ICRA 2024 Papers

Current multi-modal hybrid robots with flight and wheeled modes have fallen into the dilemma that they can only avoid obstacles by re-taking off when encountering obstacles due to the poor performance of wheeled obstacle-crossing. To tackle this problem, this paper presents a novel multi-modal hybrid robot with the ability to actively adjust the wheel’s size, which is inspired by the behavior of the turtle’s legs when it encounters obstacles, to enhance the traversal performance. In detail, we describe the hardware design that allows the robot to achieve a modal switch between flight and wheeled modes through foldable structures and variable wheel diameters; then, we present the architecture to control these two morphing mechanisms. After that, we establish the theoretical kinematic models for both the foldable arm and variable wheel and carry out extensive experiments to test the performance of the foldable arm, the variable-diameter wheel, as well as the traversal performance of the robot. Experimental results show that the proposed multimodal robot can realize the function of a quadrotor, respond quickly with full-scale folding within 0.9 s, climb a maximum slope of 36°, and traverse narrow passageways, which exhibit superior mobility and environmental adaptability.

Shape-changing displays enable real-time visualization and haptic exploration of 3D surfaces. However, many shape-changing displays are composed of individually actuated rigid bodies, which makes them both mechanically complex and unable to form smooth surfaces. In this work, we build a multi-stable curved line display inspired by physical splines. By using circular splines to initialize a discrete elastic rods simulator, we can model multiple stable shapes that fit specific boundary conditions. We then generate actuation instructions based on the circular spline initialization to drive the physical display. We demonstrate our display’s ability to create 16 shapes with 8 different boundary conditions. Our display is consistent in shape output, with an average standard deviation in height of 0.75 mm or 0.47% of the display’s maximum vertical range. We also show that our model is consistent with our display, with a mean RMSE of 6.68 mm or 3.85% of the display’s maximum vertical range for shapes we could stably simulate. We then demonstrate potential scalability by simulating a multi-segment version of the system and show the display’s ability to withstand loads during contour following in haptic exploration.

This study proposes a novel movable microfluidic chip in which a microfluidic chip is integrated into a robotic manipulator for manipulating oocytes. The microfluidic device has the ability to release a single oocyte with a gap effect. The robotic manipulator can control the position of the microfluidic chip. The microfluidic chip with a pipette tip is directly fabricated using 3D printing. Xenopus oocyte was used in the experiment. When oocytes move from the back side of the channel to the front side, they generate gaps between each other. The gap distance can reach about 16 times the diameter of the oocyte. In addition, a capacitive sensor was used to detect oocytes in the manipulation processes. The results showed that oocytes were successfully released one by one with no deformation in shape using the movable microfluidic chip. The method has significant advantages in biomedicine engineering and micro-nano-manipulation.

Stability and reliable operation under a spectrum of environmental conditions is still an open challenge for soft and continuum style manipulators. The inability to carry sufficient load and effectively reject external disturbances are two drawbacks which limit the scale of continuum designs, preventing widespread adoption of this technology. To tackle these problems, this work details the design and experimental testing of a modular, tendon driven bead-style continuum manipulator with tunable stiffness. By embedding the ability to independently control the stiffness of distinct sections of the structure, the manipulator can regulate it’s posture under greater loads of up to 1kg at the end-effector, with reference to the flexible state. Likewise, an internal routing scheme vastly improves the stability of the proximal segment when operating the distal segment, reducing deviations by at least 70.11%. Operation is validated when gravity is both tangential and perpendicular to the manipulator backbone, a feature uncommon in previous designs. The findings presented in this work are key to the development of larger scale continuum designs, demonstrating that flexibility and tip stability under loading can co-exist without compromise.

The standard quadrotor is one of the most popular and widely used aerial vehicle of recent decades, offering great maneuverability with mechanical simplicity. However, the under-actuation characteristic limits its applications, especially when it comes to generating desired wrench with six degrees of freedom (DOF). Therefore, existing work often compromises between mechanical complexity and the controllable DOF of the aerial system. To take advantage of the mechanical simplicity of a standard quadrotor, we propose a modular aerial system, IdentiQuad, that combines only homogeneous quadrotor-based modules. Each IdentiQuad can be operated alone like a standard quadrotor, but at the same time allows task-specific assembly, increasing the controllable DOF of the system. Each module is interchangeable within its assembly. We also propose a general controller for different configurations of assemblies, capable of tolerating rotor failures and balancing the energy consumption of each module. The functionality and robustness of the system and its controller are validated using physics-based simulations for different assembly configurations.

This paper reports a new hierarchical architecture for modeling autonomous multi-robot systems (MRSs): a nonlinear dynamical opinion process is used to model high-level group choice, and multi-objective behavior optimization is used to model individual decisions. Using previously reported theoretical results, we show it is possible to design the behavior of the MRS by the selection of a relatively small set of parameters. The resulting behavior - both collective actions and individual actions - can be understood intuitively. The approach is entirely decentralized and the communication cost scales by the number of group options, not agents. We demonstrated the effectiveness of this approach using a hypothetical ‘explore-exploit-migrate’ scenario in a two hour field demonstration with eight unmanned surface vessels (USVs). The results from our preliminary field experiment show the collective behavior is robust even with time-varying network topology and agent dropouts.

In robotic-assisted minimally invasive surgery, the remote center of motion (RCM) achieves precision and safe manipulation of surgical devices through the insertion point into the patient’s body. One of the RCM configurations, one-rotation and one-translation (1R1T) RCM based on a closed-loop design, enables two-degrees-of-freedom transmission from the proximal end of the robotic arm to the distal end. This feature offers important advantages, particularly in enhancing safety by minimizing physical contact risks with patients or other surgical tools owing to the simplified layout near the surgical field. However, conventional 1R1T RCM robots typically employ complex structures with numerous joints consisting of pin-and-hole mating mechanisms. This complexity can increase the overall size of the robot and compromise motion precision. This study presents a miniature 1R1T precision manipulator with RCM, ORIGANOID (dimensions: W60 x D120 x H30 mm, weight: 12.6 g). The robotic arm features flexure hinges, eliminates clearance issues, and can be fabricated using an origami-inspired robotic approach. Furthermore, using a novel backlash-free coupling method, the robotic arm could be easily attached and detached from the drive units. A prototype was fabricated and experimentally validated. The results demonstrated that high-resolution motion could be achieved within 10 μm. Furthermore, a demonstration using an eyeball model confirmed the successful implementation of 1R1T RCM.

This paper introduces a method that enables the parallel independent execution of trajectories for multi-robot / multi-arm systems in a shared workspace in MoveIt2. The proposed method leverages a centralized scheduler in a distributed set up to prevent collisions while the robots move independently. We argue that this approach is better suited than the state of the art (i.e., synchronous execution) for flexible/adaptive robotic tasks where the actions to be performed may vary in planning and execution time depending on sensor data (e.g., pick and place with inspection, assembly) as it is able to reduce the total execution time w.r.t. current approaches leveraging a single arm or multiple arms with synchronous motion planning.

Flapping wing air vehicles(FWAV) or ornithopters are bio-inspired aerial robots that mimic the flying principles of insects and birds. Autonomous take-off is an important capability for FWAV to enhance its performance and extend its working time, which is equipped by almost every kind of bird. As a common method of take-off for birds, jumping take-off has a great ability to adapt to different terrain and high energy efficiency compared with running and rotor-based take-off. Despite recent research, there is no FWAV capable of jumping take-off to this day. In this paper, we present a process to realize the jumping take-off of a meter-scale FWAV from flat ground. To lower the mechanical complexity, we eliminate the design of traditional robotic legs. Instead, we realize steady standing through a tripod-like structure that consists of two wings and a jumping mechanism. Two flapping wings are directly driven by two independent servos. Three carbon fiber springs are employed to build a lightweight jumping module with high elastic energy. We build the dynamic model to analyze the aerodynamic effect during the jumping phase and realize a stable transition to flapping flight. This work lays the foundation for outdoor flight without human assistance.

Despite outstanding semantic scene segmentation in closed-worlds, deep neural networks segment novel instances poorly, which is required for autonomous agents acting in an open world. To improve out-of-distribution (OOD) detection for segmentation, we introduce a metacognitive approach in the form of a lightweight module that leverages entropy measures, segmentation predictions, and spatial context to characterize the segmentation model’s uncertainty and detect pixel-wise OOD data in real-time. Additionally, our approach incorporates a novel method of generating synthetic OOD data in context with in-distribution data, which we use to fine-tune existing segmentation models with maximum entropy training. This further improves the metacognitive module’s performance without requiring access to OOD data while enabling compatibility with established pre-trained models. Our resulting approach can reliably detect OOD instances in a scene, as shown by state-of-the-art performance on OOD detection for semantic segmentation benchmarks.

Magnetic continuum robots (MCR) have shown great potential in minimally invasive interventions because they can be actively and remotely navigated through complex in vivo environments. However, the deformation capability of current MCRs is limited by fixed magnetization congurations, preventing them from accessing hard-to-reach areas. This is due to the fact that under a global magnetic field, fixed magnetization conguration causes the magnets on the MCRs exposed to coupled magnetic forces and torques, resulting in a lack of controllable degrees of freedom. Here, we introduce a reprogrammable magnetic continuum robot (RMCR) enabled by magnetic reprogramming modules (MRM). Actuated by shape memory alloys, the magnetic moment direction of MRMs can be selectively reprogrammed in real-time and in-situ. Magnetic reprogramming capabilities enable the RMCR to achieve complex shape transformations. Results show that the range of motion in the tip direction of the RMCR increases by 193% compared with regular MCR. Besides, MRMs on the RMCR can achieve active attraction and separation under simple magnetic fields. The reprogramming process of the RMCR is theoretically investigated. A design methodology for MRMs is then proposed and the fabrication process of RMCR is described in detail. Furthermore, a kinematic model of the RMCR is established, simulated, and experimentally validated.

Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that leverages learning-based switching between multiple backup controllers to formally guarantee safety under bounded control inputs while satisfying driver intention. By leveraging backup controllers designed to uphold safety and input constraints, backup control barrier functions (BCBFs) construct implicitly defined control invariant sets via a feasible quadratic program (QP). However, BCBF performance largely depends on the design and conservativeness of the chosen backup controller, especially in our setting of human-driven vehicles in complex, e.g, off-road, conditions. While conservativeness can be reduced by using multiple backup controllers, determining when to switch is an open problem. Consequently, we develop a broadcast scheme that estimates driver intention and integrates BCBFs with multiple backup strategies for human-robot interaction. An LSTM classifier uses data inputs from the robot, human, and safety algorithms to continually choose a backup controller in real-time. We demonstrate our method’s efficacy on a dualtrack robot in obstacle avoidance scenarios. Our framework guarantees robot safety while adhering to driver intention.

This paper presents a large-scale climbing robot that employs a parallel mechanism with three translational degrees of freedom as its locomotion method. Using a robot frame having a triangular pyramid shape, the robot provides a good stability during the locomotion and task execution. Three suction cups, called the perimeter cups, are attached to the vertices of the robot’s pyramid base, whereas three other suction cups called the middle cups, are attached to the end-effector of the parallel mechanism. The climbing motion is made by attaching and releasing the perimeter and middle cups one after another. The synchronization between the parallel mechanism’s motion and the suction cups during locomotion, as well as the improved gait trajectory, was established to ensure successful climbing. The control scheme of the robot integrates the servo control, the suction control, and the application control in a modular fashion. The successful climbing of the robot proves the scalability of the proposed climbing robot using active suction cups with an optimized design. Finally, a painting application was presented to demonstrate the robot’s capability to perform a wall painting task.

Tactile sensing plays a critical role in enabling robots to interact safely with target objects in dynamic and unstructured environments. While various tactile sensors based on different sensing principles or different sensitive materials have been proposed, the development of flexible large-area tactile sensors for robots is still challenging. In this paper, a novel highly sensitive piezoresistive sponge based on multi-walled carbon nanotubes (MWCNTs) and polyurethane (PU) sponge is fabricated for pressure sensing. The sensing behavior of the piezoresistive sponge was experimentally evaluated, showing high sensitivity and fast response. Based on the piezoresistive sponge, a flexible large-area tactile sensor is designed for distributed force detection with electrical resistance tomography technology. The sensing performance of the sensor is validated by touch location, sensitivity analysis, real-time touch discrimination, and touch modality recognition. The experimental results indicate that the sensor performs well in detecting the position and force of contact in a large area. The sensor’s performance shows promise in embodied tactile sensing and human–robot interaction.

This paper reports a numerical method for modeling underwater vehicle (UV) interactions with the free surface using a finite-dimensional dynamical plant model. Although finite-dimensional plant models of fully submerged UV behavior are well-established, they are unable to model the ubiquitous condition of a UV operating at or near the free surface. We report a Monte Carlo-based hybrid model approach for calculating the buoyancy and righting moment of a partially or fully submerged UV in order to model interactions with the free surface. We also report a preliminary evaluation of the hybrid model in numerical simulations, comparing the hybrid model’s performance to that of a model for fully submerged UVs and to the experimentally observed behavior of an actual vehicle while fully submerged and while interacting with the free surface. The results of this preliminary study suggest that the proposed hybrid approach may offer a simple and practical method for modeling UV behavior when submerged or interacting with the free surface.

Prior robot-assisted cranium-milling studies only considered controlling the force in the skull’s vertical direction and neglected the milling cutter’s feed force. Additionally, achieving stable force control in multiple directions is challenging for robots due to the uneven skull surface. Here a hybrid admittance control algorithm incorporating a model-free adaptive nonlinear force control and fuzzy control algorithms is proposed to accomplish effective automatic cranial-milling tasks. First, a pure data-driven model-free adaptive control method based on partial form dynamic linearization is used to control the feed force. Second, fuzzy control minimizes the total error of both the vertical and feed force by adaptively adjusting the milling cutter’s velocity and position. 42 ex vivo animal skull-milling experiments conducted by the automatic robotic cranium-milling system indicate that when using the proposed control algorithm, the force error percentage can be maintained below 5.0% within 3 s and the maximal root mean square error percentages for vertical and feed force are 1.85% and 1.94%, respectively. Moreover, no instances of dura mater damage are observed and the robotic system exhibits a high level of autonomy as it performs the skull milling task with minimal human involvement throughout the entire experiment. The results suggest the potential for advancing the intelligence level of neurosurgery in the future.

Using tactile signal is a natural way to perceive potential dangers and safeguard robots. One possible method is to use full-body tactile sensors on the robot and perform safety maneuvers when dangerous stimuli are detected. In this work, we proposed a method based on full-body tactile sensors that operates at three different levels of granularity to ensure that robot interacts with the environment safely. The results showed that our system dramatically reduced the overall collision chance compared with several baselines, and intelligently handled current collisions. Our proposed framework is generalizable to a wide variety of robots, enabling them to predict and avoid dangerous collisions and reactively handle accidental tactile stimuli.

There are many instances of helical mechanisms that are used to efficiently grasp different objects with various shapes and sizes in nature. Inspired by the helical grasping in the nature, we proposed a helical bistable soft gripper with high load capacity and energy saving. An off-the-shelf bistable steel shell (BSS) as the stiff element was inserted into a 3D printing soft helical exoskeleton to achieve coiling around and holding the objects without energy consumption. Two air pouches were designed as the actuator to control the transition between the two stable states. To facilitate gripper design, a simplified model of the gripper was conducted, and the geometric parameters of the gripper are listed in a table for reference. The transition pressures between the two stable states were experimentally characterized. Moreover, we conduct experiments to demonstrate the capability of the gripper in two working modes. The gripper exhibits coiling diameters ranging between 40 mm and 60 mm and is successfully attached to various slender objects of different geometries with a maximum holding force of 92.67 N (up to 135.1 times of its mass) in hanging mode. Finally, the gripper was integrated into a robot arm and successfully grasped different objects, and the maximum grasping weight is 221.6 g in the grasping mode.

Visual topic modeling (VTM) provides key insight into data sets based on learned semantic topic models. The Gaussian-Dirichlet Random Field (GDRF), a state-of-the-art VTM technique, models these semantic topics in continuous space as densities. However, ambiguity in learned topics is a disadvantage of such Dirichlet-based VTM algorithms. We propose the Guided Gaussian-Dirichlet Random Field (GGDRF). Our method applies Dirichlet Forest priors from natural language processing (NLP) to the vision domain as a way to embed visual scientific knowledge into the estimation process. This modification and addition to the GDRF provides a key shift from unsupervised machine learning to semi-supervised machine learning in the robotic VTM domain. We show through simulation and real-world underwater data that the proposed GGDRF outperforms the previous GDRF method both quantitatively and qualitatively by improving alignment between estimated topics and scientific interests.