Pedro Piacenza

Sharfin Islam, Zewen Chen, Zhanpeng He et al.

Bimanual robot manipulators can achieve impressive dexterity, but typically rely on two full six- or seven- degree-of-freedom arms so that paired grippers can coordinate effectively. This traditional framework increases system complexity while only exploiting a fraction of the overall workspace for dexterous interaction. We introduce the MiniBEE (Miniature Bimanual End-effector), a compact system in which two reduced-mobility arms (3+ DOF each) are coupled into a kinematic chain that preserves full relative positioning between grippers. To guide our design, we formulate a kinematic dexterity metric that enlarges the dexterous workspace while keeping the mechanism lightweight and wearable. The resulting system supports two complementary modes: (i) wearable kinesthetic data collection with self-tracked gripper poses, and (ii) deployment on a standard robot arm, extending dexterity across its entire workspace. We present kinematic analysis and design optimization methods for maximizing dexterous range, and demonstrate an end-to-end pipeline in which wearable demonstrations train imitation learning policies that perform robust, real-world bimanual manipulation.

Pedro Piacenza, Keith Behrman, Benedikt Schifferer et al.

Despite significant advances in touch and force transduction, tactile sensing is still far from ubiquitous in robotic manipulation. Existing methods for building touch sensors have proven difficult to integrate into robot fingers due to multiple challenges, including difficulty in covering multicurved surfaces, high wire count, or packaging constrains preventing their use in dexterous hands. In this paper, we present a multicurved robotic finger with accurate touch localization and normal force detection over complex, three-dimensional surfaces. The key to our approach is the novel use of overlapping signals from light emitters and receivers embedded in a transparent waveguide layer that covers the functional areas of the finger. By measuring light transport between every emitter and receiver, we show that we can obtain a very rich signal set that changes in response to deformation of the finger due to touch. We then show that purely data-driven deep learning methods are able to extract useful information from such data, such as contact location and applied normal force, without the need for analytical models. The final result is a fully integrated, sensorized robot finger, with a low wire count and using easily accessible manufacturing methods, designed for easy integration into dexterous manipulators.

Pedro Piacenza, Sydney Sherman, Matei Ciocarlie

While tactile sensor technology has made great strides over the past decades, applications in robotic manipulation are limited by aspects such as blind spots, difficult integration into hands, and low spatial resolution. We present a method for localizing contact with high accuracy over curved, three dimensional surfaces, with a low wire count and reduced integration complexity. To achieve this, we build a volume of soft material embedded with individual off-the-shelf pressure sensors. Using data driven techniques, we map the raw signals from these pressure sensors to known surface locations and indentation depths. Additionally, we show that a finite element model can be used to improve the placement of the pressure sensors inside the volume and to explore the design space in simulation. We validate our approach on physically implemented tactile domes which achieve high contact localization accuracy ($1.1mm$ in the best case) over a large, curved sensing area ($1,300mm^2$ hemisphere). We believe this approach can be used to deploy tactile sensing capabilities over three dimensional surfaces such as a robotic finger or palm.

Pedro Piacenza, Emily Hannigan, Clayton Baumgart et al.

Traditional methods for achieving high localization accuracy on tactile sensors usually involve a matrix of miniaturized individual sensors distributed on the area of interest. This approach usually comes at a price of increased complexity in fabrication and circuitry, and can be hard to adapt to non-planar geometries. We propose a method where sensing terminals are embedded in a volume of soft material. Mechanical strain in this material results in a measurable signal between any two given terminals. By having multiple terminals and pairing them against each other in all possible combinations, we obtain a rich signal set using few wires. We mine this data to learn the mapping between the signals we extract and the contact parameters of interest. Our approach is general enough that it can be applied with different transduction methods, and achieves high accuracy in identifying indentation location and depth. Moreover, this method lends itself to simple fabrication techniques and makes no assumption about the underlying geometry, potentially simplifying future integration in robot hands.

Pedro Piacenza, Weipeng Dang, Emily Hannigan et al.

Traditional methods to achieve high localization accuracy with tactile sensors usually use a matrix of miniaturized individual sensors distributed on the area of interest. This approach usually comes at a price of increased complexity in fabrication and circuitry, and can be hard to adapt for non planar geometries. We propose to use low cost optic components mounted on the edges of the sensing area to measure how light traveling through an elastomer is affected by touch. Multiple light emitters and receivers provide us with a rich signal set that contains the necessary information to pinpoint both the location and depth of an indentation with high accuracy. We demonstrate sub-millimeter accuracy on location and depth on a 20mm by 20mm active sensing area. Our sensor provides high depth sensitivity as a result of two different modalities in how light is guided through our elastomer. This method results in a low cost, easy to manufacture sensor. We believe this approach can be adapted to cover non-planar surfaces, simplifying future integration in robot skin applications.

Pedro Piacenza, Yuchen Xiao, Steve Park et al.

Achieving high spatial resolution in contact sensing for robotic manipulation often comes at the price of increased complexity in fabrication and integration. One traditional approach is to fabricate a large number of taxels, each delivering an individual, isolated response to a stimulus. In contrast, we propose a method where the sensor simply consists of a continuous volume of piezoresistive elastomer with a number of electrodes embedded inside. We measure piezoresistive effects between all pairs of electrodes in the set, and count on this rich signal set containing the information needed to pinpoint contact location with high accuracy using regression algorithms. In our validation experiments, we demonstrate submillimeter median accuracy in locating contact on a 10mm by 16mm sensor using only four electrodes (creating six unique pairs). In addition to extracting more information from fewer wires, this approach lends itself to simple fabrication methods and makes no assumptions about the underlying geometry, simplifying future integration on robot fingers.