6-DoF Stability Field via Diffusion Models

A core capability for robot manipulation is reasoning over where and how to stably place objects in cluttered environments. Traditionally, robots have relied on object-specific, hand-crafted heuristics in order to perform such reasoning, with limited generalizability beyond a small number of object instances and object interaction patterns. Recent approaches instead learn notions of physical interaction, namely motion prediction, but require supervision in the form of labeled object information or come at the cost of high sample complexity, and do not directly reason over stability or object placement. We present 6-DoFusion, a generative model capable of generating 3D poses of an object that produces a stable configuration of a given scene. Underlying 6-DoFusion is a diffusion model that incrementally refines a randomly initialized SE(3) pose to generate a sample from a learned, context-dependent distribution over stable poses. We evaluate our model on different object placement and stacking tasks, demonstrating its ability to construct stable scenes that involve novel object classes as well as to improve the accuracy of state-of-the-art 3D pose estimation methods.Comment: In submissio

Is That a Chair? Imagining Affordances Using Simulations of an Articulated Human Body

For robots to exhibit a high level of intelligence in the real world, they must be able to assess objects for which they have no prior knowledge. Therefore, it is crucial for robots to perceive object affordances by reasoning about physical interactions with the object. In this paper, we propose a novel method to provide robots with an ability to imagine object affordances using physical simulations. The class of chair is chosen here as an initial category of objects to illustrate a more general paradigm. In our method, the robot "imagines" the affordance of an arbitrarily oriented object as a chair by simulating a physical sitting interaction between an articulated human body and the object. This object affordance reasoning is used as a cue for object classification (chair vs non-chair). Moreover, if an object is classified as a chair, the affordance reasoning can also predict the upright pose of the object which allows the sitting interaction to take place. We call this type of poses the functional pose. We demonstrate our method in chair classification on synthetic 3D CAD models. Although our method uses only 30 models for training, it outperforms appearance-based deep learning methods, which require a large amount of training data, when the upright orientation is not assumed to be known a priori. In addition, we showcase that the functional pose predictions of our method align well with human judgments on both synthetic models and real objects scanned by a depth camera.Comment: 7 pages, 6 figures. Accepted to ICRA202

An Interactive Approach for Functional Prototype Recovery from a Single RGBD Image

Inferring the functionality of an object from a single RGBD image is difficult for two reasons: lack of semantic information about the object, and missing data due to occlusion. In this paper, we present an interactive framework to recover a 3D functional prototype from a single RGBD image. Instead of precisely reconstructing the object geometry for the prototype, we mainly focus on recovering the object’s functionality along with its geometry. Our system allows users to scribble on the image to create initial rough proxies for the parts. After user annotation of high-level relations between parts, our system automatically jointly optimizes detailed joint parameters (axis and position) and part geometry parameters (size, orientation, and position). Such prototype recovery enables a better understanding of the underlying image geometry and allows for further physically plausible manipulation. We demonstrate our framework on various indoor objects with simple or hybrid functions