CLARU
phi-monsterapache-2.0

Deconfounded ordinal set (RoboCasa)

A dataset of 504 episodes from RoboCasa focused on ordinal reasoning tasks with randomized spacing and slots, where rank words rather than absolute positions predict targets. Part of the Galahad release designed to deconfound ordinal understanding from absolute position encoding.

Downloads3
Episodes504

Why This Matters for Physical AI

This dataset addresses the fundamental problem of grounding ordinal language concepts (e.g., 'first', 'second') in robotic manipulation by explicitly deconfounding ordinal reasoning from absolute spatial positions, which is critical for enabling language-guided robots to follow relative spatial instructions in diverse environments.

Technical Profile

Environment
simulation
Task Types
manipulation
Episodes
504
Data Format
LeRobot
Annotation Types
language_instructions
License
apache-2.0
Part of the RoboCasa family

Access

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