12,231 research outputs found
ARNOLD: A Benchmark for Language-Grounded Task Learning With Continuous States in Realistic 3D Scenes
Understanding the continuous states of objects is essential for task learning
and planning in the real world. However, most existing task learning benchmarks
assume discrete(e.g., binary) object goal states, which poses challenges for
the learning of complex tasks and transferring learned policy from simulated
environments to the real world. Furthermore, state discretization limits a
robot's ability to follow human instructions based on the grounding of actions
and states. To tackle these challenges, we present ARNOLD, a benchmark that
evaluates language-grounded task learning with continuous states in realistic
3D scenes. ARNOLD is comprised of 8 language-conditioned tasks that involve
understanding object states and learning policies for continuous goals. To
promote language-instructed learning, we provide expert demonstrations with
template-generated language descriptions. We assess task performance by
utilizing the latest language-conditioned policy learning models. Our results
indicate that current models for language-conditioned manipulations continue to
experience significant challenges in novel goal-state generalizations, scene
generalizations, and object generalizations. These findings highlight the need
to develop new algorithms that address this gap and underscore the potential
for further research in this area. See our project page at:
https://arnold-benchmark.github.ioComment: The first two authors contributed equally; 20 pages; 17 figures;
project availalbe: https://arnold-benchmark.github.io
On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law
Out-of-distribution (OOD) testing is increasingly popular for evaluating a
machine learning system's ability to generalize beyond the biases of a training
set. OOD benchmarks are designed to present a different joint distribution of
data and labels between training and test time. VQA-CP has become the standard
OOD benchmark for visual question answering, but we discovered three troubling
practices in its current use. First, most published methods rely on explicit
knowledge of the construction of the OOD splits. They often rely on
``inverting'' the distribution of labels, e.g. answering mostly 'yes' when the
common training answer is 'no'. Second, the OOD test set is used for model
selection. Third, a model's in-domain performance is assessed after retraining
it on in-domain splits (VQA v2) that exhibit a more balanced distribution of
labels. These three practices defeat the objective of evaluating
generalization, and put into question the value of methods specifically
designed for this dataset. We show that embarrassingly-simple methods,
including one that generates answers at random, surpass the state of the art on
some question types. We provide short- and long-term solutions to avoid these
pitfalls and realize the benefits of OOD evaluation
- …