Search is not available for this dataset
action listlengths 7 7 | observation.state listlengths 7 7 | timestamp float32 0 89 | frame_index int64 0 2.67k | episode_index int64 0 499 | index int64 0 490k | task_index int64 0 139 |
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End of preview. Expand in Data Studio
vla_kinova_gen3_clutter_pick_place
A LeRobot dataset of tabletop clutter pick-and-place teleoperation on a Kinova Gen3 (6-DoF) arm with a Robotiq 2F-85 gripper, recorded for fine-tuning vision-language-action (VLA) policies.
Dataset Goal
The goal is a language-conditioned manipulation policy that (a) grounds varied
natural-language instructions to the correct object and destination in cluttered
scenes, and (b) performs the pick-and-place and contact-rich behaviours that a
VLM + classical-planner stack cannot do. Prompts follow a
"<verb> <object reference> and <verb> it <destination reference>" template and are
designed to span a controlled range of grounding difficulty and manipulation
difficulty.
Dataset Design
Each episode is tagged along four axes, deliberately spread across the 500 episodes:
- Object reference — O1 direct/named (45%), O2 visual property e.g. colour/size (31%), O3 spatial e.g. leftmost/behind the cup (18%), O4 functional/implicit e.g. something to write with (6%).
- Destination reference — D1 direct-named (72%), D2 shape contrast round vs rectangular (6%), D3 spatial container left/right/closer to you (10%), D4 relational under/on/over another object (11%).
- Scene density — sparse (54%) / medium (32%) / dense (14%) objects on the table.
- Access — open (55%) vs blocking (45%), where blocking requires a physically-intelligent step (clearing, nudging, flipping an upside-down cup, uncovering) before the grasp.
A dedicated physical-intelligence portion adds deformables (towel, bag, cable, rope ≈ 18% of episodes), occlusion (target hidden under a cloth), and cover/drape/stack relations. Verbs are rotated (pick / grab / take / get + put / place / set).
Composition
| Episodes | 500 |
| Frames (timesteps) | 490147 |
| Distinct tasks | 140 |
| FPS | 30 |
| State / action | 7-DoF (6 arm joints + gripper), joint positions |
| Cameras | observation.images.external (480×640), observation.images.wrist (240×320) |
Roughly 50+ distinct target objects span writing implements, cubes/blocks/cylinders, tools (screwdrivers, pliers, fork, scissors), tapes, glue sticks, cables, spray cans, and deformables; destinations are bowl, cup, pen-holder, plate, lunchbox, and the round / rectangular / left / right / "closer to you" containers.
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