Sense, Think, Grasp: A study on visual and tactile information processing for autonomous manipulation

Interacting with the environment using hands is one of the distinctive abilities of humans with respect to other species. This aptitude reflects on the crucial role played by objects\u2019 manipulation in the world that we have shaped for us. With a view of bringing robots outside industries for supporting people during everyday life, the ability of manipulating objects autonomously and in unstructured environments is therefore one of the basic skills they need. Autonomous manipulation is characterized by great complexity especially regarding the processing of sensors information to perceive the surrounding environment. Humans rely on vision for wideranging tridimensional information, prioprioception for the awareness of the relative position of their own body in the space and the sense of touch for local information when physical interaction with objects happens. The study of autonomous manipulation in robotics aims at transferring similar perceptive skills to robots so that, combined with state of the art control techniques, they could be able to achieve similar performance in manipulating objects. The great complexity of this task makes autonomous manipulation one of the open problems in robotics that has been drawing increasingly the research attention in the latest years. In this work of Thesis, we propose possible solutions to some key components of autonomous manipulation, focusing in particular on the perception problem and testing the developed approaches on the humanoid robotic platform iCub. When available, vision is the first source of information to be processed for inferring how to interact with objects. The object modeling and grasping pipeline based on superquadric functions we designed meets this need, since it reconstructs the object 3D model from partial point cloud and computes a suitable hand pose for grasping the object. Retrieving objects information with touch sensors only is a relevant skill that becomes crucial when vision is occluded, as happens for instance during physical interaction with the object. We addressed this problem with the design of a novel tactile localization algorithm, named Memory Unscented Particle Filter, capable of localizing and recognizing objects relying solely on 3D contact points collected on the object surface. Another key point of autonomous manipulation we report on in this Thesis work is bi-manual coordination. The execution of more advanced manipulation tasks in fact might require the use and coordination of two arms. Tool usage for instance often requires a proper in-hand object pose that can be obtained via dual-arm re-grasping. In pick-and-place tasks sometimes the initial and target position of the object do not belong to the same arm workspace, then requiring to use one hand for lifting the object and the other for locating it in the new position. At this regard, we implemented a pipeline for executing the handover task, i.e. the sequences of actions for autonomously passing an object from one robot hand on to the other. The contributions described thus far address specific subproblems of the more complex task of autonomous manipulation. This actually differs from what humans do, in that humans develop their manipulation skills by learning through experience and trial-and-error strategy. Aproper mathematical formulation for encoding this learning approach is given by Deep Reinforcement Learning, that has recently proved to be successful in many robotics applications. For this reason, in this Thesis we report also on the six month experience carried out at Berkeley Artificial Intelligence Research laboratory with the goal of studying Deep Reinforcement Learning and its application to autonomous manipulation

Robust and Accurate Superquadric Recovery: a Probabilistic Approach

Interpreting objects with basic geometric primitives has long been studied in computer vision. Among geometric primitives, superquadrics are well known for their ability to represent a wide range of shapes with few parameters. However, as the first and foremost step, recovering superquadrics accurately and robustly from 3D data still remains challenging. The existing methods are subject to local optima and sensitive to noise and outliers in real-world scenarios, resulting in frequent failure in capturing geometric shapes. In this paper, we propose the first probabilistic method to recover superquadrics from point clouds. Our method builds a Gaussian-uniform mixture model (GUM) on the parametric surface of a superquadric, which explicitly models the generation of outliers and noise. The superquadric recovery is formulated as a Maximum Likelihood Estimation (MLE) problem. We propose an algorithm, Expectation, Maximization, and Switching (EMS), to solve this problem, where: (1) outliers are predicted from the posterior perspective; (2) the superquadric parameter is optimized by the trust-region reflective algorithm; and (3) local optima are avoided by globally searching and switching among parameters encoding similar superquadrics. We show that our method can be extended to the multi-superquadrics recovery for complex objects. The proposed method outperforms the state-of-the-art in terms of accuracy, efficiency, and robustness on both synthetic and real-world datasets. The code is at http://github.com/bmlklwx/EMS-superquadric_fitting.git.Comment: Accepted to CVPR202

Investigating Scene Understanding for Robotic Grasping: From Pose Estimation to Explainable AI

In the rapidly evolving field of robotics, the ability to accurately grasp and manipulate objects—known as robotic grasping—is a cornerstone of autonomous operation. This capability is pivotal across a multitude of applications, from industrial manufacturing automation to supply chain management, and is a key determinant of a robot's ability to interact effectively with its environment. Central to this capability is the concept of scene understanding, a complex task that involves interpreting the robot's environment to facilitate decision-making and action planning. This thesis presents a comprehensive exploration of scene understanding for robotic grasping, with a particular emphasis on pose estimation, a critical aspect of scene understanding. Pose estimation, the process of determining the position and orientation of objects within the robot's environment, is a crucial component of robotic grasping. It provides the robot with the necessary spatial information about the objects in the scene, enabling it to plan and execute grasping actions effectively. However, many current pose estimation methods provide relative pose compared to a 3D model, which lacks descriptiveness without referencing the 3D model. This thesis explores the use of keypoints and superquadrics as more general and descriptive representations of an object's pose. These novel approaches address the limitations of traditional methods and significantly enhance the generalizability and descriptiveness of pose estimation, thereby improving the overall effectiveness of robotic grasping. In addition to pose estimation, this thesis briefly touches upon the importance of uncertainty estimation and explainable AI in the context of robotic grasping. It introduces the concept of multimodal consistency for uncertainty estimation, providing a reliable measure of uncertainty that can enhance decision-making in human-in-the-loop situations. Furthermore, it explores the realm of explainable AI, presenting a method for gaining deeper insights into deep learning models, thereby enhancing their transparency and interpretability. In summary, this thesis presents a comprehensive approach to scene understanding for robotic grasping, with a particular emphasis on pose estimation. It addresses key challenges and advances the state of the art in this critical area of robotics research. The research is structured around five published papers, each contributing to a unique aspect of the overall study

A Robotic System for Learning Visually-Driven Grasp Planning (Dissertation Proposal)

We use findings in machine learning, developmental psychology, and neurophysiology to guide a robotic learning system\u27s level of representation both for actions and for percepts. Visually-driven grasping is chosen as the experimental task since it has general applicability and it has been extensively researched from several perspectives. An implementation of a robotic system with a gripper, compliant instrumented wrist, arm and vision is used to test these ideas. Several sensorimotor primitives (vision segmentation and manipulatory reflexes) are implemented in this system and may be thought of as the innate perceptual and motor abilities of the system. Applying empirical learning techniques to real situations brings up such important issues as observation sparsity in high-dimensional spaces, arbitrary underlying functional forms of the reinforcement distribution and robustness to noise in exemplars. The well-established technique of non-parametric projection pursuit regression (PPR) is used to accomplish reinforcement learning by searching for projections of high-dimensional data sets that capture task invariants. We also pursue the following problem: how can we use human expertise and insight into grasping to train a system to select both appropriate hand preshapes and approaches for a wide variety of objects, and then have it verify and refine its skills through trial and error. To accomplish this learning we propose a new class of Density Adaptive reinforcement learning algorithms. These algorithms use statistical tests to identify possibly interesting regions of the attribute space in which the dynamics of the task change. They automatically concentrate the building of high resolution descriptions of the reinforcement in those areas, and build low resolution representations in regions that are either not populated in the given task or are highly uniform in outcome. Additionally, the use of any learning process generally implies failures along the way. Therefore, the mechanics of the untrained robotic system must be able to tolerate mistakes during learning and not damage itself. We address this by the use of an instrumented, compliant robot wrist that controls impact forces

\u3cem\u3eGRASP News\u3c/em\u3e, Volume 6, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory, edited by Gregory Long and Alok Gupta

Adapting Everyday Manipulation Skills to Varied Scenarios

This work is partially funded by: (1) AGH University of Science and Technology, grant No 15.11.230.318. (2) Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center 1320, EASE. (3) Elphinstone Scholarship from University of Aberdeen.Postprin

GRASP News Volume 9, Number 1

A report of the General Robotics and Active Sensory Perception (GRASP) Laboratory