25 research outputs found
EDGI: Equivariant Diffusion for Planning with Embodied Agents
Embodied agents operate in a structured world, often solving tasks with
spatial, temporal, and permutation symmetries. Most algorithms for planning and
model-based reinforcement learning (MBRL) do not take this rich geometric
structure into account, leading to sample inefficiency and poor generalization.
We introduce the Equivariant Diffuser for Generating Interactions (EDGI), an
algorithm for MBRL and planning that is equivariant with respect to the product
of the spatial symmetry group SE(3), the discrete-time translation group Z, and
the object permutation group Sn. EDGI follows the Diffuser framework (Janner et
al., 2022) in treating both learning a world model and planning in it as a
conditional generative modeling problem, training a diffusion model on an
offline trajectory dataset. We introduce a new SE(3)xZxSn-equivariant diffusion
model that supports multiple representations. We integrate this model in a
planning loop, where conditioning and classifier guidance let us softly break
the symmetry for specific tasks as needed. On object manipulation and
navigation tasks, EDGI is substantially more sample efficient and generalizes
better across the symmetry group than non-equivariant models.Comment: Accepted at NeurIPS 2023. v2: matches camera-ready versio
Feature Likelihood Score: Evaluating Generalization of Generative Models Using Samples
The past few years have seen impressive progress in the development of deep
generative models capable of producing high-dimensional, complex, and
photo-realistic data. However, current methods for evaluating such models
remain incomplete: standard likelihood-based metrics do not always apply and
rarely correlate with perceptual fidelity, while sample-based metrics, such as
FID, are insensitive to overfitting, i.e., inability to generalize beyond the
training set. To address these limitations, we propose a new metric called the
Feature Likelihood Score (FLS), a parametric sample-based score that uses
density estimation to provide a comprehensive trichotomic evaluation accounting
for novelty (i.e., different from the training samples), fidelity, and
diversity of generated samples. We empirically demonstrate the ability of FLS
to identify specific overfitting problem cases, where previously proposed
metrics fail. We also extensively evaluate FLS on various image datasets and
model classes, demonstrating its ability to match intuitions of previous
metrics like FID while offering a more comprehensive evaluation of generative
models