Robotic hand tool use has garnered significant attention from robotics researchers, because it enhances dexterity beyond the limitations imposed by manipulators with fixed tool configurations and human-involved manual tool changes. Despite extensive research, current methodologies predominantly focus on imitating human hand trajectories, often neglecting the pivotal role of tool-environment interaction. This study addresses this gap by exploring the task of cucumber peeling as a case study to implement contact-based demonstration strategies in robotic tool use. Our approach concentrates on the subtle tool contact behaviors that manifest through contact dynamics. Specifically, we select appropriate tool stiffness for the peeling tasks, which is captured via a handheld teaching device equipped with optical tactile sensors. Subsequently, object-level stiffness control strategies are employed to emulate these behaviors using a three-fingered robotic hand. Experimental results from real-world cucumber peeling trials substantiate our methodology, illustrating that the robotic hand can adjust contact through finger movements, thereby achieving humanlike peeling efficiency without necessitating alterations to the tool structure. This study not only demonstrates the feasibility of sophisticated tool use by robotic hands, but also highlights the critical importance of integrating tactile feedback to refine interaction with the environment.

This paper addresses the scarcity of large-scale datasets for accurate object-in-hand pose estimation, which is crucial for robotic in-hand manipulation within the "Perception-Planning-Control" paradigm. Specifically, we introduce VinT-6D, the first extensive multi-modal dataset integrating vision, touch, and proprioception, to enhance robotic manipulation. VinT-6D comprises 2 million VinT-Sim and 0.1 million VinT-Real entries, collected via simulations in Mujoco and Blender and a custom-designed real-world platform. This dataset is tailored for robotic hands, offering models with whole-hand tactile perception and high-quality, well-aligned data. To the best of our knowledge, the VinT-Real is the largest considering the collection difficulties in the real-world environment so it can bridge the gap of simulation to real compared to the previous works. Built upon VinT-6D, we present a benchmark method that shows significant improvements in performance by fusing multi-modal information. The project is available at https://VinT-6D.github.io/.

Robots and prostheses are increasingly designed with curvilinear surfaces for functional, aesthetic, aerodynamic, and safety reasons. Electronic skins (e-skins) capable of sensing contact location and pressure across complex, non-developable surfaces are essential for empowering next-generation robots with tactile awareness. This will facilitate safe and natural human-machine interactions while enhancing object manipulation capabilities. Despite the evident advantages of conformal e-skins, current fabrication methods face significant challenges in realizing their full potential. In this paper, we introduce thermoforming as a technique to efficiently fabricate tactile sensitive e-skins that conform to curvilinear surfaces. The performance, repeatability and uniformity of the sensors are characterized in detail. We also present a custom calibration pipeline where accurate digital replicas of conformal e-skins are generated for use in simulations. Finally, we demonstrate the benefits of 3D e-skins in a tool manipulation task.

Tactile servoing is an effective approach to enabling robots to safely interact with unknown environments. One of the core problems in tactile servoing is to robustly converge the contact features to the desired ones via a dedicated controller. This paper proposes a Data-Driven Model Predictive Controller (DDMPC) to compute the motion command given the previous interaction experience and feature deviations in tactile space. Compared with the manually designed PID-based controller, the proposed controller depends on the sound control theory and its convergence is guaranteed from a computational perspective. It is applied to the balancing control of a rolling bottle on a robotic forearm covered by a custom tactile sensor array. The real experiment demonstrates the superior robustness of the proposed approach and shows its great potential for other tactile servoing scenarios with measurement noise, which is inevitable for current tactile sensors.

Anthropomorphic robotic arms, mimicking the structure and function of human arms, show great potential for helping people in various tedious and repetitive household tasks. However, such arms mostly consist of multiple serial links controlled independently by actuators at joints with high reduction ratios, posing challenges in household services in terms of load capacity, responsiveness, and safety. In this paper, we propose a high-performance anthropomorphic arm called TRX-Arm based on differential cable transmission, characterized by features of high dynamics, high load capacity, and inherent compliance. TRX-Arm is composed of three deferential cable-driven coupling joints and one independent roll joint. Thanks to the cable differential transmission, the joints are capable of achieving doubled torque and stiffness without replacing motors. To enhance safety in human-robot interaction, the actuators including motors, reducer, belt, and pulley are mounted at the shoulder near the base and drive the joints remotely using cables, thereby minimizing the inertia of the whole arm. The workspace of TRX-Arm has a volume of 1.56 m3, much larger than that of the human arm. Real experiments show its capabilities including high repeatability and load capacity as well as high dynamic behavior of a dual-arm robot platform built with TRX-Arms.

Objects of daily life are designed to suit the human hand. Without major modifications to these objects and our environments, robots will need end-effectors with human hand-like configuration and dexterity to efficiently operate on them. Tight integration of tactile and proprioceptive sensors are also critical to ensure robust execution of manipulation policies without sacrificing range-of-motion. Reliability is also key, and a mechanically robust, easy to repair end-effector is important to minimize downtime. To meet these challenges, we designed a 13 degree-of-freedom anthropomorphic hand with over 1000 tactile sensing elements, named TRX-Hand5. Also embedded within are positional encoders and cable tension sensors to provide proprioceptive perception. TRX-Hand5 has a novel biomimetic topology with six small posture motors in the palm to replicate the function of intrinsic hand muscles and five large power motors in the forearm to play the role of forearm flexor muscles. The whole hand weighs 2.6 kg with its dimensions comparable to those of an adult male’s hand and is capable of actuating its fingertips at over 200°/s while exerting up to 22 N of force. The system can be disassembled in modules for easy maintenance.

In conjunction with huge recent progress in cam-era and computer vision technology, camera-based sensors have increasingly shown considerable promise in relation to tactile sensing. In comparison to competing technologies (be they resistive, capacitive or magnetic based), they offer super-high-resolution, while suffering from fewer wiring problems. The human tactile system is composed of various types of mechanoreceptors, each able to perceive and process distinct information such as force, pressure, texture, etc. Camera-based tactile sensors such as GelSight mainly focus on high-resolution geometric sensing on a flat surface, and their force measurement capabilities are limited by the hysteresis and non-linearity of the silicone material. In this paper, we present a miniaturised dome-shaped camera-based tactile sensor that allows accurate force and tactile sensing in a single coherent system. The key novelty of the sensor design is as follows. First, we demonstrate how to build a smooth silicone hemispheric sensing medium with uniform markers on its curved surface. Second, we enhance the illumination of the rounded silicone with diffused LEDs. Third, we construct a force-sensitive mechanical structure in a compact form factor with usage of springs to accurately perceive forces. Our multi-modal sensor is able to acquire tactile information from multi-axis forces, local force distribution, and contact geometry, all in real-time. We apply an end-to-end deep learning method to process all the information.

Grasping of objects using multi-fingered robotic hands often fails due to small uncertainties in the hand motion control and the object's pose estimation. To tackle this problem, we propose a grasping adjustment strategy based on tactile seroving. Our technique employs feedback from a sensorized multi-fingered robotic hand to collaboratively servo the fingers and palm to achieve the desired grasp. We demonstrate the performance of our method through simulation and physical experiments by having a robot grasp different objects under conditions of variable uncertainty. The results show that our approach achieved a higher success rate and tolerated greater uncertainty than an open-looped grasp.

Reinforcement Learning (RL) methods have been widely applied for robotic manipulations via sim-to-real transfer, typically with proprioceptive and visual information. However, the incorporation of tactile sensing into RL for contact-rich tasks lacks investigation. In this paper, we model a tactile sensor in simulation and study the effects of its feedback in RL-based robotic control via a zero-shot sim-to-real approach with domain randomization. We demonstrate that learning and controlling with feedback from tactile sensor arrays at the gripper, both in simulation and reality, can enhance grasping stability, which leads to a significant improvement in robotic manipulation performance for a door opening task. In real-world experiments, the door open angle was increased by 45% on average for transferred policies with tactile sensing over those without it.

Spiking neural networks are well suited to perform time-dependent pattern recognition problems by encoding the temporal dimension in precise spike times. With an appropriate set of weights, a spiking neuron can emit precisely timed action potentials in response to spatiotemporal input spikes. However, deriving supervised learning rules for spike mapping is nontrivial due to the increased complexity. Existing methods rely on heuristic approaches that do not guarantee a convex objective function and, therefore, may not converge to a global minimum. In this paper, we present a novel technique to obtain the weights of spiking neurons by formulating the problem in a convex optimization framework, rendering it be compatible with the established methods. We introduce techniques to influence the weight distribution and membrane trajectory, and then study how these factors affect robustness in the presence of noise. In addition, we show how the existence of a solution can be determined and assess memory capacity limits of a neuron model using synthetic examples. The practical utility of our technique is further assessed by its application to gait-event detection using the experimental data.

In this brief, a neuron with nonlinear dendrites (NNLDs) and binary synapses that is able to learn temporal features of spike input patterns is considered. Since binary synapses are considered, learning happens through formation and elimination of connections between the inputs and the dendritic branches to modify the structure or morphology of the NNLD. A morphological learning algorithm inspired by the tempotron, i.e., a recently proposed temporal learning algorithm is presented in this brief. Unlike tempotron, the proposed learning rule uses a technique to automatically adapt the NNLD threshold during training. Experimental results indicate that our NNLD with 1-bit synapses can obtain accuracy similar to that of a traditional tempotron with 4-bit synapses in classifying single spike random latency and pairwise synchrony patterns. Hence, the proposed method is better suited for robust hardware implementation in the presence of statistical variations. We also present results of applying this rule to real-life spike classification problems from the field of tactile sensing.

The range of motion (ROM) in stroke patients is often severely affected. Poststroke rehabilitation is guided through the use of clinical assessment scales for the rROM. Unfortunately, these scales are not widely utilized in clinical practice as they are excessively time-consuming. Although commercial motion-capture systems are capable of providing the information required for the assessments, most systems are either too costly or lack the convenience required for assessments to be conducted on a daily basis. This paper presents the design and implementation of a smartphone-based system for automated motor assessment using low-cost off-the-shelf inertial sensors. The system was used to automate a portion of the upper-extremity Fugl-Meyer assessment (FMA), which is widely used to quantify motor deficits in stroke survivors. Twelve out of 33 items were selected, focusing mainly on joint angle measurements of the upper body. The system has the ability to automatically identify the assessment item being conducted, and calculate the maximum respective joint angle achieved. Preliminary results show the ability of this system to achieve comparable results to goniometer measurements, while significantly reducing the time required to conduct the assessments. The portability and ease-of-use of the system would simplify the task of conducting range-of-motion assessments.