Reasoning about Geometric Object Interactions in 3D for Manipulation Action Understanding

In order to efficiently interact with human users, intelligent agents and autonomous systems need the ability of interpreting human actions. We focus our attention on manipulation actions, wherein an agent typically grasps an object and moves it, possibly altering its physical state. Agent-object and object-object interactions during a manipulation are a defining part of the performed action itself. In this thesis, we focus on extracting semantic cues, derived from geometric object interactions in 3D space during a manipulation, that are useful for action understanding at the cognitive level. First, we introduce a simple grounding model for the most common pairwise spatial relations between objects and investigate the descriptive power of their temporal evolution for action characterization. We propose a compact, abstract action descriptor that encodes the geometric object interactions during action execution, as captured by the spatial relation dynamics. Our experiments on a diverse dataset confirm both the validity and effectiveness of our spatial relation models and the discriminative power of our representation with respect to the underlying action semantics. Second, we model and detect lower level interactions, namely object contacts and separations, viewing them as topological scene changes within a dense motion estimation setting. In addition to improving motion estimation accuracy in the challenging case of motion boundaries induced by these events, our approach shows promising performance in the explicit detection and classification of the latter. Building upon dense motion estimation and using detected contact events as an attention mechanism, we propose a bottom-up pipeline for the guided segmentation and rigid motion extraction of manipulated objects. Finally, in addition to our methodological contributions, we introduce a new open-source software library for point cloud data processing, developed for the needs of this thesis, which aims at providing an easy to use, flexible, and efficient framework for the rapid development of performant software for a range of 3D perception tasks

Prediction of Manipulation Actions

By looking at a person’s hands, one can often tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor’s intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded videos of subjects performing different manipulation actions on the same object, such as “squeezing”, “flipping”, “washing”, “wiping” and “scratching” with a sponge. In psychophysical experiments, we evaluated human observers’ skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input image patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets show that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithms to predict in real time what and how a dexterous action is performed.This work was funded by the support of the National Science Foundation under Grant SMA 1540917 and Grant CNS 1544797, by Samsung under the GRO Program (Nos. 20477, 355022), and by DARPA through U.S. Army Grant W911NF-14-1- 0384

Prediction of manipulation actions

Looking at a person's hands one often can tell what the person is going to do next, how his/her hands are moving and where they will be, because an actor's intentions shape his/her movement kinematics during action execution. Similarly, active systems with real-time constraints must not simply rely on passive video-segment classification, but they have to continuously update their estimates and predict future actions. In this paper, we study the prediction of dexterous actions. We recorded from subjects performing different manipulation actions on the same object, such as "squeezing", "flipping", "washing", "wiping" and "scratching" with a sponge. In psychophysical experiments, we evaluated human observers' skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object. We then developed a recurrent neural network based method for action prediction using as input patches around the hand. We also used the same formalism to predict the forces on the finger tips using for training synchronized video and force data streams. Evaluations on two new datasets showed that our system closely matches human performance in the recognition task, and demonstrate the ability of our algorithm to predict what and how a dexterous action is performed