RoboSimStudio (RSS) is an interactive toolkit designed to simplify the creation of realistic robot manipulation simulations. It allows users to explicitly choose and edit solvers, contact models, and physical parameters, enabling iterative refinement of simulations based on observed behavior. Experiments demonstrate human-guided real-to-sim approaches using visual evaluation, force-feedback, and agentic real-to-sim-to-real methods.
arXiv:2609.37089v1 Announce Type: new
Abstract: Real-world videos provide rich demonstrations of manipulation, but turning them into reusable robot skills requires visually aligned environments, exec...
By Kerui Ren, Yingxiang Xu, Kaiwen Song, Linning Xu, Bo Dai, Mulin Yu, Tao Lu
arXiv:2609.24660v2 Announce Type: cross
Abstract: Human demonstrations offer a scalable way to collect manipulation data, but their contacts may be unstable or infeasible when transferred to a robot...
By Shengcheng Luo, Xiaoyang Cheng, Hong Ying, Xiaoying Zhou, Jiaming Jiang, Haoran Guo, Wanlin Li, Ziyuan Jiao, Chenxi Xiao
arXiv:2607. 00033v1 Announce Type: cross Abstract: Dexterous robot manipulation can benefit from the abundance of human demonstrations, but transferring such demonstrations to robot policies remains challenging.
By Xinghao Zhu, Zixi Liu, Shalin Jain, Chenran Li, Milad Noori, Huihua Zhao, John Welsh, Michael Andres Lin, Wei Liu, Tingwu Wang, Xingye Da, Zhengyi Luo, Vishal Kulkarni, Naema Bhatti, Yuke Zhu, Linxi Fan, Bowen Wen, Danfei Xu, Soha Pouya, Yan Chang
arXiv:2602. 02402v2 Announce Type: replace-cross Abstract: Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and robot actions.
By Mu Huang, Hui Wang, Kerui Ren, Linning Xu, Yunsong Zhou, Mulin Yu, Bo Dai, Jiangmiao Pang
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
arXiv:2609.38982v1 Announce Type: cross
Abstract: Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital w...
By Jiaheng Hu, Roberto Martin-Martin, Peter Stone, Rocky Duan, Zhenyu Jiang, Guanya Shi
arXiv:2602.02269v2 Announce Type: replace-cross
Abstract: We present $multipanda\_ros2$, a novel open-source ROS2 architecture for multi-robot control of Franka Robotics robots. Leveraging ros2 contr...
By Jon \v{S}kerlj, Seongjin Bien, Abdeldjallil Naceri, Sami Haddadin
Coding agents powered by large language models (LLMs) have shown remarkable abilities to autonomously reason about and achieve goals in the digital world. However, bringing this success to the physica...
arXiv:2505. 01458v2 Announce Type: replace-cross Abstract: Navigation and manipulation are core capabilities in Embodied AI, but training agents to perform them directly in the real world is costly, time-consuming, and unsafe.
By Lik Hang Kenny Wong, Xueyang Kang, Kaixin Bai, Jianwei Zhang
arXiv:2610.17161v1 Announce Type: cross
Abstract: Teleoperation is widely used to collect high-quality demonstrations for robot learning. Dexterous manipulation, however, requires coordinated control...
By Ying Yuan, David Held, Jitendra Malik, Haozhi Qi
ActiveAlign is a framework that uses active perception to close the Real2Sim2Real gap in robot manipulation. It introduces two modules: ActiveAlign‑Waving, which performs hand‑eye calibration via active hand motions and differentiable rendering, and ActiveAlign‑Touching, which refines object alignment with tactile feedback for millimeter‑level precision. The approach enables strong real‑to‑simulation alignment, allowing simulation environments to be curated from uncalibrated datasets and enabling a real Unitree G1 robot to manipulate millimeter‑scale objects such as buttons and sliders.