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Dynamics Probing Benchmark Dataset
Physics-varied manipulation episodes for probing whether video encoders encode contact dynamics in their intermediate representations.
Overview
This dataset contains 12,100 episodes across 6 manipulation tasks in Isaac Lab simulation, with per-episode physics randomization (friction, mass, damping, restitution). Each episode includes dual-camera RGB video (384x384), robot state/action trajectories, and dense physics ground truth annotations.
Purpose: Dynamics probing β train linear probes on frozen video encoder features to predict physics quantities (contact force, friction coefficient, object velocity, etc.) and identify which layers encode contact dynamics.
Tasks
| Task | Episodes | Policy | Physics Params | Description |
|---|---|---|---|---|
| Push | 1,500 | Scripted (Step0) | mass, obj friction, surface friction | Franka pushes cube on table, random direction |
| Strike | 3,000 | Scripted (Step0) | mass, obj friction, surface friction, damping, restitution | Franka strikes ball, random direction |
| PegInsert | 2,500 | RL (rl_games) | held friction, fixed friction, held mass | Peg-in-hole insertion with pre-grasped peg |
| NutThread | 2,500 | RL (rl_games) | held friction, fixed friction, held mass | Nut threading onto bolt with pre-grasped nut |
| Drawer | 2,000 | RL (RSL-RL) | handle friction, gripper friction, drawer damping | Open cabinet drawer |
| Reach | 600 | Scripted (Oracle) | None (negative control) | Move EE to random target position |
Data Format (LeRobot V2)
Each task directory contains:
{task}/
βββ data/chunk-000/
β βββ episode_000000.parquet
β βββ episode_000001.parquet
β βββ ...
βββ videos/chunk-000/
β βββ observation.images.image_0/ (table camera)
β βββ observation.images.image_1/ (wrist camera)
βββ meta/
βββ info.json
βββ episodes.jsonl
βββ tasks.jsonl
βββ stats.json
Per-frame columns (parquet)
| Column | Shape | Description |
|---|---|---|
observation.state |
(8,) | EE pose [x,y,z,r,p,y,pad,gripper] |
action |
(7,) | IK delta [dx,dy,dz,dr,dp,dy,gripper] |
observation.images.image_0 |
video | Table camera RGB 384x384 |
observation.images.image_1 |
video | Wrist camera RGB 384x384 |
Physics Ground Truth (per-frame, task-specific)
Common (all tasks):
physics_gt.ee_position(3),ee_orientation(4),ee_velocity(3),ee_angular_velocity(3),ee_acceleration(3)
Push/Strike:
physics_gt.object_position(3),object_velocity(3),object_angular_velocity(3),object_acceleration(3)physics_gt.contact_finger_l_object_flag(1),contact_finger_l_object_force(3)physics_gt.contact_object_surface_flag(1),contact_object_surface_force(3)physics_gt.object_to_target_distance(1),object_on_surface(1)- Strike only:
physics_gt.ball_planar_travel_distance(1)
PegInsert:
physics_gt.peg_position(3),peg_orientation(4),peg_velocity(3),peg_angular_velocity(3)physics_gt.hole_position(3)physics_gt.insertion_depth(1),peg_hole_lateral_error(1)- Pair contacts:
contact_finger_l_peg_*,contact_finger_r_peg_*,contact_peg_socket_*
NutThread:
physics_gt.nut_position(3),nut_orientation(4),nut_velocity(3),nut_angular_velocity(3)physics_gt.bolt_position(3),bolt_orientation(4)physics_gt.axial_progress(1),nut_bolt_relative_angle(1)- Pair contacts:
contact_finger_l_nut_*,contact_finger_r_nut_*,contact_nut_bolt_*
Drawer:
physics_gt.drawer_joint_pos(1),drawer_joint_vel(1)physics_gt.handle_position(3),handle_velocity(3)physics_gt.drawer_opening_extent(1)- Pair contacts:
contact_finger_l_handle_*,contact_finger_r_handle_*
Reach (negative control):
physics_gt.target_position(3),ee_to_target_distance(1)
Episode Metadata (episodes.jsonl)
Each episode records randomized physics parameters:
- Push/Strike:
object_0_mass,object_0_static_friction,surface_static_friction - PegInsert:
peg_static_friction,peg_mass,hole_static_friction - NutThread:
nut_static_friction,nut_mass,bolt_static_friction - Drawer:
drawer_joint_damping,handle_static_friction
Environment
- Simulator: Isaac Lab v2.2.1 on Isaac Sim 4.5.0
- Robot: Franka Panda (all tasks)
- Control: Differential IK (position-only for Push/Strike, pose for others)
- Cameras: 384x384 RGB, table_cam (third-person) + wrist_cam
- Physics: PhysX GPU, dt=0.01s, decimation=2 (50Hz control)
Design Rationale
Why physics randomization? If a video encoder encodes physics in its intermediate layers, linear probes trained on episodes with varied friction/mass should predict these quantities from frozen features. Tasks without physics variation (Reach) serve as negative controls.
Episode count rationale: Proportional to (param space dim) Γ (dynamics complexity) / (informative frame density). Strike has 3,000 episodes (most) due to 5D param space and sparse impact events (~25% informative frames).
Reproducibility
- Code: PhysREPA_Tasks (commit
2808943) - RL Checkpoints: Included in
checkpoints/directory of this repopeg_insert_rl_games.pthβ PegInsert (rl_games, reward 381)nut_thread_rl_games.pthβ NutThread (rl_games, reward 952)drawer_rsl_rl.ptβ Drawer (RSL-RL, model_4999)
- Environment:
environment_info.jsonandpip_freeze.txtin this repo - Platform: Isaac Sim 4.5.0, Isaac Lab v2.2.1, Python 3.10, CUDA 12.8, 4x NVIDIA A6000
Reproduce collection
# Clone code
git clone https://github.com/Leesangoh/PhysREPA_Tasks
cd PhysREPA_Tasks
# Push (scripted, 16 parallel envs)
/isaac-sim/python.sh collect_sample_data.py --task push --num_episodes 1500 --num_envs 16 --step0 --output_dir <OUT> --headless
# PegInsert (RL, 200 parallel envs)
/isaac-sim/python.sh collect_sample_data.py --task peg_insert --num_episodes 2500 --num_envs 200 --filter_success --rl_checkpoint checkpoints/peg_insert_rl_games.pth --output_dir <OUT> --headless
# Drawer (RL, 32 parallel envs)
/isaac-sim/python.sh collect_sample_data.py --task drawer --num_episodes 2000 --num_envs 32 --filter_success --rl_checkpoint checkpoints/drawer_rsl_rl.pt --output_dir <OUT> --headless
License
MIT
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