The system of robot manipulation involves a pipeline consisting of the perception of objects in the environment and the planning of actions in 3D space. Deep learning approaches are employed to segment scenes into components of objects and then learn object-centric features to predict actions for downstream tasks. Despite having achieved promising performance in several manipulation tasks, supervised approaches lack inductive biases related to general properties of objects. Recent advances show that by encoding and reconstructing scenes in an object-centric fashion, the model can discover object-like entities from raw data without human supervision. Moreover, by reconstructing the discovered objects, the model can learn a variational latent space that captures the various shapes and textures of the objects, regularised by a chosen prior distribution. In this thesis, we investigate the properties of this learned object-centric latent space and develop novel object-centric generative models (OCGMs) that can be applied to real-world robotics scenarios.
In the first part of this thesis, we investigate a tool-synthesis task which leverages a learned latent space to optimise a wide range of tools applied to a reaching task. Given an image that illustrates the obstacles and the reaching target in the scene, an affordance predictor is trained to predict the feasibility of the tool for the given task. To imitate human tool-use experiences, feasibility labels are acquired from simulated trial-and-errors of the reaching task. We found that by employing an activation maximisation step, the model can synthesis proper tools for the given tasks with high accuracy. Moreover, the tool-synthesis process indicates the existence of a task-relevant trajectory in the learned latent space that can be found by a trained affordance predictor.
The second part of this thesis focuses on the development of novel OCGMs and their applications to robotic tasks. We first introduce a 2D OCGM that is deployed to robot manipulation datasets in both simulation and real-world scenarios. Despite the intensive interactions between robot arm and objects, we find the model discovers meaningful object entities from the raw observations without any human supervision. We next upgrade the 2D OCGM to 3D by leveraging NeRFs as decoders to explicitly model the 3D geometry of objects and the background. To disentangle the object spatial information from its appearance information, we propose a minimum volume principle for unsupervised 6D pose estimation of the objects. Considering the occlusion in the scene, we further improve the pose estimation by introducing a shape completion module to imagine the unobserved parts of the objects before the pose estimation step. In the end, we successfully apply the model in real-world robotics scenarios and compare its performance in several tasks including the 3D reconstruction, object-centric latent representation learning, 6D pose estimation for object rearrangement, against several baselines. We find that despite being an unsupervised approach, our model achieves improved performance across a range of different real-world tasks
