THE COLOSSEUM: A Benchmark for Evaluating Generalization for Robotic Manipulation
This addresses the need for systematic generalization evaluation in robotics, providing a benchmark for researchers to improve model robustness, though it is incremental as it builds on existing evaluation frameworks.
The paper tackles the problem of evaluating robotic manipulation policies' generalization to environmental changes by introducing THE COLOSSEUM, a simulation benchmark with 20 tasks and 14 perturbation axes, revealing that state-of-the-art models suffer 30-50% success rate degradation and up to ≥75% with multiple perturbations, with results correlated to real-world experiments (R² = 0.614).
To realize effective large-scale, real-world robotic applications, we must evaluate how well our robot policies adapt to changes in environmental conditions. Unfortunately, a majority of studies evaluate robot performance in environments closely resembling or even identical to the training setup. We present THE COLOSSEUM, a novel simulation benchmark, with 20 diverse manipulation tasks, that enables systematical evaluation of models across 14 axes of environmental perturbations. These perturbations include changes in color, texture, and size of objects, table-tops, and backgrounds; we also vary lighting, distractors, physical properties perturbations and camera pose. Using THE COLOSSEUM, we compare 5 state-of-the-art manipulation models to reveal that their success rate degrades between 30-50% across these perturbation factors. When multiple perturbations are applied in unison, the success rate degrades $\geq$75%. We identify that changing the number of distractor objects, target object color, or lighting conditions are the perturbations that reduce model performance the most. To verify the ecological validity of our results, we show that our results in simulation are correlated ($\bar{R}^2 = 0.614$) to similar perturbations in real-world experiments. We open source code for others to use THE COLOSSEUM, and also release code to 3D print the objects used to replicate the real-world perturbations. Ultimately, we hope that THE COLOSSEUM will serve as a benchmark to identify modeling decisions that systematically improve generalization for manipulation. See https://robot-colosseum.github.io/ for more details.