Robotics and embodied AI

Manipulation, locomotion, sim-to-real transfer and autonomous driving: learning systems that have to survive physics.

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arXiv AI

Jun 11

Intelligent Automation for Embodied Benchmark Construction: Pipelines, Embodiments, Simulators, and Trends

arXiv:2606. 12207v1 Announce Type: cross Abstract: Embodied intelligence now spans navigation, household assistance, manipulation, autonomous driving, aerial agents, and multimodal large-model control.

By Jinshan Lai, Jianwei Hu, Baoyang Jiang, Fengchun Zhang, Leyuan Wang, Haotian Li, Yida Wang, Tingxuan Huang, Xi Ren, Qiang Ma

agents robotics multimodal benchmarks

arXiv AI

Jun 11

The Latent Color Subspace: Emergent Order in High-Dimensional Chaos

arXiv:2603. 12261v2 Announce Type: replace-cross Abstract: Text-to-image generation models have advanced rapidly, yet achieving fine-grained control over generated images remains difficult, largely due to limited understanding of how semantic information is encoded.

By Mateusz Pach, Jessica Bader, Quentin Bouniot, Serge Belongie, Zeynep Akata

diffusion robotics

arXiv AI

Jun 11

Making Foresight Actionable: Repurposing Representation Alignment in World Action Models

arXiv:2606. 12217v1 Announce Type: cross Abstract: World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions.

By Lu Qiu, Yizhuo Li, Yi Chen, Yuying Ge, Yixiao Ge, Xihui Liu

diffusion robotics safety

arXiv Machine Learning

Jun 11

Kalman Linear Attention: Parallel Bayesian Filtering For Efficient Language Modelling and State Tracking

arXiv:2602. 10743v2 Announce Type: replace Abstract: State-space language models such as Mamba and gated linear attention (GLA) offer linear-complexity, parallelisable alternatives to transformers, but their linear state updates limit expressivity and robust state tracking.

By Vaisakh Shaj, Cameron Barker, Aidan Scannell, Andras Szecsenyi, Elliot J. Crowley, Amos Storkey

llms robotics benchmarks

arXiv Machine Learning

Jun 11

MASK: Multi-Agent Semantic K-Scheduling for Risk-Sensitive 6G Robotics

arXiv:2606. 11249v1 Announce Type: cross Abstract: Realizing the vision of 6G connected robotics requires reconciling high-performance collaborative control with the rigid spectral limitations of physical wireless channels.

By Ahmet Gunhan Aydin, Elif Tugce Ceran

agents robotics efficiency benchmarks

arXiv AI

Jun 11

AutoMine Solution for AV2 2026 Scenario Mining Challenge

arXiv:2606. 11874v1 Announce Type: new Abstract: With the development of autonomous driving systems, mining high-value, safety-critical, and planning-relevant scenarios from large-scale driving logs has become essential for data-driven evaluation.

By Songliang Cao, Jiele Zhao, Yuru Wang, Hao Li, Daqi Liu, Zehan Zhang, Fangzhen Li, Yu Wang, Yue Zhang, Bing Wang, Guang Chen, Hao Lu, Hangjun Ye

llms agents robotics multimodal safety

arXiv AI

Jun 11

DuoBench: A Reproducible Benchmark for Bimanual Manipulation in Simulation and the Real World

arXiv:2606. 11901v1 Announce Type: cross Abstract: Bimanual robot systems substantially expand manipulation capabilities, but coordinating two arms introduces additional control complexity and failure modes that are not well captured by existing benchmarks.

By Tobias J\"ulg, Seongjin Bien, Simon Hilber, Yannik Blei, Pierre Krack, Maximilian Li, Sven Parusel, Rudolf Lioutikov, Florian Walter, Wolfram Burgard

robotics multimodal benchmarks

arXiv AI

Jun 11

ConsistencyPlanner: Real-time Planning with Fast-Sampling Consistency Models

arXiv:2606. 11569v1 Announce Type: cross Abstract: Closed-loop planning in complex, real-world driving scenarios presents a critical challenge for autonomous driving systems.

By Qichao Zhang, Xing Fang, Jiaqi Fang, Zhenwen Cai, Jie Ling, Qiankun Yu, Dongbin Zhao

agents robotics multimodal safety

arXiv Machine Learning

Jun 11

DAM-VLA: Decoupled Asynchronous Multimodal Vision Language Action model

arXiv:2606. 12105v1 Announce Type: cross Abstract: Vision-language-action (VLA) models inherit a shared synchronous clock from vision-language pretraining, processing every input at one rate.

By Pankhuri Vanjani, Zhuoyue Li, Jakub Suliga, Moritz Reuss, Gianluca Geraci, Xinkai Jiang, Rudolf Lioutikov

robotics multimodal

arXiv AI

Jun 11

The Unreasonable Effectiveness of Discrete-Time Gaussian Process Mixtures for Robot Policy Learning

arXiv:2505. 03296v2 Announce Type: replace-cross Abstract: We present Mixture of Discrete-time Gaussian Processes (MiDiGap), a novel approach for flexible policy representation and imitation learning in robot manipulation.

By Jan Ole von Hartz, Adrian R\"ofer, Joschka Boedecker, Abhinav Valada

robotics multimodal benchmarks

arXiv AI

Jun 11

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

arXiv:2606. 11324v1 Announce Type: cross Abstract: We introduce Embodied-R1.

By Yifu Yuan, Yaoting Huang, Xianze Yao, Yutong Li, Shuoheng Zhang, Linqi Han, Pengyi Li, Jiangeng Sun, Wenting Jia, Zhao Zhang, Yuhao Liu, Ruihao Liao, Yucheng Hu, Qiyu Wu, Yuxiao Li, Zibin Dong, Fei Ni, Yan Zheng, Shuyang Gu, Yi Ma, Hongyao Tang, Han Hu, Jianye Hao

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 11

PAWS: Preference Learning with Advantage-Weighted Segments

arXiv:2606. 11982v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) learns policies from human trajectory-level comparisons, avoiding explicit reward design and expert demonstrations.

By Aleksandar Taranovic, Onur Celik, Niklas Freymuth, Ge Li, Serge Thilges, Huy Le, Tai Hoang, Rania Rayyes, Gerhard Neumann

reinforcement-learning robotics

arXiv Machine Learning

Jun 11

Self-Supervised Multisensory Pretraining for Contact-Rich Robot Reinforcement Learning

arXiv:2511. 14427v4 Announce Type: replace-cross Abstract: Effective contact-rich manipulation requires robots to synergistically leverage vision, force, and proprioception.

By Rickmer Krohn, Vignesh Prasad, Gabriele Tiboni, Georgia Chalvatzaki

llms rag agents reinforcement-learning robotics multimodal

arXiv AI

Jun 11

RelayFormer: A Unified Local-Global Attention Framework for Scalable Image and Video Manipulation Localization

arXiv:2508. 09459v3 Announce Type: replace-cross Abstract: Visual manipulation localization (VML) aims to identify tampered regions in images and videos, a task that has become increasingly challenging with the rise of advanced editing tools.

By Wen Huang, Jiarui Yang, Tao Dai, Jiawei Li, Shaoxiong Zhan, Bin Wang, Shu-Tao Xia

robotics benchmarks

arXiv Machine Learning

Jun 11

Fourier Features Let Agents Learn High Precision Policies with Imitation Learning

arXiv:2606. 12334v1 Announce Type: new Abstract: High-precision robotic manipulation requires fine-grained spatial reasoning that is often difficult to achieve with RGB-only policies due to depth ambiguity and perspective scale issues.

By Bal\'azs Gyenes, Emiliyan Gospodinov, Jan Frieling, Enrico Krohmer, Nicolas Schreiber, Xiaogang Jia, Niklas Freymuth, Gerhard Neumann

agents robotics benchmarks safety

arXiv Machine Learning

Jun 11

OGPO: Sample Efficient Full-Finetuning of Generative Control Policies

arXiv:2605. 03065v2 Announce Type: replace Abstract: Generative control policies (GCPs), such as diffusion- and flow-based control policies, have emerged as effective parameterizations for robot learning.

By Sarvesh Patil, Mitsuhiko Nakamoto, Manan Agarwal, Shashwat Saxena, Jesse Zhang, Giri Anantharaman, Cleah Winston, Chaoyi Pan, Douglas Chen, Nai-Chieh Huang, Zeynep Temel, Oliver Kroemer, Sergey Levine, Abhishek Gupta, Hongkai Dai, Paarth Shah, Max Simchowitz

diffusion reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jun 11

FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning

arXiv:2606. 12406v1 Announce Type: cross Abstract: Contact-rich manipulation requires force sensitivity, but many robot arms lack dedicated force sensors due to their high cost.

By Steven Oh, Jason Jingzhou Liu, Tony Tao, Philip Han, Kenneth Shaw, Satoshi Funabashi, Ruslan Salakhutdinov, Deepak Pathak

robotics

arXiv AI

Jun 11

TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation

arXiv:2606. 11637v1 Announce Type: new Abstract: Touch is a key modality for embodied agents to understand the physical world.

By Kailin Lyu, Di Wu, Pengwei Zhang, Yuhang Zheng, Yingxin Lai, Long Xiao, Kangyi Wu, Pengna Li, Chen Gao, Lianyu Hu, Xiaobin Hu, Jie Hao, Ce Hao, Weihao Yuan, Shuicheng Yan

agents robotics benchmarks

arXiv Machine Learning

Jun 11

Learning Object Manipulation from Scratch via Contrastive Interaction

arXiv:2606. 11525v1 Announce Type: cross Abstract: Contrastive Reinforcement Learning (CRL) has seen recent success in a wide variety of goal-conditioned robotics tasks by learning structured representations of the dynamics.

By Tongle Shen, Caleb Chuck, Fan Feng, Biwei Huang

agents reinforcement-learning robotics multimodal

arXiv Machine Learning

Jun 11

Energy-Conserved Neural Pipelines: Attenuating Error Propagation in Modular Neural Networks via Physical Conservation Constraints

arXiv:2606. 11341v1 Announce Type: new Abstract: Modular neural network pipelines suffer from error compounding: noise at any module boundary propagates and potentially amplifies through subsequent modules.

By David Young, Swan Yi Htet

robotics safety

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