arXiv AI

Knowledge Reutilization in Meta-Reinforcement Learning

arXiv:2606. 18132v1 Announce Type: new Abstract: Meta-reinforcement learning enables fast adaptation by extracting shared structure from related tasks, but existing end-to-end methods often couple task inference with embodiment-specific control.

arXiv Machine Learning

Aug 12

BooST: Bridging Semantics and Motions for Efficient Skill Transfer

arXiv:2608. 10600v1 Announce Type: cross Abstract: Skill abstraction---the process of learning reusable and temporally extended behaviors---has emerged as a key paradigm for improving sample efficiency and generalization in robot learning.

By Jusuk Lee, Daesol Cho, Jonghun Shin, Seungyeon Yoo, Jonghae Park, Taekbeom Lee, H. Jin Kim

robotics multimodal

arXiv Machine Learning

Jul 20

Dynamics-Aware Meta-Imitation for Generalization to Unseen Robotic Manipulation

arXiv:2607. 15880v1 Announce Type: cross Abstract: Imitation Learning aims to learn skills from extensive observations and demonstrations for robots, so it suffers from data scarcity and environment generalization.

By Zhenduo Shang, Xiyao Liu, Bohan Li, Xudong Wang, Teng Ren, Lianqing Liu, Zhi Han

agents diffusion robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 5

Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning

arXiv:2512. 09706v2 Announce Type: replace Abstract: The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models.

By Kaichen He, Zihao Wang, Muyao Li, Anji Liu, Yitao Liang

agents reinforcement-learning robotics fine-tuning 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

1d ago

Temporal Logic Guided Universal Task Representations for Reinforcement Learning

arXiv:2608. 15509v1 Announce Type: cross Abstract: Task guided agents demonstrate strong performance in a wide range of complex tasks.

By Hao Zhang, Zhangli Zhou, Zhen Kan

agents reinforcement-learning robotics

arXiv AI

Jul 7

Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation

arXiv:2607. 05377v1 Announce Type: cross Abstract: While recent Vision-Language-Action (VLA) models show promise toward generalist manipulation policies, they struggle with long-horizon tasks due to their Markovian nature-relying solely on current observations.

By Jiaqi Peng, Xiqian Yu, Delin Feng, Yuqiang Yang, Wenzhe Cai, Jing Xiong, Ganlin Yang, Jinliang Zheng, Jiafei Cao, Xueyuan Wei, Jiangmiao Pang, Yuan Shen, Tai Wang

agents robotics fine-tuning multimodal

arXiv AI

Jul 20

SLAC: Safe and Efficient Real-Robot Reinforcement Learning via Unsupervised Simulation Pre-Training

arXiv:2506. 04147v5 Announce Type: replace-cross Abstract: Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators.

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

reinforcement-learning robotics benchmarks safety

arXiv AI

Jul 22

From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning

arXiv:2607. 18830v1 Announce Type: cross Abstract: Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks.

By Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi

rag reinforcement-learning benchmarks

arXiv AI

Jul 1

MIRTH: Mutual-Information Reasoning with Temporal Hubs for Vision-Language-Action Agents

arXiv:2606. 31167v1 Announce Type: cross Abstract: VLA models have emerged as a powerful paradigm for transferring semantic knowledge from web-scale data to physical robotic control.

By Hao Sun, Yu Song, Shiyu Teng, Ziwei Niu, Yen-Wei Chen

rag agents robotics efficiency multimodal benchmarks

arXiv AI

Jun 30

Towards Continual Motion-Language Agents: LoRA Variants for Incremental Motion Understanding and Generation

arXiv:2606. 30266v1 Announce Type: cross Abstract: Motion-language agents must possess the bidirectional capability to both understand human movement (motion-to-text, M2T) and generate it from natural language (text-to-motion, T2M).

By Bertram Taetz, Hugo Albuquerque Cosme da Silva, Gabriele Bleser-Taetz

llms agents fine-tuning benchmarks

arXiv Machine Learning

Aug 10

Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection

arXiv:2608. 06434v1 Announce Type: cross Abstract: Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness.

By Yuewei Sun, Lang Qin, Zechuan Tian, Jingwen Li, Guiqin Wang, Shengzeng Huo, Wenxin Ren, Tao Fang, Xiaochen Zhang, Guanqing Deng, Xiang Wang, Xiaowen Dong, Qinghai Guo, Yuxin Ma

robotics efficiency multimodal benchmarks

arXiv Machine Learning

Aug 11

LUCID: Latent-Skill Unified Control via Imagined Dynamics for Long-Horizon Humanoid Loco-Manipulation

arXiv:2608. 07746v1 Announce Type: new Abstract: Long-horizon humanoid loco-manipulation requires composing versatile whole-body skills and reliable high-level decision making.

By Cheng Guo, Mingzhe Ni, Angelo Cangelosi, Arash Ajoudani

reinforcement-learning robotics safety

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