In-hand manipulation is a crucial ability for reorienting and repositioning objects within grasps. The main challenges in this are not only the complexity of the computational models, but also the risks of grasp instability caused by active finger motions, such as rolling, sliding, breaking, and remaking contacts. This paper presents the development of the Roller Ring (RR), a modular robotic attachment with active surfaces that is wearable by both robot and human hands to manipulate without lifting a finger. By installing the angled RRs on hands, such that their spatial motions are not colinear, we derive a general differential motion model for manipulating objects. Our motion model shows that complete in-hand manipulation skill sets can be provided by as few as only 2 RRs through non-holonomic object motions, while more RRs can enable enhanced manipulation dexterity with fewer motion constraints. Through extensive experiments, we test the RRs on both a robot hand and a human hand to evaluate their manipulation capabilities. We show that the RRs can be employed to manipulate arbitrary object shapes to provide dexterous in-hand manipulation.

Manipulation of objects within a robot's hand is one of the most important challenges in achieving robot dexterity. To address this challenge, Roller Graspers use steerable rolling fingertips. The fingertips impart motions and exert forces to achieve six degree of freedom mobility and closed-loop grasp force control. The design reported here uses image processing from cameras placed inside steerable compliant rollers to track contact conditions and locations. Integration of this data into a controller enables a variety of robust in-hand manipulation capabilities. We demonstrate that the same information can be used to reconstruct object shape. In addition, we show that by converting in-hand manipulation from a discontinuous process, with fingers frequently attaching and detaching from the object surface, to a continuous process, we can implement a convergent control loop that minimizes errors that otherwise accumulate during large object motions. The difference is apparent when comparing the results of an object rotation using a discontinuous finger-gaiting approach, as would be required without rolling fingertips, to the results obtained with continuous rolling. The results suggest that hybrid rolling fingertip and finger-gaiting approaches to manipulation may be a promising future research direction.

In this paper, we use impact-induced acceleration in conjunction with periodic stick-slip to successfully and quickly transport parts vertically against gravity. We show analytically that vertical vibratory transport is more difficult than its horizontal counterpart, and provide guidelines for achieving optimal vertical vibratory transport of a part. Namely, such a system must be capable of quickly realizing high accelerations, as well as supply normal forces at least several times that required for static equilibrium. We also show that for a given maximum acceleration, there is an optimal normal force for transport. To test our analytical guidelines, we built a vibrating surface using flexures and a voice coil actuator that can accelerate a magnetic ram into various materials to generate impacts. The surface was used to transport a part against gravity. Experimentally obtained motion tracking data confirmed the theoretical model. A series of grasping tests with a vibrating-surface equipped parallel jaw gripper confirmed the design guidelines.

Robot in-hand manipulation is an important skill for robots to carry out sophisticated tasks that require moving the grasped object within hand. In this work, we present the Roller Grasper V3, a nonanthropomorphic robot grasper with a steerable roller on each of its four fingertips, and a manipulation architecture that enables the Roller Grasper V3 to achieve full 6-DoF manipulation of the grasped object in$SE(3)$. The manipulation architecture consists of a high-level planner that searches for a feasible path with waypoints for the object to be manipulated, and a low-level control policy that is used to navigate the object in between the adjacent waypoints. The method was experimentally validated on the Roller Grasper V3 to manipulate multiple objects with different geometries and topologies.

This paper describes the development of BACH (Belt-Augmented Compliant Hand), a compliant robotic hand equipped with active surfaces. The hand can securely grasp an object using power grasp and simultaneously manipulate the grasped object. The hand consists of three identical fingers, each with an actuated timing belt wrapped around a Fin Ray based compliant finger backbone. Each finger is mounted on a compliant pivot joint allowing for further adaptability. The combination of compliant mechanisms and active surfaces allows the hand to perform dexterous in-hand manipulation with great robustness. Multiple analyses were conducted to optimize and validate the design of BACH. The hand was experimentally tested for grasping and manipulating objects of various geometries and sizes, and it demonstrated highly robust and efficient in-hand manipulation capabilities.

In this paper, we present a method to manipulate unknown objects in-hand using tactile sensing without relying on a known object model. In many cases, vision-only approaches may not be feasible; for example, due to occlusion in cluttered spaces. We address this limitation by introducing a method to reorient unknown objects using tactile sensing. It incrementally builds a probabilistic estimate of the object shape and pose during task-driven manipulation. Our approach uses Bayesian optimization to balance exploration of the global object shape with efficient task completion. To demonstrate the effectiveness of our method, we apply it to a simulated Tactile-Enabled Roller Grasper, a gripper that rolls objects in hand while collecting tactile data. We evaluate our method on an insertion task with randomly generated objects and find that it reliably reorients objects while significantly reducing the exploration time.

In this paper we present a potential energy map based approach that provides a framework for the design and control of a robotic grasper. Unlike other potential energy map approaches, our framework considers friction for a more realistic perspective on grasper performance. Our analysis establishes the importance of considering dynamically variable geometry in grasper design, namely palm width, link lengths, and transmission ratios, which are assumed to be able to change in real-time. Our analysis assumes a two-phalanx tendon-pulley underactuated grasper, but it can be extended to other underactuated mechanisms. We demonstrate the utility of these novel potential energy maps and the method used to generate them in order by showing how various design parameters impact the grasping and in-hand manipulation performance of a particular design across a range of object sizes and friction coefficients. Optimal grasping designs have palms that scale with object size and transmission ratios that scale with the coefficient of friction. Using a custom in-hand manipulation metric, we compared the in-hand manipulation capabilities of a grasper that only dynamically varied its palm size, link lengths, and transmission ratios to a grasper with a variable palm and controllable actuation efforts. The analysis revealed the advantage of dynamically variable geometry; by varying only its palm size, link lengths, and transmission ratios in real-time, safe, caged in-hand manipulation of a wide range of objects could be performed.

This paper describes the development of a novel non-anthropomorphic robot hand with the ability to manipulate objects by means of articulated, actively driven rollers located at the fingertips. An analysis is conducted and systems of equations for two-finger and three-finger manipulation of a sphere are formulated to demonstrate full six degree of freedom nonholonomic spatial motion capability. A prototype version of the hand was constructed and used to grasp and manipulate a variety of objects. Tests conducted with the prototype confirmed the validity of the mathematical analysis. Unlike conventional approaches to within-hand manipulation using legacy robotic hands, the continuous rotation capability of our rolling fingertips allows for unbounded rotation of a grasped object without the need for finger gaiting.

The ability to perform in-hand manipulation still remains an unsolved problem; having this capability would allow robots to perform sophisticated tasks requiring repositioning and reorienting of grasped objects. In this work, we present a novel non-anthropomorphic robot grasper with the ability to manipulate objects by means of active surfaces at the fingertips. Active surfaces are achieved by spherical rolling fingertips with two degrees of freedom (DoF) - a pivoting motion for surface reorientation - and a continuous rolling motion for moving the object. A further DoF is in the base of each finger, allowing the fingers to grasp objects over a range of size and shapes. Instantaneous kinematics was derived and objects were successfully manipulated both with a custom handcrafted control scheme as well as one learned through imitation learning, in simulation and experimentally on the hardware.

We explore interactions enabled by 2D spatial manipulation and self-actuation of a tabletop shape display. To explore these interactions, we developed shapeShift, a compact, high-resolution (7 mm pitch), mobile tabletop shape display. shapeShift can be mounted on passive rollers allowing for bimanual interaction where the user can freely manipulate the system while it renders spatially relevant content. shapeShift can also be mounted on an omnidirectional-robot to provide both vertical and lateral kinesthetic feedback, display moving objects, or act as an encountered-type haptic device for VR. We present a study on haptic search tasks comparing spatial manipulation of a shape display for egocentric exploration of a map versus exploration using a fixed display and a touch pad. Results show a 30% decrease in navigation path lengths, 24% decrease in task time, 15% decrease in mental demand and 29% decrease in frustration in favor of egocentric navigation.