Fine-Tuning Generative Models as an Inference Method for Robotic Tasks
Orr Krupnik Elisei Shafer Tom Jurgenson Aviv Tamar
Adaptable models could greatly benefit robotic agents operating in the real
world, allowing them to deal with novel and varying conditions. While
approaches such as Bayesian inference are well-studied frameworks for adapting
models to evidence, we build on recent advances in deep generative models which
have greatly affected many areas of robotics. Harnessing modern GPU
acceleration, we investigate how to quickly adapt the sample generation of
neural network models to observations in robotic tasks. We propose a simple and
general method that is applicable to various deep generative models and robotic
environments. The key idea is to quickly fine-tune the model by fitting it to
generated samples matching the observed evidence, using the cross-entropy
method. We show that our method can be applied to both autoregressive models
and variational autoencoders, and demonstrate its usability in object shape
inference from grasping, inverse kinematics calculation, and point cloud
completion.