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random-sequence/flock-robotics-vla-training-v2

FLock Robotics VLA Training Dataset v2 Expert demonstrations for the FLock Robotics VLA competition task. All trajectories are successful. Quick start from datasets import load_dataset ds = load_dataset("random-sequence/flock-robotics-vla-training-v2") print(ds["train"][0]) # Keys: episode_index, step_index, task, difficulty, instruction, # image (PIL), action [7], proprio [25], reward, done Dataset statistics Task Episodes Difficulty… See the full description on the dataset page: https://huggingface.co/datasets/random-sequence/flock-robotics-vla-training-v2.

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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Dataset Card

FLock Robotics VLA Training Dataset v2

Expert demonstrations for the FLock Robotics VLA competition task. All trajectories are successful.

Quick start

python

from datasets import load_dataset

ds = load_dataset("random-sequence/flock-robotics-vla-training-v2")
print(ds["train"][0])
# Keys: episode_index, step_index, task, difficulty, instruction,
#       image (PIL), action [7], proprio [25], reward, done

Dataset statistics

Task	Episodes	Difficulty	Steps
lift_cube	8	low	~1,100
pickplacecan	20	low	~3,800
pickplacemilk	19	low	~3,600
pickplacebread	18	low	~3,300
pickplacecereal	16	low	~2,900
stack_blocks	22	medium	~10,600
Total	103		25,328

All 103 episodes are 100% successful (success-filtered collection: each episode was retried until success).

Schema

Each row is one timestep:

Column	Type	Description
`episode_index`	int32	Trajectory ID (0–102)
`step_index`	int32	Timestep within episode
`task`	string	Task name (e.g. `lift_cube`)
`difficulty`	string	`low` or `medium`
`instruction`	string	Natural language task instruction
`image`	Image	224×224 RGB agent-view frame
`action`	float32[7]	OSC delta `[Δx, Δy, Δz, Δroll, Δpitch, Δyaw, gripper]` clipped to [-1, 1]
`proprio`	float32[25]	Robot state: joint pos/vel, EEF pose, gripper
`reward`	float32	Sparse reward (1.0 at success, 0 otherwise)
`done`	bool	Episode termination flag

Schema version: robotics_vla_sample_trajectory_v1

Environment

—Simulator: robosuite 1.5.2
—Robot: Panda 7-DOF
—Controller: BASIC composite (OSC_POSE arm + gripper)
—Camera: agentview 224×224

Raw zip

training_traces_v2.zip is also available for backward compatibility. It contains the same data as individual metadata.json + trajectory.npz pairs under trajectories/traj_NNN_<task>_<difficulty>/.

SHA256: e255a52aa0bb9fbf77a6d44ce368e41cb1d2dec184c2e7b6530e83088d772411