With its opposable thumb, multiple joints and gripping skin, human hands are often considered to be the pinnacle of dexterity, and many robotic hands are designed in their image. But having been shaped by the slow process of evolution, human hands are far from optimized, with the biggest drawbacks including our single, asymmetrical thumbs and attachment to arms with limited mobility. "We can easily see the limitations of the human hand when attempting to reach objects underneath furniture or behind shelves, or performing simultaneous tasks like holding a bottle while picking up a chip can," says Aude Billard, head of the Learning Algorithms and Systems Laboratory (LASA) in EPFL's School of Engineering. "Likewise, accessing objects positioned behind the hand while keeping the grip stable can be extremely challenging, requiring awkward wrist contortions or body repositioning." A team composed of Billard, LASA researcher Xiao Gao, and Kai Junge and Josie Hughes from the Computational Robot Design and Fabrication Lab designed a robotic hand that overcomes these challenges.

The use of anthropomorphic robotic hands for assisting individuals in situations where human hands may be unavailable or unsuitable has gained significant importance. In this paper, we propose a novel task called human-assisting dexterous grasping that aims to train a policy for controlling a robotic hand's fingers to assist users in grasping objects. Unlike conventional dexterous grasping, this task presents a more complex challenge as the policy needs to adapt to diverse user intentions, in addition to the object's geometry. We address this challenge by proposing an approach consisting of two sub-modules: a hand-object-conditional grasping primitive called Grasping Gradient Field (GraspGF), and a history-conditional residual policy. GraspGF learns'how' to grasp by estimating the gradient of a synthesised success grasping example set, while the residual policy determines'when' and at what speed the grasping action should be executed based on the trajectory history. Experimental results demonstrate the superiority of our proposed method compared to baselines, highlighting the user-awareness and practicality in real-world applications. The codes and demonstrations can be viewed at https://sites.google.com/view/graspgf.

Abstract--In robotic hand research, minimizing the number of actuators while maintaining human-hand-consistent dimensions and degrees of freedom constitutes a fundamental challenge. Drawing bio-inspiration from human hand kinematic configurations and muscle distribution strategies, this work proposes a novel 15-DoF dexterous robotic hand, with detailed analysis of its mechanical architecture, electrical system, and control system. The bionic hand employs a new tendon-driven mechanism, significantly reducing the number of motors required by traditional tendon-driven systems while enhancing motion performance and simplifying the mechanical structure. This design integrates five motors in the forearm to provide strong gripping force, while ten small motors are installed in the palm to support fine manipulation tasks. Additionally, a corresponding joint sensing and motor driving electrical system was developed to ensure efficient control and feedback. The entire system weighs only 1.4kg, combining lightweight and high-performance features. Through experiments, the bionic hand exhibited exceptional dexterity and robust grasping capabilities, demonstrating significant potential for robotic manipulation tasks. HE development of actuator systems with human-level dexterity presents significant challenges [1], [2], stemming from the bio-integrated nature of the human hand: it is not an isolated entity but a highly coupled system intricately connected through skeletal-muscular-neural networks to the forearm, forming a synergistic functional unit.

Abstract-- T ask-space control of robotic fingers is a critical enabler of dexterous manipulation, as manipulation objectives are most naturally specified in terms of fingertip motions and applied forces rather than individual joint angles. While task-space planning and control have been extensively studied for larger, arm-scale manipulators, demonstrations of precise task-space trajectory tracking in compact, multi-DoF robotic fingers remain scarce. In this paper, we present the physical prototyping and experimental characterization of a three-degree-of-freedom, linkage-driven, series-parallel robotic finger with analytic forward kinematics and a closed-form Jacobian. A resolved motion rate control (RMRC) scheme is implemented to achieve closed-loop task-space trajectory tracking. We experimentally evaluate the fingertip tracking performance across a variety of trajectories, including straight lines, circles, and more complex curves, and report millimeter-level accuracy. T o the best of our knowledge, this work provides one of the first systematic experimental demonstrations of precise task-space trajectory tracking in a linkage-driven robotic finger, thereby establishing a benchmark for future designs aimed at dexterous in-hand manipulation. I. INTRODUCTION Task-space control is a cornerstone of modern robotics because it allows specifying and executing motions directly in terms of end-effector positions and orientations, which are quantities most relevant to manipulation tasks. In dexterous manipulation, we are rarely interested in individual joint angles; rather, we care about applying forces, displacements, and velocities at specific points on the fingertips or the grasped object.

Abstract--Anthropomorphic underactuated hands are valued for their structural simplicity and inherent adaptability. However, the uncertainty arising from interdependent joint motions makes it challenging to capture various grasp states during hand-object interaction without increasing structural complexity through multiple embedded sensors. This motivates the need for an approach that can extract rich grasp-state information from a single sensing source while preserving the simplicity of underactuation. This study proposes an anthropomorphic underactuated hand that achieves comprehensive grasp state estimation, using only tendon-based proprioception provided by series elastic actuators (SEAs). Our approach is enabled by the design of a compact SEA with high accuracy and reliability that can be seamlessly integrated into sensorless fingers. By coupling accurate proprioceptive measurements with potential energy-based modeling, the system estimates multiple key grasp state variables, including contact timing, joint angles, relative object stiffness, and external disturbances. Finger-level experimental validations and extensive hand-level grasp functionality demonstrations confirmed the effectiveness of the proposed approach. NTHROPOMORPHIC robotic hands have been widely adopted to replicate the functionality of the human hand. Among various actuation strategies, underactuated hands are extensively employed due to their structural simplicity and adaptability to diverse object geometries [1], [2].

Biological synergies have emerged as a widely adopted paradigm for dexterous hand design, enabling human-like manipulation with a small number of actuators. Nonetheless, excessive coupling tends to diminish the dexterity of hands. This paper tackles the trade-off between actuation complexity and dexterity by proposing an anthropomorphic finger topology with 4 DoFs driven by 2 actuators, and by developing an adaptive, modular dexterous hand based on this finger topology. We explore the biological basis of hand synergies and human gesture analysis, translating joint-level coordination and structural attributes into a modular finger architecture. Leveraging these biomimetic mappings, we design a five-finger modular hand and establish its kinematic model to analyze adaptive grasping and in-hand manipulation. Finally, we construct a physical prototype and conduct preliminary experiments, which validate the effectiveness of the proposed design and analysis.

Dexterous functional tool-use grasping is essential for effective robotic manipulation of tools. However, existing approaches face significant challenges in efficiently constructing large-scale datasets and ensuring generalizability to everyday object scales. These issues primarily arise from size mismatches between robotic and human hands, and the diversity in real-world object scales. To address these limitations, we propose the ScaleADFG framework, which consists of a fully automated dataset construction pipeline and a lightweight grasp generation network. Our dataset introduce an affordance-based algorithm to synthesize diverse tool-use grasp configurations without expert demonstrations, allowing flexible object-hand size ratios and enabling large robotic hands (compared to human hands) to grasp everyday objects effectively. Additionally, we leverage pre-trained models to generate extensive 3D assets and facilitate efficient retrieval of object affordances. Our dataset comprising five object categories, each containing over 1,000 unique shapes with 15 scale variations. After filtering, the dataset includes over 60,000 grasps for each 2 dexterous robotic hands. On top of this dataset, we train a lightweight, single-stage grasp generation network with a notably simple loss design, eliminating the need for post-refinement. This demonstrates the critical importance of large-scale datasets and multi-scale object variant for effective training. Extensive experiments in simulation and on real robot confirm that the ScaleADFG framework exhibits strong adaptability to objects of varying scales, enhancing functional grasp stability, diversity, and generalizability. Moreover, our network exhibits effective zero-shot transfer to real-world objects. Project page is available at https://sizhe-wang.github.io/ScaleADFG_webpage

At only 16, Jared Lepora has also co-authored a paper. Breakthroughs, discoveries, and DIY tips sent every weekday. In October, a student presented a robotic hand made entirely from LEGOs at the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems in Hangzhou, China. Nonetheless, the 16-year-old co-authored research recently published on arXiv along with colleagues including his father Nathan Lepora, a professor of robotics and artificial intelligence at the University of Bristol. Jared used LEGO MINDSTORMS, a LEGO robotics kit, to build a LEGO version of SoftHand-A, a 3D-printed anthropomorphic robot hand introduced in an earlier study .

The human hand plays a vital role in daily life and industrial applications, yet replicating its multifunctional capabilities-including motion, sensing, and coordinated manipulation with robotic systems remains a formidable challenge. Developing a dexterous robotic hand requires balancing human-like agility with engineering constraints such as complexity, size-to-weight ratio, durability, and force-sensing performance. This letter presents Dex-Hand 021, a high-performance, cable-driven five-finger robotic hand with 12 active and 7 passive degrees of freedom (DoFs), achieving 19 DoFs dexterity in a lightweight 1 kg design. We propose a proprioceptive force-sensing-based admittance control method to enhance manipulation. Experimental results demonstrate its superior performance: a single-finger load capacity exceeding 10 N, fingertip repeatability under 0.001 m, and force estimation errors below 0.2 N. Compared to PID control, joint torques in multi-object grasping are reduced by 31.19%, significantly improves force-sensing capability while preventing overload during collisions. The hand excels in both power and precision grasps, successfully executing 33 GRASP taxonomy motions and complex manipulation tasks. This work advances the design of lightweight, industrial-grade dexterous hands and enhances proprioceptive control, contributing to robotic manipulation and intelligent manufacturing.

A robot hand built from Lego pieces by a 16-year-old and his father can grab and move objects, displaying similar qualities to a leading robotic hand. Jared Lepora, a student at Bristol Grammar School, UK, began developing the hand when he was 14 with his father, Nathan Lepora, who works at the University of Bristol. The device borrows principles from cutting-edge robotic hands, including the Pisa/IIT SoftHand, but uses only off-the-shelf parts from Lego Mindstorms, a line of educational kits for building programmable robots. "My dad's a professor at Bristol University for robotics, and I really liked the designs [of robotic hands]," says Jared. "It just inspired me to do it in an educational format and out of Lego." The hand is driven by two motors using tendons, and each of its four fingers has three joints.