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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}/
β”œβ”€β”€ data/chunk-000/
β”‚   β”œβ”€β”€ episode_000000.parquet
β”‚   β”œβ”€β”€ episode_000001.parquet
β”‚   └── ...
β”œβ”€β”€ videos/chunk-000/
β”‚   β”œβ”€β”€ observation.images.image_0/  (table camera)
β”‚   └── observation.images.image_1/  (wrist camera)
└── meta/
    β”œβ”€β”€ info.json
    β”œβ”€β”€ episodes.jsonl
    β”œβ”€β”€ tasks.jsonl
    └── 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 repo
    • peg_insert_rl_games.pth β€” PegInsert (rl_games, reward 381)
    • nut_thread_rl_games.pth β€” NutThread (rl_games, reward 952)
    • drawer_rsl_rl.pt β€” Drawer (RSL-RL, model_4999)
  • Environment: environment_info.json and pip_freeze.txt in 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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