RubetekRobotics/UR10e-LinearGripper-Jig-SimDist-Dataset

UR10e Linear Gripper — Jig / Bottom Enclosure — SimDist Stage-2 Dataset

Action-conditioned simulation rollouts for pretraining a latent world model, generated with the Simulation Distillation (SimDist) stage-2 procedure (arXiv:2603.15759, RSS 2026; code CLeARoboticsLab/simdist, MIT).

Status: placeholder — generation not yet run. This card describes the dataset that will land here.

Task

Insertion of JigV2 (insertive) into BottomEnclosure (receptive), on a UR10e with a custom linear two-jaw gripper, simulated in IsaacLab. 10 Hz control, 16 s episodes (160 steps), 6-D relative end-effector pose (Cartesian OSC) plus a binary gripper command.

How it was generated

SimDist Algorithm 2. Per environment, a diagonal action-noise covariance is sampled; on each reset the environment is reassigned to the expert policy with probability 0.5, else to a uniformly drawn sub-optimal checkpoint. Gaussian action perturbations are injected in contiguous bursts of U[1,5] steps interleaved with clean stretches of U[5,10] steps (the paper's manipulation intervals). The binary gripper dimension is perturbed by flipping the commanded open/close rather than by additive noise, since additive noise on a thresholded signal has no controllable effect.

The point of the perturbations is coverage: a planner searches far outside the expert distribution, so the dataset must contain mistakes, recoveries and failures, not just clean expert trajectories. The paper's own ablation shows expert-only data collapses success from 0.90 to 0.10.

Expert policy, checkpoint ladder and value function come from UR10e-LinearGripper-Jig-BottomEnclosure-Stage1; reset states from UR10e-LinearGripper-Jig-BottomEnclosure-Resets.

Contents

One HDF5 file per shard. All arrays share a leading timestep axis.

Field	Type	Notes
proprio	float32 (N, 20)	last gripper action (1), last arm action (6), arm joint positions (6), end-effector pose (6), binary contact (1)
front_rgb, side_rgb, wrist_rgb	vlen uint8 (N,)	JPEG-encoded, quality 90
actions	float32 (N, 7)	raw policy output, before the environment's action scale
rewards	float32 (N,)
values	float32 (N,)	V(s) from the expert critic, always the final checkpoint
expert_flags	bool (N,)	true only when on the expert and the action was uncorrupted
env_ids, episode_ids	int32 (N,)	episode boundary markers

Observations are deliberately non-privileged. No ground-truth object pose appears anywhere in the observation fields — a world-model encoder that could read object pose directly would defeat the entire premise. Privileged simulator state is used only for the reward and the critic.

Images are stored JPEG-encoded rather than raw: raw uint8 is 451,584 B/step against ~30,714 B/step encoded at 224 px, and a float32 representation would be 1.8 MB/step.

Known limitations

• Dataset scale is the axis the method is most sensitive to (paper Table I: 0.90 at full scale, 0.72 at 50 %, 0.06 at 10 %). The realised scale here is recorded below once generation completes.
• The expert was trained against a reset mixture in which ObjectPartiallyAssembledEEGrasped was only ~33 % genuinely holding the object, due to a recorder bug since fixed. That cannot be undone and is documented rather than hidden.

Licence

MIT, following upstream SimDist.