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radeonvla_reflex_physical_1k

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metadata 
license: cc-by-4.0
task_categories:
  - robotics
tags:
  - lerobot
  - robotics
  - imitation-learning
  - vision-language-action
  - amd-rocm
  - genesis

RadeonVLA-Reflex Physical-1K Dataset Card

Overview

  • Dataset name: RadeonVLA-Reflex Physical-1K
  • Version: physical-1k-1000
  • Generator commit: dbc12b69e88be787b679bb41cf2026ca43e0eda6
  • Genesis version: 1.1.2
  • LeRobot version: 0.6.0
  • License: CC BY 4.0
  • Public URL: https://huggingface.co/datasets/a3124371940/radeonvla_reflex_physical_1k
  • SHA256 (manifest+meta): 5a801c07ac0cee40a9bbd2ee0d5598a8c94a89c3b95e6ec68ab753b5c70246ed

Task coverage

The primary L1 dataset has 20 variations: five fruits × four bowl positions.

Fruit Bowl positions Training target Validation Held-out evaluation
banana white-left, blue-left, white-right, blue-right 50 each 5 each 10 each
lemon white-left, blue-left, white-right, blue-right 50 each 5 each 10 each
plum white-left, blue-left, white-right, blue-right 50 each 5 each 10 each
apple white-left, blue-left, white-right, blue-right 50 each 5 each 10 each
orange white-left, blue-left, white-right, blue-right 50 each 5 each 10 each

The release table will replace planned counts with the immutable dataset manifest. L2–L4 data are reported separately and are not implied by the primary L1 total.

Frame schema

Field Shape/type Description
observation.images.world (240, 320, 3) uint8 World RGB camera
observation.images.wrist (240, 320, 3) uint8 Wrist RGB camera
observation.state (9,) float32 Ordered robot and gripper qpos
action (9,) float32 Absolute joint-position target
task string Natural-language instruction
timestamp float Episode time (dataset metadata)
episode_index integer Episode identifier
frame_index integer Frame within episode
seed integer Reset seed in the external per-episode certificate
success boolean Strict judgement in the external per-episode certificate

seed and success are not tensor columns in the LeRobot frame schema. They live in certificates/episode_XXXXXX.json, one atomic certificate for each committed episode.

Action protocol

  • Action type: absolute_joint_position
  • Joint names and order: panda_joint1..7, panda_finger_joint1..2
  • Dimension: 9
  • Joint unit: radians (arm), meters (fingers)
  • Gripper convention and range: [0.0, 0.04], open=0.04, closed=0.0
  • Control frequency: 20 Hz dataset (sim 100 Hz, decimated)

Data generation

I collect data with the scripted multi-goal expert (python -m radeonvla.record_dataset):

  1. expert states follow resolved L1–L4 goals after scene randomization;
  2. success requires every fruit center to finish inside the inner bowl footprint after at least 60 simulation settle steps;
  3. pose jitter is non-overlapping; optional appearance/physics DR flags are supported;
  4. recording rate is 20 Hz (sim 100 Hz, decimated);
  5. formal collection disables kinematic attachment, placement nudges, and off-table respawns; a context guard aborts any rigid-body pose write during the episode;
  6. failed episodes are discarded and are not part of Physical-1K;
  7. validate_dataset checks schema, non-finite values, image statistics, exact 20×50 coverage, unique seeds, zero interventions, and certificate/episode correspondence;
  8. I spot-check camera videos under datasets/*/videos/ before training;
  9. Recording happens under .inprogress; the target path is replaced only after finalize, coverage checks, and a successful LeRobot reopen.
  10. --resume-incomplete reconstructs saved counts from LeRobot metadata and reconciles the two-phase episode certificates before appending with a fresh seed.

Split policy

  • Strict smoke seeds: 12000–12999
  • Training seeds: 20000–29999
  • Validation seeds: 40000–40999
  • Formal evaluation seeds: 50000–59999
  • Interruption/recovery seeds: 60000–60999

No seed may occur in more than one split.

Quality checks

  • no black or corrupt images;
  • state and action match the frozen schema;
  • no NaN or infinity;
  • task, object, and container agree;
  • episode success is independently verified;
  • random replay videos were manually inspected.

Assets and limitations

Robot and YCB meshes are populated via setup_assets (see assets/README.md and THIRD_PARTY_NOTICES.md). The YCB data portal publishes the object models under CC BY 4.0; this release preserves that license, credits the YCB authors, and notes that blue bowl appearance is applied at scene build time. The Franka MJCF bundled by Genesis carries Apache-2.0 terms.

This dataset is simulation-only; object and language coverage are limited to the registered fruit/bowl suite. Final episode/frame counts and immutable revision replace the measured episode/frame fields after the 1,000-episode validator passed.

YCB attribution

Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, and Aaron M. Dollar, “The YCB Object and Model Set: Towards Common Benchmarks for Manipulation Research,” ICAR 2015. Source: https://www.ycbbenchmarks.com/ and https://ycb-benchmarks.s3-website-us-east-1.amazonaws.com/.