Omkar2118/datasets huggingface dataset:
SimplerEnv-Google Robot
Full name: Evaluating Real-World Robot Manipulation Policies in Simulation

Significant progress has been made in building generalist robot manipulation policies, yet their scalable and reproducible evaluation remains challenging, as real-world evaluation is operationally expensive and inefficient. We propose employing physical simulators as efficient, scalable, and informative complements to real-world evaluations. These simulation evaluations offer valuable quantitative metrics for checkpoint selection, insights into potential real-world policy behaviors or failure modes, and standardized setups to enhance reproducibility.

This repository's code is based in the SAPIEN simulator and the CPU based ManiSkill2 benchmark. We have also integrated the Bridge dataset environments into ManiSkill3, which offers GPU parallelization and can run 10-15x faster than the ManiSkill2 version. For instructions on how to use the GPU parallelized environments and evaluate policies on them, see: https://github.com/simpler-env/SimplerEnv/tree/maniskill3

This repository encompasses 2 real-to-sim evaluation setups:

Visual Matching evaluation: Matching real & sim visual appearances for policy evaluation by overlaying real-world images onto simulation backgrounds and adjusting foreground object and robot textures in simulation.
Variant Aggregation evaluation: creating different sim environment variants (e.g., different backgrounds, lightings, distractors, table textures, etc) and averaging their results.

Introducing paper: Evaluating Real-World Robot Manipulation Policies in Simulation