More Like this

1. A Low-Cost Compliant Gripper Using Cooperative Mini-Delta Robots for Dexterous Manipulation

   Mannam, P.; Rudich, A.; Zhang, Kevin L.; Veloso, M.; Kroemer, O.; Temel, Z. (July 2021, Robotics science and systems)

   null (Ed.)

   Traditional parallel-jaw grippers are insufficient for delicate object manipulation due to their stiffness and lack of dexterity. Other dexterous robotic hands often have bulky fingers, rely on complex time-varying cable drives, or are prohibitively expensive. In this paper, we introduce a novel low-cost compliant gripper with two centimeter-scaled 3-DOF delta robots using off-the-shelf linear actuators and 3D-printed soft materials. To model the kinematics of delta robots with soft compliant links, which diverge from typical rigid links, we train neural networks using a perception system. Furthermore, we analyze the delta robot’s force profile by varying the starting position in its workspace and measuring the resulting force from a push action. Finally, we demonstrate the compliance and dexterity of our gripper through six dexterous manipulation tasks involving small and delicate objects. Thus, we present the groundwork for creating modular multi-fingered hands that can execute precise and low-inertia manipulations. 

   more » « less
2. Inter-finger Small Object Manipulation With DenseTact Optical Tactile Sensor

   https://doi.org/10.1109/LRA.2023.3333735  

   Do, Won Kyung; Aumann, Bianca; Chungyoun, Camille; Kennedy, Monroe (January 2024, IEEE Robotics and Automation Letters)

   The ability to grasp and manipulate small objects in cluttered environments remains a significant challenge. This letter introduces a novel approach that utilizes a tactile sensor-equipped gripper with eight degrees of freedom to overcome these limitations. We employ DenseTact 2.0 for the gripper, enabling precise control and improved grasp success rates, particularly for small objects ranging from 5 mm to 25 mm. Our integrated strategy incorporates the robot arm, gripper, and sensor to manipulate and orient small objects for subsequent classification, effectively. We contribute a specialized dataset designed for classifying these objects based on tactile sensor output and a new control algorithm for in-hand orientation tasks. Our system demonstrates 88% of successful grasp and successfully classified small objects in cluttered scenarios. 

   more » « less
3. VibTac: A High-Resolution High-Bandwidth Tactile Sensing Finger for Multi-Modal Perception in Robotic Manipulation

   https://doi.org/10.1109/TOH.2025.3561049  

   Athar, Sheeraz; Zhang, Xinwei; Ueda, Jun; Zhao, Ye; She, Yu (January 2025, IEEE Transactions on Haptics)

   Tactile sensing is pivotal for enhancing robot manipulation abilities by providing crucial feedback for localized information. However, existing sensors often lack the necessary resolution and bandwidth required for intricate tasks. To address this gap, we introduce VibTac, a novel multi-modal tactile sensing finger designed to offer high-resolution and high-bandwidth tactile sensing simultaneously. VibTac seamlessly integrates vision-based and vibration-based tactile sensing modes to achieve high-resolution and high-bandwidth tactile sensing respectively, leveraging a streamlined human-inspired design for versatility in tasks. This paper outlines the key design elements of VibTac and its fabrication methods, highlighting the significance of the Elastomer Gel Pad (EGP) in its sensing mechanism. The sensor’s multi-modal performance is validated through 3D reconstruction and spectral analysis to discern tactile stimuli effectively. In experimental trials, VibTac demonstrates its efficacy by achieving over 90% accuracy in insertion tasks involving objects emitting distinct sounds, such as ethernet connectors. Leveraging vision-based tactile sensing for object localization and employing a deep learning model for “click” sound classification, VibTac showcases its robustness in real-world scenarios. Video of the sensor working can be accessed at https://youtu.be/kmKIUlXGroo. 

   more » « less
4. Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation

   https://doi.org/10.1109/IROS47612.2022.9981692  

   Lancaster, Patrick; Gyawali, Pratik; Mavrogiannis, Christoforos; Srinivasa, Siddhartha S.; Smith, Joshua R. (October 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates. 

   more » « less
5. A Novel Variable Stiffness Compliant Robotic Gripper Based on Layer Jamming

   https://doi.org/10.1115/1.4047156  

   Gao, Yuan; Huang, Xiguang; Mann, Ishan Singh; Su, Hai-Jun (October 2020, Journal of Mechanisms and Robotics)

   Abstract In this paper, we present a novel compliant robotic gripper with three variable stiffness fingers. While the shape morphing of the fingers is cable-driven, the stiffness variation is enabled by layer jamming. The inherent flexibility makes compliant gripper suitable for tasks such as grasping soft and irregular objects. However, their relatively low load capacity due to intrinsic compliance limits their applications. Variable stiffness robotic grippers have the potential to address this challenge as their stiffness can be tuned on demand of tasks. In our design, the compliant backbone of finger is made of 3D-printed PLA materials sandwiched between thin film materials. The workflow of the robotic gripper follows two basic steps. First, the compliant skeleton is driven by a servo motor via a tension cable and bend to a desired shape. Second, upon application of a negative pressure, the finger is stiffened up because friction between contact surfaces of layers that prevents their relative movement increases. As a result, their load capacity will be increased proportionally. Tests for stiffness of individual finger and load capacity of the robotic gripper are conducted to validate capability of the design. The results showed a 180-fold increase in stiffness of individual finger and a 30-fold increase in gripper’s load capacity. 

   more » « less