Dataset Structure The dataset is provided in a unified HDF5 format. Wrench (Force/Torque): 3-axis force, 3-axis torque sampled. Robot State: 6-DOF joint positions, velocities and efforts. End-Effector Pose: 6-DOF Cartesian poses (position + quaternion). Vision: RGB and Depth images. File Hierarchy: /wrench/data: [N, 6] array of forces and torques /wrench/timestamps: [N] array of aligned timestamps /pose/data: [M, 7] array of cartesian poses /pose/timestamps [M] array of aligned timestamps /joint_states/data: [M, 3, 6] array of joint states /joint_states/timestamps [M] array of aligned timestamps /rgb/data: [K, H, W, 3] rgb image stream /rgb/timestamps: [K] array of aligned timestamps /depth/data: [K, H, W, 3] depth image stream /depth/timestamps: [K] array of aligned timestamps Folder Organization Dataset contains five trial folders. Each trial folder consists of subfolders representing the assembly subtasks. In each subtask folder, the data of the executed movement and assembly primitives are saved as .h5 files: trial_XY/subtaskname/timestamp_asm_subtaskname_primitivename.h5 Where trial_XY can be one of [trial_0, trial_1, trial_2, trial_3, trial_4]. Use Cases Sim-to-Real Transfer: Comparing force profiles of simulated assembly against physical reality. Imitation Learning: Training policies using the provided joint states and RGB observations. Anomaly Detection: Developing classifiers to detect assembly failures based on force-torque transients. Keywords Robotics, Manipulation, Benchmarking, MuJoCo, HDF5, Sim-to-Real.