Engineered assembloids fabricated from tissue spheroids hold immense promise for developmental biology, disease modeling, and regenerative medicine. However, fabricating heterogeneous assembloids with precise spatial patterning remains a critical bottleneck, often reliant on manual pipetting that lacks scalability and reproducibility. While magnetic microrobotics, which transforms spheroids into controllable robots, offers a non-invasive alternative, it faces a fundamental challenge: the global input of magnetic actuation. A single command moves all robots simultaneously, leading to coupled motion and frequent assembly failures. Here, we present a collaborative control framework that overcomes this limitation by leveraging local constraints and a novel motion decoupling strategy. We reformulate the high-dimensional, coupled multi-robot planning problem into a low-dimensional aggregate space, effectively transforming the assembly task into a dynamic sequential decision problem. This framework, coupled with a graph-based dynamic path optimization algorithm, enables deterministic, collision-free assembly. Experimental validation demonstrates a 78.57% higher success rate and a 33.20% higher assembly efficiency compared to conventional strategies. This work establishes a foundational engineering principle for assembloid fabrication, transitioning the process from a qualitative experiments to a controllable and programmable engineering discipline, thereby unlocking the potential for deterministic construction of complex biological structures.

Magnetic compression anastomosis (MCA) offers a promising solution for minimally invasive anastomosis surgery. However, current MCA schemes lack safe, real-time localization and guidance for compression magnets, hindering surgeons' ability to control the compression magnets effectively in complex, unstructured endoluminal environments. To address these limitations, this paper introduces the MagsL-HUD endoscopic system, a novel solution that enables multi-magnetic six-degree-of-freedom (six-DoF) localization and head-up display (HUD) guidance within the endoscopic view (EV). Specifically, the system integrates a developed Endo-MagCap device with an orthogonal magnet configuration, along with a magnetic sensor array, to achieve real-time full-pose localization. An endoscopic camera model is incorporated for HUD visualization, enhancing intuitive interaction for surgeons' better-informed decisions. Eventually, the effectiveness of the MagsL-HUD endoscopic system is validated through laboratory experiments and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> animal trials. The system demonstrates six-DoF tracking accuracy with average errors of 0.0070 m and 0.1437 rad, and 0.0071 m and 0.1721 rad in the designed trajectory cases for two compression magnets, respectively. Additionally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> porcine tests confirm the system's feasibility and applicability, successfully performing a stomach-colon MCA surgery with a final compression gap of approximately 0.00247 m. Further comparative studies demonstrate that the MagsL-HUD method has a compression success rate of 71.4<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> versus 42.9<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> of the non-HUD approaches in the designed tests. This work represents a significant step toward the clinical adoption of magnetic-assisted endoscopy for minimally invasive anastomosis surgeries, holding substantial practical significance for improving the safety and efficacy of MCA procedures in complex, unstructured endoluminal environments.

Many robots are not equipped with a manipulator and many objects are not suitable for prehensile manipulation (such as large boxes and cylinders). In these cases, pushing is a simple yet effective non-prehensile skill for robots to interact with and further change the environment. Existing work often assumes a set of predefined pushing modes and fixed-shape objects. This work tackles the general problem of controlling a robotic fleet to push collaboratively numerous arbitrary objects to respective destinations, within complex environments of cluttered and movable obstacles. It incorporates several characteristic challenges for multi-robot systems such as online task coordination under large uncertainties of cost and duration, and for contact-rich tasks such as hybrid switching among different contact modes, and under-actuation due to constrained contact forces. The proposed method is based on combinatorial hybrid optimization over dynamic task assignments and hybrid execution via sequences of pushing modes and associated forces. It consists of three main components: (I) the decomposition, ordering and rolling assignment of pushing subtasks to robot subgroups; (II) the keyframe guided hybrid search to optimize the sequence of parameterized pushing modes for each subtask; (III) the hybrid control to execute these modes and transit among them. Last but not least, a diffusion-based accelerator is adopted to predict the keyframes and pushing modes that should be prioritized during hybrid search; and further improve planning efficiency. The framework is complete under mild assumptions. Its efficiency and effectiveness under different numbers of robots and general -shaped objects are validated extensively in simulations and hardware experiments, as well as generalizations to heterogeneous robots, planar assembly and 6D pushing.

Robotic dexterous manipulation is a challenging problem due to high degrees of freedom (DoFs) and complex contacts of multi-fingered robotic hands. Many existing deep reinforcement learning (DRL) based methods aim at improving sample efficiency in high-dimensional output action spaces. However, existing works often overlook the role of representations in achieving generalization of a manipulation policy in the complex input space during the hand-object interaction. In this paper, we propose DexRep, a novel hand-object interaction representation to capture object surface features and spatial relations between hands and objects for dexterous manipulation skill learning. Based on DexRep, policies are learned for three dexterous manipulation tasks, i.e. grasping, in-hand reorientation, bimanual handover, and extensive experiments are conducted to verify the effectiveness. In simulation, for grasping, the policy learned with 40 objects achieves a success rate of 87.9% on more than 5000 unseen objects of diverse categories, significantly surpassing existing work trained with thousands of objects; for the in-hand reorientation and handover tasks, the policies also boost the success rates and other metrics of existing hand-object representations by 20% to 40%. The grasp policies with DexRep are deployed to the real world under multi-camera and single-camera setups and demonstrate a small sim-to-real gap.

Event cameras in motion tend to detect object boundaries or texture edges, which produce lines of brightness changes, especially in man-made environments. While lines can constitute a robust intermediate representation that is consistently observed, the sparse nature of lines may lead to drastic deterioration with minor estimation errors. Only a few previous works, often accompanied by additional sensors, utilize lines to compensate for the severe domain discrepancies of event sensors along with unpredictable noise characteristics. We propose a method that can stably extract tracks of varying appearances of lines using a clever algorithmic process that observes multiple representations from various time slices of events, compensating for potential adversaries within the event data. We then propose geometric cost functions that can refine the 3D line maps and camera poses, eliminating projective distortions and depth ambiguities. The 3D line maps are highly compact and can be equipped with our proposed cost function, which can be adapted for any observations that can detect and extract line structures or projections of them, including 3D point cloud maps or image observations. We demonstrate that our formulation is powerful enough to exhibit a significant performance boost in event-based mapping and pose refinement across diverse datasets, and can be flexibly applied to multimodal scenarios. Our results confirm that the proposed line-based formulation is a robust and effective approach for the practical deployment of event-based perceptual modules. Project page

Underwater acoustic communication, characterized by limited bandwidth, high latency, and low reliability, poses significant challenges for data exchange in bathymetric collaborative simultaneous localization and mapping (CSLAM). In this paper, we introduce a novel vector quantization (VQ) method called ID(O) for mapping data compression in bathymetric CSLAM. ID(O) encodes the map into an index map (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {I}$</tex-math></inline-formula>), a central depth map (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {D}$</tex-math></inline-formula>), and an orientation map (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbb {O}$</tex-math></inline-formula>). To accommodate strict communication constraints, orientations can be partially or fully excluded from transmission, and we propose a method to estimate these orientations during map restoration. Moreover, we integrate ID(O) within a feature-based bathymetric CSLAM framework named TTT CSLAM. Extensive experiments on two large-scale sea trial datasets demonstrate that ID(O) achieves about 40<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> higher restoration accuracy than the baseline method using principal component analysis. TTT CSLAM with ID(O) can match that with lossless compression regarding mapping accuracy and efficiency, and it is robust against 40<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> packet loss and large dead reckoning drift errors across diverse environments. To the best of our knowledge, ID(O) is the first VQ method for bathymetric data compression, and TTT CSLAM with ID(O) is the first bathymetric CSLAM tested within an underwater communication network employed by acoustic modems.

Work-related musculoskeletal disorders (WMDs) affect a high percentage of operators performing repeated weight lifting and load carrying in industrial scenarios. Since upper limb muscles are affected in the process, the assistance provided by upper body exoskeletons is increasingly needed to prevent WMDs and their consequent cost to the health system. This paper presents the evaluation of Flexos, a portable, bilateral, shoulder exoskeleton prototype designed to assist logistic and industrial operators in performing occupational tasks. An in-lab assessment was conducted on twelve healthy subjects - 9 males, 3 females - to evaluate Flexos capability in assisting the user during the execution of isometric, dynamic, and carrying-load tasks. Different metrics were extracted from time-series signals to assess the effort related to five targeted muscles surrounding the shoulder complex. Despite the limited experimental size and the prototypal level of the device, Flexos managed to cover almost all the shoulders range of motion - 89.2% flexion/extension, and 88.4% internal/external rotation - and to globally decrease muscular activity in occupational activities, particularly when isometric contractions are required for a prolonged time, with average reductions of -27.2% for the static task, -18.6% for the dynamic task and -23.4% for the carrying-load task.

Quadrupedal robots hold promising potential for applications in navigating cluttered environments with resilience akin to their animal counterparts. However, their floating-base configuration makes them susceptible to real-world uncertainties, presenting substantial challenges in locomotion control. Deep reinforcement learning has emerged as a viable alternative for developing robust locomotion controllers. However, approaches relying solely on proprioception often sacrifice collision-free locomotion, as they require front-foot contact to detect stairs and adapt the gait. Meanwhile, incorporating exteroception necessitates a precisely modeled map observed by exteroceptive sensors over time. This work proposes a novel method for fusing proprioception and exteroception through a resilient multi-modal reinforcement learning framework. The proposed method yields a controller demonstrating agile locomotion on a quadrupedal robot across diverse real-world courses, including rough terrains, steep slopes, and high-rise stairs, while maintaining robustness in out-of-distribution situations.