We propose a novel unsupervised method for motion-based 3D part decomposition of articulated objects using a single monocular video of a dynamic scene. In contrast to existing unsupervised methods relying on optical flow or tracking techniques, our approach addresses this problem without additional information by leveraging Gaussian splatting techniques. We generate a series of Gaussians from a monocular video and analyze their relationships to decompose the dynamic scene into motion-based parts. To decompose dynamic scenes consisting of articulated objects, we design an articulated deformation field suitable for the movement of articulated objects. And to effectively understand the relationships of Gaussians of different shapes, we propose a 3D reconstruction loss using 3D occupied voxel maps generated from the Gaussians. Experimental results demonstrate that our method outperforms existing approaches in terms of 3D part decomposition for articulated objects. More demos and code are available at https://choonsik93.github.io/artnerf/.

In this paper, we propose a signed distance field (SDF)-based deep Q-learning framework for multi-object re-arrangement. Our method learns to rearrange objects with non-prehensile manipulation, e.g., pushing, in unstructured environments. To reliably estimate Q-values in various scenes, we train the Q-network using an SDF-based scene graph as the state-goal representation. To this end, we introduce SDFGCN, a scalable Q-network structure which can estimate Q-values from a set of SDF images satisfying permutation invariance by using graph convolutional networks. In contrast to grasping-based rearrangement methods that rely on the performance of grasp predictive models for perception and movement, our approach enables rearrangements on unseen objects, including hard-to-grasp objects. Moreover, our method does not require any expert demonstrations. We observe that SDFGCN is capable of unseen objects in challenging configurations, both in the simulation and the real world.

In this paper, we tackle the problem of 6-DoF grasp detection which is crucial for robot grasping in cluttered real-world scenes. Unlike existing approaches which synthesize 6-DoF grasp data sets and train grasp quality networks with input grasp representations based on point clouds, we rather take a novel hierarchical approach which does not use any 6-DoF grasp data. We cast the 6-DoF grasp detection problem as a robot arm approaching direction selection problem using the existing 4-DoF grasp detection algorithm, by exploiting a fully convolutional grasp quality network for evaluating the quality of an approaching direction. To select the best approaching direction with the highest grasp quality, we propose an approaching direction selection method which leverages a geometry-based prior and a derivative-free optimization method. Specifically, we optimize the direction iteratively using the cross entropy method with initial samples of surface normal directions. Our algorithm efficiently finds diverse 6-DoF grasps by the novel way of evaluating and optimizing approaching directions. We validate that the proposed method outperforms other selection methods in scenarios with cluttered objects in a physics-based simulator. Finally, we show that our method outperforms the state-of-the-art grasp detection method in real-world experiments with robots.