leesangoh/gr1-tabletop-augmented

GR-1 Tabletop, Augmented

Counterfactual action perturbations for the NVIDIA GR-1 tabletop manipulation dataset, with joint torque, fingertip force, and binary contact recorded alongside — and, for every perturbed rollout, the rendered future the perturbed action actually produces.

The source dataset gives you what the robot did. This one also gives you what would have happened had it done something slightly different, and what the body felt while doing it.

What was added

	Source (NVIDIA GR-1 Teleop)	This dataset
Trajectories	teleoperated, nominal only	+ 4 perturbation strengths per anchor
Proprioception	joint positions	+ velocity, actuator torque, fingertip force, contact
Analytic baseline	—	robot-only MuJoCo rollout (no objects, no contact)
Future frames	recorded video only	rendered ego-view for every perturbed rollout

The analytic baseline matters: subtracting it turns the learning problem from "predict the whole arm trajectory" into "predict what contact adds", which is where the residual actually lives. Measured on 767 anchors, the analytic rollout explains 0.97–0.99 of the variance in free space and −0.97 to −2.63 during contact — it is exact until something is touched.

Contents

data/perturbed/<task>/episode_XXXXXX.npz     counterfactual rollouts + rendered frames
data/tactile_torque/                          per-demo torque and fingertip force
data/windows/                                 pre-cut training windows (history 8, horizon 16)
manifest_sha256.txt                           SHA-256 of every episode file

Every published file passed a full verification pass before upload: zip CRC over all bytes, exact key set, shape and dtype per array, all values finite, categories within {0,1,2}, contact binary and equal to force > 0.1, a frame offset table that starts at zero, increases, and ends exactly at the JPEG buffer length — and a decode of the first, middle, and last frame of each file.

Episode file schema

Each .npz holds every rollout for one source episode. R is the rollout count (typically 93–97: ~24 anchors × 4 strengths, plus one unperturbed control per 24).

key	shape	dtype	meaning
anchors	(R,)	int64	source frame index the rollout starts from
strength	(R,)	float64	perturbation magnitude — 0, 1, 2, 4, 7
categories	(R,)	int64	0 free · 1 pre-contact · 2 active contact
self_history	(R, 8, 127)	float32	q(39) · q̇(39) · actuator torque(39) · fingertip force(10)
action	(R, 16, 29)	float32	l_arm 7 · l_hand 6 · r_arm 7 · r_hand 6 · waist 3
known_self	(R, 16, 135)	float32	analytic q(39) · q̇(39) · τ(39) · EEF pose(18)
delta_q	(R, 16, 39)	float32	real − analytic, joint position
delta_qdot	(R, 16, 39)	float32	real − analytic, joint velocity
delta_tau	(R, 16, 39)	float32	real − analytic, joint torque
force	(R, 16, 10)	float32	per-fingertip external force
contact	(R, 16, 10)	float32	binary contact, force > 0.1
frames_jpeg	(N,)	uint8	concatenated JPEG bytes, quality 90
frames_offset	(R·9+1,)	int64	byte offsets into frames_jpeg
frames_per_rollout	()	int64	9 — one conditioning frame + 8 future
render_every	()	int64	2 — frames are every other 20 Hz step
camera	()	str	egoview

Frames are 256×256 RGB, rendered with the same crop-and-resize the source pipeline uses, so they are directly comparable to the recorded video.

Reading a rollout

import io, numpy as np
from PIL import Image

d = np.load("episode_000003.npz", allow_pickle=False)
r = 0                                              # rollout index
npr = int(d["frames_per_rollout"])                 # 9
off = d["frames_offset"]
frames = [
    np.array(Image.open(io.BytesIO(
        d["frames_jpeg"][off[r*npr + i]: off[r*npr + i + 1]].tobytes())))
    for i in range(npr)
]                                                  # frames[0] conditions, [1:] are the future
residual = d["delta_q"][r]                         # (16, 39) what contact added
torque   = d["delta_tau"][r]                       # (16, 39)
touched  = d["contact"][r] > 0.5                   # (16, 10)

strength == 0 rollouts are unperturbed controls. They exist so the renderer can be checked against the recorded video: if a strength-0 render does not match the source frame, the two pipelines disagree and every other frame is suspect.

Joint order

39 actuated DoF, in the order used by every array above:

waist 3            yaw, pitch, roll
right arm 7        shoulder pitch/roll/yaw, elbow pitch, wrist yaw/roll/pitch
right hand 11      thumb 3, index 2, middle 2, ring 2, pinky 2
left arm 7         (same as right)
left hand 11       (same as right)

Fingertip force and contact are 10-dimensional: 5 fingers × 2 hands.

Note that the action vector uses a different grouping (l_arm, l_hand, r_arm, r_hand, waist) than the joint vector. They are not interchangeable without a permutation.

How the perturbations were made

Each anchor is replayed from the recorded state with the action sequence displaced along a random direction, scaled to the listed strength, and stepped through the same low-level controllers the source used. Physics and rendering come from one pass, so the frames and the proprioception describe the same rollout rather than two runs that happened to agree.

Directions are freshly drawn and the generation seed is not stored in the files, so the released set cannot be regenerated from the source trajectories alone. What fixes the identity of this dataset is manifest_sha256.txt at the repository root: one SHA-256 per episode file, covering every file published here. Verify with

sha256sum -c manifest_sha256.txt

Generation ran across several machines. Where two of them independently produced the same episode, one copy was kept; each is an independently valid perturbation of the same source trajectory, and the manifest records which one was published.

Provenance and license

Derived from nvidia/PhysicalAI-Robotics-GR00T-Teleop-Sim (NVIDIA GEAR), licensed CC BY-NC 4.0. Rendering uses robocasa-gr1-tabletop-tasks (MIT) and robosuite (MIT); the 3D assets appearing in rendered frames carry their own licenses (Lightwheel CC BY 4.0; Objaverse — mixed CC, including CC BY-NC-SA; Sketchfab — mixed).

This dataset is released under CC BY-NC-SA 4.0 — the most restrictive term inherited from its inputs. It is non-commercial, and adaptations must be shared alike.

Changes from the source: action perturbation and replay, analytic robot-only rollout, extraction of torque/fingertip force/contact, ego-view rendering of perturbed futures, and window pre-cutting. The source trajectories themselves are unmodified.

Citation

Please cite the source dataset alongside this one:

@misc{nvidia_gr1_teleop_sim,
  title  = {PhysicalAI-Robotics-GR00T-Teleop-Sim},
  author = {{NVIDIA GEAR}},
  year   = {2025},
  howpublished = {\url{https://huggingface.co/datasets/nvidia/PhysicalAI-Robotics-GR00T-Teleop-Sim}},
  note   = {CC BY-NC 4.0}
}