arXiv AI

Evaluating Zero-Shot and One-Shot Adaptation of Small Language Models in Leader-Follower Interaction

arXiv:2602. 23312v3 Announce Type: replace-cross Abstract: Leader-follower interaction is an important paradigm in human-robot interaction (HRI).

arXiv Machine Learning

Jun 17

Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models

arXiv:2606. 17846v1 Announce Type: cross Abstract: Foundation models in language and multimodality achieve strong generalization by aligning heterogeneous data under a unified formulation and training at scale.

By Haoqi Yuan, Zhixuan Liang, Anzhe Chen, Ye Wang, Haoyang Li, Pei Lin, Yiyang Huang, Zixing Lei, Tong Zhang, Jiazhao Zhang, Jie Zhang, Jingyang Fan, Gengze Zhou, Qihang Peng, Chenxu Lv, Xiaoyue Chen, An Yang, Fei Huang, Junyang Lin, Dayiheng Liu, Jingren Zhou, Chenfei Wu, Xiong-Hui Chen

robotics multimodal benchmarks safety

arXiv Machine Learning

Jul 16

HRIBench: Benchmarking Interaction-Centric Human-Robot Collaboration

arXiv:2607. 13056v1 Announce Type: cross Abstract: Current vision-language-action (VLA) benchmarks primarily evaluate isolated manipulation skills while leaving human-robot interaction structure largely unmodeled.

By Chang Liu, Jiawei Zhang, Tao Zhang, Ye Wang, Hongyu Zhou, Qin Jin

agents robotics fine-tuning multimodal benchmarks safety

arXiv AI

Jul 13

CLAP: Direct VLM-to-VLA Adaptation via Language-Action Grounding

arXiv:2607. 08974v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) inherit semantic capabilities from pretrained VLMs, yet large-scale post-training on robot data and architectural modifications can reshape the backbone so extensively that it becomes difficult to isolate what the VLM contributes to control.

By Yuri Ishitoya, Jeremy Siburian, Masashi Hamaya, Kuniaki Saito, Cristian C. Beltran-Hernandez, Mai Nishimura

robotics fine-tuning multimodal

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

Jun 5

Is Diversity All You Need for Scalable Robotic Manipulation?

arXiv:2507. 06219v2 Announce Type: replace-cross Abstract: Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood.

By Modi Shi, Li Chen, Jin Chen, Yuxiang Lu, Chiming Liu, Guanghui Ren, Ping Luo, Di Huang, Maoqing Yao, Hongyang Li

computer-vision nlp robotics fine-tuning

arXiv AI

Jun 11

Bimanual Robot Manipulation via Multi-Agent In-Context Learning

arXiv:2604. 20348v2 Announce Type: replace-cross Abstract: Language Models (LLMs) have emerged as powerful reasoning engines for embodied control.

By Alessio Palma, Indro Spinelli, Vignesh Prasad, Luca Scofano, Yufeng Jin, Georgia Chalvatzaki, Fabio Galasso

llms agents robotics fine-tuning benchmarks

arXiv Machine Learning

Aug 13

Adaptation of Generalist Robot Policies with Minimal Data

arXiv:2608. 11363v1 Announce Type: cross Abstract: A central goal in robot learning is to move beyond task-specific human data collection toward robots that improve through autonomous interaction.

By Shreyas Kowshik, Sreyas Venkataraman, Leo Wang, Niharika Pant, Max Simchowitz, Aviral Kumar

agents reinforcement-learning robotics

arXiv AI

Jul 3

VLAFlow: A Unified Training Framework for Vision-Language-Action Models via Co-training and Future Latent Alignment

arXiv:2607. 01586v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) have recently advanced robotic manipulation, yet the effects of different robot-data pre-training paradigms remain difficult to compare because existing models often differ in architecture, data, action space, and evaluation protocol.

By Guoyang Xia, Fengfa Li, Hongjin Ji, Lei Ren, Fangxiang Feng, Kun Zhan, Yan Xie

robotics multimodal benchmarks safety

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 AI

Jul 22

Athena-Brain Technical Report: An Efficient Robot Brain for General Intelligence and Embodied Interactio

arXiv:2607. 18985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.

By Jialian Li, Junhong Liu, Yuchen Cao, Weiran Guo, Jiaming Song, Xutao Wang, Yi Zhao, Jiangpin Liu, Jie Chen

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jun 10

Task Robustness via Re-Labelling Vision-Action Robot Data

arXiv:2606. 10918v1 Announce Type: cross Abstract: The recent trend in scaling models for robot learning has resulted in impressive policies that can perform various manipulation tasks and generalize to novel scenarios.

By Artur Kuramshin, \"Ozg\"ur Aslan, Cyrus Neary, Glen Berseth

llms robotics multimodal

arXiv AI

Jul 3

Learning to Move Before Learning to Do: Task-Agnostic pretraining for VLAs

arXiv:2607. 02466v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are fundamentally bottlenecked by the scarcity of expert demonstrations -- triplets of observations, instructions, and actions that are costly to collect at scale.

By Junhao Shi, Siyin Wang, Xiaopeng Yu, Li Ji, Jingjing Gong, Xipeng Qiu

agents robotics multimodal benchmarks safety

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