang, Ziang Ye, Qi Gu, Xunliang Cai, Weiwen Liu, Yongliang Shen

llms agents reinforcement-learning

arXiv AI

Aug 5

Agentic Reinforcement Learning with Self-Distilled Reward Shaping

arXiv:2608. 03223v1 Announce Type: cross Abstract: Agentic reinforcement learning enables LLM agents to learn through interaction, but sparse trajectory-level rewards reveal success without identifying which intermediate decisions deserve credit.

By Ranxu Zhang, Guinan Chen, Chenshaodong, Jinghao Lin, Xiaozhou Xu, Sunzhe, Yanyong Zhang, Chao Wang

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 7

SkillMemo: Expert-guided Skill Memory Framework for Compositional Embodied Manipulation

arXiv:2608. 05970v1 Announce Type: cross Abstract: Embodied visuomotor models, including Diffusion Policy (DP) and Vision-Language-Action (VLA) models, have demonstrated promising performance on robotic manipulation benchmarks.

By Changyuan Wang, Chubin Zhang, Zhenyu Wu, Runhao Li, Angyuan Ma, Ke Chao, Yinan Liang, Xiuwei Xu, Ziwei Wang, Yansong Tang, Jiwen Lu

diffusion computer-vision robotics multimodal benchmarks

arXiv Machine Learning

Jul 7

Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation

arXiv:2607. 03522v1 Announce Type: new Abstract: Fine-tuning a single low-rank adapter on many domains at once is multi-task learning: the domains must be co-learned, and how they share the adapter decides whether they help or hurt one another.

By Wei Zhang, Lin Tang, Ming Zhao, Yuxuan Wang

fine-tuning

arXiv AI

Jun 16

OpenClaw-Skill: Collective Skill Tree Search for Agentic Large Language Models

arXiv:2606. 16774v1 Announce Type: new Abstract: Equipping Large Language Model (LLM) agents with effective skills is crucial for solving complex tasks in real-world systems like OpenClaw.

By Tianyi Lin, Chuanyu Sun, Jingyi Zhang, Changxu Wei, Huanjin Yao, Shunyu Liu, Xikun Zhang, Liu Liu, Jiaxing Huang

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 21

Generalize and Guide: Decomposing Rewards for Few-Shot Inverse Reinforcement Learning

arXiv:2607. 17760v1 Announce Type: cross Abstract: Inverse reinforcement learning (IRL) provides a powerful framework for learning from demonstrations.

By Ziyi Liu, Grace Zhang

agents reinforcement-learning robotics

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