This dataset provides rich multi-modal demonstrations in LeRobot format, covering 20 real-robot tasks and 3 simulation tasks. Beyond standard end-effector trajectories, we provide joint positions, velocities, currents, camera views, and mobile-base states — giving participants the freedom to explore diverse input representations.

Dataset Structure

maniparena-dataset/
├── real/
│   ├── execution_reasoning/        (10 tasks, ~5,000 episodes)
│   ├── semantic_reasoning/         (5 tasks, ~2,800 episodes)
│   └── mobile_manipulation/        (5 tasks, ~2,900 episodes)
└── sim/
    ├── press_button_in_order/      (60 episodes)
    ├── put_blocks_to_color/        (50 episodes)
    └── pick_fruits_into_basket/    (50 episodes)

Each task folder follows LeRobot format:

<task>/
    meta/info.json
    meta/tasks.jsonl
    data/chunk-000/episode_000000.parquet
    videos/chunk-000/
        observation.images.faceImg/episode_000000.mp4
        observation.images.leftImg/episode_000000.mp4
        observation.images.rightImg/episode_000000.mp4

Real Robot Data

Tabletop tasks (Execution Reasoning + Semantic Reasoning) have 56-dimensional state/action vectors. Mobile Manipulation tasks have 62-dimensional state/action vectors (56D + 6D mobile extras).

Dimension Layout

End-effector (index 0–13, 14D):

Index	Key	Dim	Description
0–2	`follow_left_ee_cartesian_pos`	3	Left arm position (x, y, z)
3–5	`follow_left_ee_rotation`	3	Left arm rotation (roll, pitch, yaw)
6	`follow_left_gripper`	1	Left gripper open/close
7–9	`follow_right_ee_cartesian_pos`	3	Right arm position (x, y, z)
10–12	`follow_right_ee_rotation`	3	Right arm rotation (roll, pitch, yaw)
13	`follow_right_gripper`	1	Right gripper open/close

Coordinate system: +x forward, +y left, +z up.

Joint — left arm (index 14–34, 21D):

Index	Key	Dim	Description
14–20	`follow_left_arm_joint_pos`	7	Left arm joint positions (6 joints + gripper)
21–27	`follow_left_arm_joint_dev`	7	Left arm joint velocities (6 joints + gripper)
28–34	`follow_left_arm_joint_cur`	7	Left arm joint currents (6 joints + gripper)

Joint — right arm (index 35–55, 21D):

Index	Key	Dim	Description
35–41	`follow_right_arm_joint_pos`	7	Right arm joint positions (6 joints + gripper)
42–48	`follow_right_arm_joint_dev`	7	Right arm joint velocities (6 joints + gripper)
49–55	`follow_right_arm_joint_cur`	7	Right arm joint currents (6 joints + gripper)

The last element (index 20, 27, 34, 41, 48, 55) in each 7D joint group is the gripper value.

Mobile manipulation extras (index 56–61, mobile tasks only, 6D):

Index	Key	Dim	Description
56–57	`head_actions`	2	Head rotation (yaw, pitch)
58	`height`	1	Lift mechanism height
59–61	`velocity_decomposed_odom`	3	Chassis velocity (vx, vy, angular velocity)

Tabletop tasks = 56D (index 0–55). Mobile Manipulation tasks = 62D (index 0–61).

Task List — Real Robot

Execution Reasoning (10 tasks):

Task	Episodes	Key Challenge
`arrange_cup_inverted_triangle`	528	Multi-object spatial planning
`put_spoon_to_bowl`	525	Precision grasping, varied shapes
`put_glasses_on_woodshelf`	513	Fragile object handling
`put_ring_onto_rod`	517	Sub-cm insertion precision
`put_items_into_drawer`	510	Multi-object coordination
`pick_items_into_basket`	532	Adaptive grasping
`pour_water_from_bottle`	526	Force control, liquid dynamics
`insert_wireline`	530	Contact-rich, mm-level accuracy
`put_stationery_in_case`	390	Multi-object organization
`put_blocks_to_color`	451	Color-zone matching

Semantic Reasoning (5 tasks):

Task	Episodes	Key Challenge
`sort_headphone`	515	Recognize headphone type
`classify_items_as_shape`	545	Map objects to shape categories
`press_button_in_order`	538	Color-button mapping + sequence
`pair_up_items`	540	Match pairs by pattern
`pick_fruits_into_basket`	645	Fruit vs. non-fruit distinction

Mobile Manipulation (5 tasks):

Task	Episodes	Key Challenge
`put_clothes_in_hamper`	540	Navigate + pick clothes
`hang_up_picture`	576	Navigate to wall + hang
`organize_shoes`	595	Navigate + arrange on rack
`put_bottle_on_woodshelf`	630	Navigate to shelf + place
`take_and_set_tableware`	531	Navigate + set table

Simulation Data (28D)

Simulation demonstrations contain 28-dimensional observation.state and action vectors, combining end-effector (14D) and joint (14D) data from the same trajectories.

Dimension Layout

End-effector (index 0–13):

Index	Key	Dim	Description
0–2	`ee_left_xyz`	3	Left arm EE position (x, y, z)
3–5	`ee_left_rpy`	3	Left arm EE rotation (roll, pitch, yaw)
6	`ee_left_gripper`	1	Left gripper
7–9	`ee_right_xyz`	3	Right arm EE position (x, y, z)
10–12	`ee_right_rpy`	3	Right arm EE rotation (roll, pitch, yaw)
13	`ee_right_gripper`	1	Right gripper

Joint (index 14–27):

Index	Key	Dim	Description
14–19	`joint_left_pos`	6	Left arm joint positions
20	`joint_left_gripper`	1	Left joint gripper
21–26	`joint_right_pos`	6	Right arm joint positions
27	`joint_right_gripper`	1	Right joint gripper

The first 14 dimensions (EE) are directly compatible with real-robot index 0–13.

Task List — Simulation

Task	Episodes	Real-robot Counterpart
`press_button_in_order`	60	press_button_in_order
`put_blocks_to_color`	50	put_blocks_to_color
`pick_fruits_into_basket`	50	pick_fruits_into_basket

Camera Views

All tasks include 3 synchronized camera streams at 480×640 resolution:

Camera	Key	Description
Front	`observation.images.faceImg`	Third-person overhead view
Left wrist	`observation.images.leftImg`	Left arm wrist-mounted camera
Right wrist	`observation.images.rightImg`	Right arm wrist-mounted camera

Recording Frequency

All data is recorded at 20 Hz.

Quick Usage

import pandas as pd
import numpy as np

# Load one episode
df = pd.read_parquet("real/execution_reasoning/put_blocks_to_color/data/chunk-000/episode_000000.parquet")

state = np.stack(df["observation.state"].tolist())  # (T, 56) for real, (T, 28) for sim
action = np.stack(df["action"].tolist())

# EE data (first 14 dims — same layout for real and sim)
ee_left = state[:, 0:7]    # xyz(3) + rpy(3) + gripper(1)
ee_right = state[:, 7:14]

# Joint data (real: index 14–51, sim: index 14–27)
left_joint_pos = state[:, 14:20]   # 6 joint positions
right_joint_pos = state[:, 33:39]  # real only (sim uses 21:27)

Citation

If you find this dataset useful in your research, please consider citing:

@misc{sun2026maniparena,
      title={ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation},
      author={Yu Sun and Meng Cao and Ping Yang and Rongtao Xu and Yunxiao Yan and Runze Xu and Liang Ma and Roy Gan and Andy Zhai and Qingxuan Chen and Zunnan Xu and Hao Wang and Jincheng Yu and Lucy Liang and Qian Wang and Ivan Laptev and Ian D Reid and Xiaodan Liang},
      year={2026},
      eprint={2603.28545},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2603.28545},
}

License

Apache License 2.0

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Paper for ManipArena/maniparena-dataset

ManipArena: Comprehensive Real-world Evaluation of Reasoning-Oriented Generalist Robot Manipulation

Paper • 2603.28545 • Published 1 day ago