Table30v2: 30 Robotic Manipulation Tasks Across 4 Embodiments

RoboChallenge Table30 v2 Dataset includes 30 diverse manipulation tasks across 4 embodiments. The tasks, such as 'put_the_books_back' and 'tie_a_knot', involve object interaction and scene understanding. The dataset was created by RoboChallenge and was last updated on 2026-04-10.

Use Cases

• Train robotic control policies based on the described manipulation tasks like 'tidy_up_the_makeup_table'.
• Benchmark multi-task learning algorithms across the 4 different embodiments mentioned in the description.
• Develop scene understanding models for object interaction based on tasks such as 'stamp_positioning' and 'paint_jam'.

Strengths

• Includes 30 distinct manipulation tasks, providing task diversity.
• Covers 4 different embodiments, suggesting potential for cross-embodiment learning.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and sample data are unknown, which may limit suitability assessment.

Provenance

Source

RoboChallenge

Collection Method

Likely involves robotic simulation or real-world data collection for manipulation tasks.

Time Range

null

Freshness

Last updated 2026-04-10 10:19:26; freshness should be verified.

Geography

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