General In-Hand Object Rotation with Vision and Touch

We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it has access to ground-truth object shapes and physical properties. Then we distill it to operate on realistic yet noisy simulated visuotactile and proprioceptive sensory inputs. These multimodal inputs are fused via a visuotactile transformer, enabling online inference of object shapes and physical properties during deployment. We show significant performance improvements over prior methods and the importance of visual and tactile sensing.Comment: CoRL 2023; Website: https://haozhi.io/rotateit

In-Hand Object Rotation via Rapid Motor Adaptation

Generalized in-hand manipulation has long been an unsolved challenge of robotics. As a small step towards this grand goal, we demonstrate how to design and learn a simple adaptive controller to achieve in-hand object rotation using only fingertips. The controller is trained entirely in simulation on only cylindrical objects, which then - without any fine-tuning - can be directly deployed to a real robot hand to rotate dozens of objects with diverse sizes, shapes, and weights over the z-axis. This is achieved via rapid online adaptation of the controller to the object properties using only proprioception history. Furthermore, natural and stable finger gaits automatically emerge from training the control policy via reinforcement learning. Code and more videos are available at https://haozhi.io/horaComment: CoRL 2022. Code and Website: https://haozhi.io/hor

NYMPH: A multiprocessor for manipulation applications

The robotics group of the Stanford Artificial Intelligence Laboratory is currently developing a new computational system for robotics applications. Stanford's NYMPH system uses multiple NSC 32016 processors and one MC68010 based processor, sharing a common Intel Multibus. The 32K processors provide the raw computational power needed for advanced robotics applications, and the 68K provides a pleasant interface with the rest of the world. Software has been developed to provide useful communications and synchronization primitives, without consuming excessive processor resources or bus bandwidth. NYMPH provides both large amounts of computing power and a good programming environment, making it an effective research tool

Compliant manipulation with a dextrous robot hand

The control of precise, compliant manipulation tasks with multifingered robots is discussed. Emphasis is placed on performing manipulations of grasped objects that are themselves undergoing compliant motion. This class of manipulations include common tasks such as using tools, writing, and sliding an object on a surface. A task-level formulation is presented and illustrated. Results of experiments are presented to demonstrate the feasibility of performing precision manipulations with a dextrous hand

Forming complex dextrous manipulations from task primitives

This paper discusses the implementation of complex manipulation tasks with a dextrous hand. The approach used is to build a set of primitive manipulation functions and combine them to form complex tasks. Only fingertip, or precision, manipulations are considered. Each function performs a simple two-dimensional translation or rotation that can be generalized to work with objects of different sizes and using different grasping forces. Complex tasks are sequential combinations of the primitive functions. They are formed by analyzing the workspaces of the individual tasks and controlled by finite state machines. We present a number of examples, including a complex manipulation removing the top of a child-proof medicine bottle-that incorporates different hybrid position/force specifications of the primitive functions of which it is composed. The work has been implemented with a robot hand system using a Utah-MIT hand

Performance of modified jatropha oil in combination with hexagonal boron nitride particles as a bio-based lubricant for green machining

This study evaluates the machining performance of newly developed modified jatropha oils (MJO1, MJO3 and MJO5), both with and without hexagonal boron nitride (hBN) particles (ranging between 0.05 and 0.5 wt%) during turning of AISI 1045 using minimum quantity lubrication (MQL). The experimental results indicated that, viscosity improved with the increase in MJOs molar ratio and hBN concentration. Excellent tribological behaviours is found to correlated with a better machining performance were achieved by MJO5a with 0.05 wt%. The MJO5a sample showed the lowest values of cutting force, cutting temperature and surface roughness, with a prolonged tool life and less tool wear, qualifying itself to be a potential alternative to the synthetic ester, with regard to the environmental concern

A survey of dextrous manipulation

technical reportThe development of mechanical end effectors capable of dextrous manipulation is a rapidly growing and quite successful field of research. It has in some sense put the focus on control issues, in particular, how to control these remarkably humanlike manipulators to perform the deft movement that we take for granted in the human hand. The kinematic and control issues surrounding manipulation research are clouded by more basic concerns such as: what is the goal of a manipulation system, is the anthropomorphic or functional design methodology appropriate, and to what degree does the control of the manipulator depend on other sensory systems. This paper examines the potential of creating a general purpose, anthropomorphically motivated, dextrous manipulation system. The discussion will focus on features of the human hand that permit its general usefulness as a manipulator. A survey of machinery designed to emulate these capabilities is presented. Finally, the tasks of grasping and manipulation are examined from the control standpoint to suggest a control paradigm which is descriptive, yet flexible and computationally efficient1

Objekt-Manipulation und Steuerung der Greifkraft durch Verwendung von Taktilen Sensoren

This dissertation describes a new type of tactile sensor and an improved version of the dynamic tactile sensing approach that can provide a regularly updated and accurate estimate of minimum applied forces for use in the control of gripper manipulation. The pre-slip sensing algorithm is proposed and implemented into two-finger robot gripper. An algorithm that can discriminate between types of contact surface and recognize objects at the contact stage is also proposed. A technique for recognizing objects using tactile sensor arrays, and a method based on the quadric surface parameter for classifying grasped objects is described. Tactile arrays can recognize surface types on contact, making it possible for a tactile system to recognize translation, rotation, and scaling of an object independently.Diese Dissertation beschreibt eine neue Art von taktilen Sensoren und einen verbesserten Ansatz zur dynamischen Erfassung von taktilen daten, der in regelmäßigen Zeitabständen eine genaue Bewertung der minimalen Greifkraft liefert, die zur Steuerung des Greifers nötig ist. Ein Berechnungsverfahren zur Voraussage des Schlupfs, das in einen Zwei-Finger-Greifarm eines Roboters eingebaut wurde, wird vorgestellt. Auch ein Algorithmus zur Unterscheidung von verschiedenen Oberflächenarten und zur Erkennung von Objektformen bei der Berührung wird vorgestellt. Ein Verfahren zur Objekterkennung mit Hilfe einer Matrix aus taktilen Sensoren und eine Methode zur Klassifikation ergriffener Objekte, basierend auf den Daten einer rechteckigen Oberfläche, werden beschrieben. Mit Hilfe dieser Matrix können unter schiedliche Arten von Oberflächen bei Berührung erkannt werden, was es für das Tastsystem möglich macht, Verschiebung, Drehung und Größe eines Objektes unabhängig voneinander zu erkennen