RoboAuto is a framework that enhances autonomous robot program search by explicitly incorporating physical understanding at the object, scene, and interaction levels. It uses a hierarchical knowledge base of executable skills and object-centric affordances, scene grounding to map knowledge to the current environment, and multi-stage planning with self-designed experiments to verify physical effects and resolve uncertainties. The approach achieves higher success rates than prior methods on multiple benchmark tasks and demonstrates transferability across simulators and embodiments.
By Buzhao Liu, Jianing Ye, Xiu Yuan, Yangtong Feng, Chengsong Huang, Tyler Dinh, Yevgeniy Vorobeychik, Ning Zhang, Chongjie Zhang
arXiv:2607. 00272v1 Announce Type: cross Abstract: Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.
By Runyu Lu, Yubo Wu, Ethan Kou, Letian Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Yuke Zhu, Linxi "Jim" Fan, Guanzhi Wang
RoboSPA is a large-scale robotic manipulation dataset and benchmark designed to evaluate Vision‑Language‑Action models on fine‑grained spatial reasoning and long‑horizon procedural planning. It contains 10 task categories, 56 base tasks, and 280 variants across five difficulty levels, with 527K trajectories collected from multiple embodiments and scenes. The benchmark introduces diagnostic metrics beyond binary success, revealing that current VLA models struggle with complex spatial relations, precise execution, and memory‑intensive planning.
By Zhenxuan Fan, Bo Zhang, Yutong Lin, Yuqian Yuan, Juekai Lin, Liang Liang, Zhuoyi Huang, Wenqiao Zhang, Juncheng Li, Siliang Tang, Jun Xiao, Yueting Zhuang
HarnessPAI introduces a model‑ and embodiment‑agnostic harness framework for Physical AI that treats code as an executable, evolvable interface organizing action primitives. The framework operates on two timescales: within a rollout it executes an open‑loop program, and across rollouts it evolves a closed‑loop program using feedback to refine the program and distill reusable skills. Across diverse robotic platforms, HarnessPAI outperforms pure action models and code‑as‑policy baselines, achieving significant gains on tasks such as LIBERO‑PRO and RoboCasa, and enabling efficient expert‑data collection for further fine‑tuning.
arXiv:2503. 22122v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) have demonstrated remarkable capabilities in robotic planning, particularly for long-horizon tasks that require a holistic understanding of the environment for task decomposition.
By Puzhen Yuan, Angyuan Ma, Yunchao Yao, Huaxiu Yao, Masayoshi Tomizuka, Mingyu Ding
arXiv:2610.36013v1 Announce Type: new
Abstract: General-purpose embodied intelligence requires robots to coordinate diverse skills over long horizons, track task progress, and recover from failures....
By Xiaomi Embodied Intelligence Team, Yuechen Luo (University of Macau, Tsinghua University), Chong He (University of Macau, Tsinghua University), Fang Li (University of Macau, Tsinghua University), Shaoqing Xu (University of Macau, Tsinghua University), Jingkai Wang (University of Macau, Tsinghua University), Menglun Su (University of Macau, Tsinghua University), Jiaxin Liu (University of Macau, Tsinghua University), Shengyin Jiang (University of Macau, Tsinghua University), Zhiyi Lai (University of Macau, Tsinghua University), Long Chen (University of Macau, Tsinghua University), Longlong Wang (University of Macau, Tsinghua University), Liang Hu (University of Macau, Tsinghua University), Haochen Liu (University of Macau, Tsinghua University), Yuncheng Jiang (University of Macau, Tsinghua University), Hongwei Xie (University of Macau, Tsinghua University), Jieming Cui (University of Macau, Tsinghua University), Hanbing Li (University of Macau, Tsinghua University), Junjie Huang (University of Macau, Tsinghua University), Zhixiang Duan (University of Macau, Tsinghua University), Hexin Zhang (University of Macau, Tsinghua University), Weixiang Liang (University of Macau, Tsinghua University), Ying Gong (University of Macau, Tsinghua University), Yuhan Wang (University of Macau, Tsinghua University), Yong Pan (University of Macau, Tsinghua University), Pengna Li (University of Macau, Tsinghua University), Kangyi Wu (University of Macau, Tsinghua University), Ken Deng (University of Macau, Tsinghua University), Kun Ma (University of Macau, Tsinghua University), Fuxi Wen (University of Macau, Tsinghua University), Zhi-xin Yang (University of Macau, Tsinghua University), Hangjun Ye (University of Macau, Tsinghua University)
arXiv:2601. 20334v2 Announce Type: replace-cross Abstract: Robotic manipulation has increasingly adopted vision-language-action (VLA) models, which achieve strong performance but typically require task-specific demonstrations and fine-tuning, and often generalize poorly under domain shift.
By Brian Y. Tsui, Alan Y. Fang, Tiffany J. Hwu
arXiv:2607. 11119v1 Announce Type: cross Abstract: Robot manipulation is a complex task that requires visual understanding, physical reasoning, planning, and closed-loop control.
By Hengyuan Hu, Priya Sundaresan, Jensen Gao, Dorsa Sadigh
RoboCousin is an extensible simulation platform that transforms user-provided observations into reusable assets, scenes, and expert trajectories for bimanual robotic manipulation. It converts object images into simulation-ready models with visual, collision, semantic, and physical metadata, automatically generates grasp candidates, and builds digital cousins that vary objects, backgrounds, layouts, and language instructions while preserving task-relevant affordances. The platform supports both tabletop and room-level scene construction, and the authors release RoboCousin-OBD with over 3,000 annotated objects and 50 backgrounds, generating more than one million expert trajectories across 50 tasks, with simulation and real-robot experiments demonstrating comparable annotation quality and effective sim-to-real transfer.
By Jingxuan Zhu, Jingyi Li, LiangLiang Chen, Zhiyuan Jing, Jidong Zhang, Hongming Li
RAPID is a system that automatically generates, verifies, and refines robot programs from a single visual human demonstration. It infers a testable task specification, action primitives, and an interactive environment, using an object-centric relational program representation to enable reuse beyond the demonstration. The approach was evaluated in simulation on eight contact-rich manipulation tasks and successfully deployed on a real Franka arm, showing strong generalization across object pose, shape, material, and environment.
By Yuyao Liu, Jiayuan Mao, David Hsu, Leslie Pack Kaelbling, Tom\'as Lozano-P\'erez
World Action Agent (WAA) is a multi‑agent framework that lets vision‑language models (VLMs) directly pilot robots by operating within a visual action workspace. The workspace provides automatically selected contact views, editable action rehearsals, and in‑view correction to refine decisions before low‑level execution. WAA learns procedural skills from expert videos and human teaching, and its interaction traces can train smaller VLMs, achieving state‑of‑the‑art success on LIBERO‑Pro and improving out‑of‑domain performance on robosuite and Qwen3.5‑9B.
By Yehang Zhang, Haojian Huang, Yifan Chang, Jianchong Su, Bohan Zhou, Yingjie Xu, Wosong Chen, Tianhao Zhou, Chenxu Wang, Tianyi Zhang, Yangkai Wei, Wenqian Li, Shiyuan Deng, Yinchuan Li, Ying-Cong Chen, Zexi Li
The paper introduces a closed‑loop framework to evaluate frontier AI models on the LIBERO‑10 robot manipulation benchmark, allowing models to control a robot via visual inputs, proprioception, and tool calls without task‑specific fine‑tuning. Six models (GPT‑6 Astra, GPT‑6.1 Sol, GPT‑6 Luna, Claude Opus 5, Claude Opus 5.5, Gemini‑4 Argon) were tested across 18 configurations and 1,800 episodes, with GPT‑6 Astra achieving the highest success rate of 44%. Results show that higher reasoning effort generally improves performance, yet benefits vary by model and task, and many models struggle with precise spatial arrangements and complex multi‑step interactions.
By May Espinola, Qifan Zhang, Yu Xiang