Data & Benchmarks

Push-T Benchmark

Push-T is a planar manipulation benchmark in which a policy must push a T-shaped block to a target pose using a circular pusher, adapted from the Implicit Behavioral Cloning task suite and popularized by the Diffusion Policy paper of Chi et al. (2023). The task stresses multimodal solution strategies, contact-rich dynamics, and precise long-horizon corrections, and it is evaluated by final pose overlap with the goal.

Why it matters for physical AI

Push-T became the standard quick test of whether a policy class can represent multimodal action distributions, and results on it drove the adoption of diffusion-based policies for manipulation.