---
license: mit
task_categories:
- robotics
- reinforcement-learning
language:
- en
pretty_name: PhysProbe Dynamics Probing Dataset
size_categories:
- 10K<n<100K
tags:
- robotics
- isaac-lab
- physics
- probing
- manipulation
- franka
---

# PhysProbe Dynamics Probing Dataset

Manipulation episodes from Isaac Lab collected for probing physics understanding in video world models (V-JEPA 2, VideoMAE, DINOv2). Each episode includes dual-camera RGB (384×384), robot state, scripted/RL actions, and per-timestep physics ground truth (contact forces, object kinematics, physics randomization parameters).

## Tasks

| Task | Episodes | Policy | Physics Randomization |
|------|---------:|--------|------------------------|
| Push | 1,500 | Scripted (random direction, no target — Step 0) | mass, obj_friction, surface_friction |
| Strike | 3,000 | Scripted (random direction, no target — Step 0) | mass, friction, surface_friction, restitution |
| Reach | 600 | Scripted | None (negative control) |
| Drawer | 2,000 | RL (RSL_RL) | drawer_joint_damping |
| PegInsert | 2,500 | Scripted (Factory) | held_friction, fixed_friction, held_mass |
| NutThread | 2,500 | Scripted (Factory) | held_friction, fixed_friction, held_mass |
| **Total** | **12,100** | | |

## Format

LeRobot V2 layout. Per-task:
```
<task>/
  data/chunk-000/episode_NNNNNN.parquet
  videos/chunk-000/observation.images.image_0/episode_NNNNNN.mp4   # table_cam
  videos/chunk-000/observation.images.image_1/episode_NNNNNN.mp4   # wrist_cam
  meta/info.json
  meta/episodes.jsonl
  meta/tasks.jsonl
  meta/stats.json
  meta/modality.json
```

Per-episode parquet columns:
- `observation.state` (8D): 7 joint positions + 1 gripper
- `action` (3–8D, task-dependent)
- `next.reward`, `next.done`
- `physics_gt.*` (task-specific — see below)
- Frame index, timestep, episode index metadata

## Physics Ground Truth (`physics_gt.*`)

### Common across all tasks
- `ee_position` (3), `ee_orientation` (4), `ee_velocity` (3), `ee_angular_velocity` (3) — end-effector kinematics
- `joint_pos` (7), `joint_vel` (7) — arm joint state
- `phase` (1) — task phase label (task-dependent enum; 7 = idle)

### Contact fields (per task)

**Push, Strike, Reach:**
- `contact_flag` (1), `contact_force` (3), `contact_point` (3) — aggregate ee↔object + object↔surface
- `contact_finger_l_object_flag/force`, `contact_finger_r_object_flag/force` — per-finger ee↔object (new in 2026-04-23)
- `contact_object_surface_flag/force` — object↔surface (new in 2026-04-23)

**PegInsert, NutThread:**
- `contact_flag` (1), `contact_force` (3), `contact_point` (3) — aggregate
- `contact_finger_l_peg_flag/force`, `contact_finger_r_peg_flag/force` (PegInsert) — finger↔peg
- `contact_finger_l_nut_flag/force`, `contact_finger_r_nut_flag/force` (NutThread) — finger↔nut
- `contact_peg_socket_flag/force` (PegInsert) — peg↔hole (reconstructed as residual from peg total contact minus finger reactions; direct pair filter unsupported in Factory direct env)
- `contact_nut_bolt_flag/force` (NutThread) — nut↔bolt (direct exact-pair sensor)

**Drawer:**
- `handle_position` (3), `handle_velocity` (3)
- `drawer_joint_pos` (1), `drawer_joint_vel` (1)

### Task-specific kinematics

**Push/Strike/Reach (Step 0):**
- `object_position` (3), `object_orientation` (4), `object_velocity` (3), `object_angular_velocity` (3)
- `target_position` (3) — placeholder `[0, 0, 0]` (no target in Step 0)

**PegInsert/NutThread:**
- `held_position` (3), `held_orientation` (4), `held_velocity` (3), `held_angular_velocity` (3) — peg/nut kinematics
- `fixed_position` (3), `fixed_orientation` (4) — hole/bolt pose
- `insertion_depth` (1) — peg_insert only

### Physics randomization parameters (per episode)

Stored in episode metadata (`physics_gt.*_{static,dynamic}_friction`, `*_mass`, etc.) — see per-task schema above for exact fields.

## 2026-04-23 Recollection Note

The previous version of this dataset (before 2026-04-23) had a data-collection bug: contact forces were zero-filled across all tasks because the sensor configuration did not use the proper body filters / the Factory direct env does not support `get_net_contact_forces` on `ArticulationView`. This version fixes the following:

1. **Push, Strike, Drawer, Reach**: Per-pair `ContactSensor` with `filter_prim_paths_expr` on finger/object bodies → real nonzero contact forces.
2. **NutThread**: Direct exact-pair sensor (`contact_nut_bolt_*`) → direct nut↔bolt force.
3. **PegInsert**: GPU pair filtering on hole is unsupported in direct Factory env. Peg↔socket contact is reconstructed as a **residual**: `F_peg_socket = F_peg_total - F_finger_l_peg - F_finger_r_peg` (Newton's 3rd law). This is sparser and noisier than a direct sensor; finger-grip force dominates and is subtracted, so pay attention when using `contact_peg_socket_*` for downstream probing.
4. **Phase label**: Now correctly tracks scripted policy state transitions (previously always 7/idle for RL policies).

### Known caveats (unchanged from previous release)

- Push/Strike are Step 0: no target, `success=True` for all, `target_position=[0,0,0]`.
- Drawer randomizes only `drawer_joint_damping` (Isaac Lab env limitation — handle friction/mass are fixed).
- Factory tasks (PegInsert, NutThread): collection uses scripted policy; success rate is low for unguided inserts.

## Collection Pipeline

- Isaac Sim 4.5 / Isaac Lab v2.2.1
- Scripted oracle policies + optional RL checkpoints (`rl_games` for Factory, `rsl_rl` for Drawer)
- Dual camera rendering at 384×384, 15 fps
- `num_envs` per task: 8 (Factory), 16 (others) parallel
- Hardware: 4× NVIDIA A6000 48 GB

Collection script: [Leesangoh/PhysREPA_Tasks](https://github.com/Leesangoh/PhysREPA_Tasks) (`archive_data_collection/collect_sample_data.py`).

## Intended use

This dataset is designed for **probing physics understanding** in pretrained video encoders:
- Linear probes from mean-pooled features onto `physics_gt.*` targets
- Temporal-aligned window-level probing (per-window features → per-window targets)
- Do NOT episode-mean aggregate features/targets for kinematic probing — that collapses temporal structure and produces misleading results.

## Citation

TBD (paper in preparation).