Endowing the curved surfaces of rounded vision-based tactile fingers is essential for dexterous robotic manipulation, as they offer more sufficient contact with the environment. However, current rounded designs are constrained by a low sensing frequency (30–60 Hz) and the need for recalibration when adapting to new sensors due to the reliance on multi-channel captures, which hinders their performance in dynamic robotic tasks and large-scale deployment. In this work, we introduce R-Tac0, a low-cost rounded VBTS engineered for high-resolution and high-speed perception. The key innovation is a monochrome vision-based sensing principle: utilizing a black-and-white camera to capture the reflection properties of the compound rounded elastomer under monochromatic illumination. This single-channel imaging significantly reduces data volume and simplifies computational complexity, enabling 120 Hz tactile perception. A lightweight neural network can calibrate the sensor to achieve a depth reconstruction accuracy of 0.169 mm per pixel, while exhibiting surprisingly good transferability to new sensors. In experiments, we demonstrate the advantages of R-Tac0’s rounded design by evaluating its performance under different contact angles, its high-frequency perception in slip detection, and its effectiveness in robotic dynamic pose estimation.

This paper introduces a pipeline that integrates multimodal large language models (LLMs) for tangible storytelling, featuring flexible materials generation, intuitive hands-on performance, and easy finalization. The design system enables teachers, parents, and children to create stage elements through natural language interactions and generate paper-cut style images. These elements can be easily fabricated using standard printing paper and assembled into a reconfigurable cardstock stage, allowing children to craft various plotlines through manipulation. The storytelling process can be directly recorded as a short film or transformed into an elaborate storybook using styled image filters and refining LLMs. By introducing the role of the stage in both the design and manipulation processes, this pipeline offers intuitive guidance and affordance for free but organized creation. The flexibility introduced by LLMs supports educators in diverse course design and children in self-expression. Without the requirement for specific hardware, the system also has the potential to be applied more broadly in less developed areas.

Existing motion planning approaches for over-actuated unmanned aerial vehicle (UAV) platforms can achieve online planning without considering dynamics. However, in many envisioned application areas such as aerial manipulation, payload delivery, and moving target tracking, it is critical to ensure dynamic consistency in the generated trajectory. The dynamics of these platforms introduce a high nonlinearity, leading to a substantial increase in computational burden. This paper presents an efficient method to plan motions that are consistent with the dynamics of over-actuated UAVs. With a hierarchical control structure, the dimension of the optimization problem is greatly reduced with synthesized wrench commands. Additionally, by exploring the dynamics of over-actuated UAVs, the complex planning process is decoupled into two simpler sub-problems. As a result, the proposed planner can be solved as two small quadratic programmings (QPs) and deployed in real-time. The computational efficiency and dynamic consistency of the proposed method are verified through both simulations and experiments, including comparison with other approaches and dynamic target tracking.

This paper presents a Genetic Algorithm (GA) designed to reconfigure a large group of modular Unmanned Aerial Vehicles (UAVs), each with different weights and inertia parameters, into an over-actuated flight structure with improved dynamic properties. Previous research efforts either utilized expert knowledge to design flight structures for a specific task or relied on enumeration-based algorithms that required extensive computation to find an optimal one. However, both approaches encounter challenges in accommodating the heterogeneity among modules. Our GA addresses these challenges by incorporating the complexities of over-actuation and dynamic properties into its formulation. Additionally, we employ a tree representation and a vector representation to describe flight structures, facilitating efficient crossover operations and fitness evaluations within the GA framework, respectively. Using cubic modular quadcopters capable of functioning as omnidirectional thrust generators, we validate that the proposed approach can (i) adeptly identify suboptimal configurations ensuring both over-actuation and trajectory tracking accuracy and (ii) significantly reduce computational costs compared to traditional enumeration-based methods.

Vision-based Tactile Sensors (VBTSs) show significant promise in that they can leverage image measurements to provide high-spatial-resolution human-like performance. However, current VBTS designs, typically confined to the fingertips of robotic grippers, prove somewhat inadequate, as many grasping and manipulation tasks require multiple contact points with the object. With an end goal of enabling large-scale, multi-surface tactile sensing via VBTSs, our research (i) develops a synchronized image acquisition system with minimal latency, (ii) proposes a modularized VBTS design for easy integration into finger phalanges, and (iii) devises a zero-shot calibration approach to improve data efficiency in the simultaneous calibration of multiple VBTSs. In validating the system within a miniature 3-fingered robotic gripper equipped with 7 VBTSs we demonstrate improved tactile perception performance by covering the contact surfaces of both gripper fingers and palm. Additionally, we show that our VBTS design can be seamlessly integrated into various end-effector morphologies significantly reducing the data requirements for calibration.

Generative models have revolutionized the field of art and design, providing an emerging and accessible approach to creating diverse artwork. However, effectively utilizing these models still requires significant expertise, making the system inaccessible to novice users, such as young children. This paper introduces a novel approach to artwork generation, combining tangible elements with AI generative models, resulting in a more engaging and immersive learning experience. With materials prepared by teachers, students can easily create digital artwork by manipulating tangible building blocks. The experiments demonstrate that the proposed pipeline can be applied to various scenarios, using either off-the-shelf or carefully designed tangible elements. This approach provides an interdisciplinary learning platform for arts and design education, fostering creativity and exploration of various art styles and design topics.

We investigate the sequential manipulation planning problem for unmanned aerial manipulators (UAMs). Unlike prior work that primarily focuses on one-step manipulation tasks, sequential manipulations require coordinated motions of a UAM's floating base, the manipulator, and the object being manipulated, entailing a unified kinematics and dynamics model for motion planning under designated constraints. By leveraging a virtual kinematic chain (VKC)-based motion planning framework that consolidates components' kinematics into one chain, the sequential manipulation task of a UAM can be planned as a whole, yielding more coordinated motions. Integrating the kinematics and dynamics models with a hierarchical control framework, we demonstrate, for the first time, an over-actuated UAM achieves a series of new sequential manipulation capabilities in both simulation and experiment.

This paper presents a novel modular robot system that can self-reconfigure to achieve omnidirectional movements for collaborative object transportation. Each robotic module is equipped with a steerable omni-wheel for navigation and is shaped as a regular icositetragon with a permanent magnet installed on each corner for stable docking. After aggregating multiple modules and forming a structure that can cage a target object, we have developed an optimization-based method to compute the distribution of all wheels' heading directions, which enables efficient omnidirectional movements of the structure. By implementing a hierarchical controller on our prototyped system in both simulation and experiment, we validated the trajectory tracking performance of an individual module and a team of six modules in multiple navigation and collaborative object transportation settings. The results demonstrate that the proposed system can maintain a stable caging formation and achieve smooth transportation, indicating the effectiveness of our hardware and locomotion designs.

Human-computer symbiosis is a crucial direction for the development of artificial intelligence. As intelligent systems become increasingly prevalent in our work and personal lives, it is important to develop strategies to support users across physical and virtual environments. While technological advances in personal digital devices, such as personal computers and virtual reality devices, can provide immersive experiences, they can also disrupt users’ awareness of their surroundings and enhance the frustration caused by disturbances. In this paper, we propose a joint observation strategy for artificial agents to support users across virtual and physical environments. We introduce a prototype system, neighbor-environment observer (NEO), that utilizes non-invasive sensors to assist users in dealing with disruptions to their immersive experience. System experiments evaluate NEO from different perspectives and demonstrate the effectiveness of the joint observation strategy. A user study is conducted to evaluate its usability. The results show that NEO could lessen users’ workload with the learned user preference. We suggest that the proposed strategy can be applied to various smart home scenarios.

Tracking position and orientation independently affords more agile maneuver for over-actuated multirotor Unmanned Aerial Vehicles (UAVs) while introducing undesired downwash effects; downwash flows generated by thrust generators may counteract others due to close proximity, which significantly threatens the stability of the platform. The complexity of modeling aerodynamic airflow challenges control algorithms from properly compensating for such a side effect. Leveraging the input redundancies in over-actuated UAVs, we tackle this issue with a novel control allocation framework that considers downwash effects and explores the entire allocation space for an optimal solution. This optimal solution avoids downwash effects while providing high thrust efficiency within the hardware constraints. To the best of our knowledge, ours is the first formal derivation to investigate the downwash effects on over-actuated UAVs. We verify our framework on different hardware configurations in both simulation and experiment.

This paper presents LinkBricks, a creative construction kit for intuitively creating and programming interactive robots towards young children. Integrating building blocks, a hierarchical programming framework and a tablet application, this kit is proposed to maintain the low floor and wide walls for children who lack knowledge in conventional programming. The blocks have LEGO-compatible interlock structures and are embedded with various wireless sensors and actuators to create different interactive robots. The programming application is easy-to-use and provides heuristics to involve children in the creative activities. A preliminary evaluation is conducted and indicates that LinkBricks increases young children's engagement with, comfort with, and interest in working with interactive robots. Meanwhile, it has the potential of helping them to learn the concepts of programming and robots.

This paper presents WalkingBot, a modular robot system that allows non-expert users to build a multi-legged robot in various morphologies using a set of building blocks with sensors and actuators embedded. The kinematic model of the built robot is interpreted automatically and revealed in a customized GUI through an integrated hardware and software design, so that users can understand, control, and program the robot easily. A Model Predictive Control (MPC) scheme is introduced to generate a control policy for various motions (e.g. moving forward, turning left) corresponding to the sensed robot structure, affording rich robot motions right after assembling. Targeting different levels of programming skill, two programming methods, visual block programming and events programming, are also presented to enable users to create their own interactive legged robot.

The use of armatures is a convenient way of deforming and animating 3D digital models. However, interact with an armature is usually time-consuming, and often requires professional skills. Tangible interfaces, such as building blocks, while having improved the accessibility of digital construction, are still lacking in flexibility and present difficulties in dealing with curved armatures. This paper introduces TwistBlocks, a pluggable and twistable modular TUI that improves the accessibility of 3D modeling and animating by physical armature interaction. TwistBlocks is capable of creating complex armatures with dense branches, and supports a high DOF (Degree of Freedom) in physical manipulation. In addition, a set of software tools are provided for novice users to easily create, rig, and animate models. The global-posture sensing network sensing scheme can also measure the rotation and movement of the physical armature, and enables interaction between multiple models.