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:2610.18835v2 Announce Type: cross Abstract: It has been difficult to accurately model the physics of the interaction between a robot and the objects it manipulates, including contact mechanics,...
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...
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.
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.
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...
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...
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.
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.
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...
ActiveAlign is a framework that uses active perception to reduce the Real2Sim2Real gap in robot manipulation. It introduces two modules: ActiveAlign-Waving, which performs hand‑eye calibration through active hand motions and differentiable rendering, and ActiveAlign-Touching, which refines object alignment with tactile feedback to achieve millimeter‑level precision. The approach is demonstrated by curating simulation environments for uncalibrated datasets and enabling a Unitree G1 robot to manipulate millimeter‑scale objects such as buttons and sliders.
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...
arXiv:2509.12151v3 Announce Type: replace-cross Abstract: We present a learnable physics-based model that predicts motion of the robot end effector and reaction force-torque in contact-rich manipulat...