Camera-based 3D semantic scene completion (SSC) provides dense geometric and semantic perception for autonomous driving and robotic navigation. However, existing methods rely on a coupled encoder to deliver both semantic and geometric priors, which forces the model to make a trade-off between conflicting demands and limits its overall performance. To tackle these challenges, we propose FoundationSSC, a novel framework that performs dual decoupling at both the source and pathway levels. At the source level, we introduce a foundation encoder that provides rich semantic feature priors for the semantic branch and high-fidelity stereo cost volumes for the geometric branch. At the pathway level, these priors are refined through specialised, decoupled pathways, yielding superior semantic context and depth distributions. Our dual-decoupling design produces disentangled and refined inputs, which are then utilised by a hybrid view transformation to generate complementary 3D features. Additionally, we introduce a novel Axis-Aware Fusion (AAF) module that addresses the often-overlooked challenge of fusing these features by anisotropically merging them into a unified representation. Extensive experiments demonstrate the advantages of FoundationSSC, achieving simultaneous improvements in both semantic and geometric metrics, surpassing prior bests by +0.23 mIoU and +2.03 IoU on SemanticKITTI. Additionally, we achieve state-of-the-art performance on SSCBench-KITTI-360, with 21.78 mIoU and 48.61 IoU.

Micro-expression recognition (MER) aims to uncover genuine emotions and underlying psychological states. However, existing MER methods struggle with three main challenges. 1) Scarcity of micro-expression samples. 2) Difficulty in modeling nearly imperceptible facial movements. 3) Reliance on apex frame annotations. To address these issues, we propose a Self-supervised Oriented Deformation model for Apex-free Micro-expression Recognition (SODA4MER). Our approach enhances local deformation perception using muscle-group priors and amplifies subtle features through Dynamic Stereotype Theory (DST) based enhancement, while contrastive learning eliminates the need for manual apex annotations. Specifically, the Oriented deformation estimator of SODA4MER is first pretrained in a self-supervised manner. Secondly, a Gated Temporal Variance Gaussian model (GTVG) is introduced to adaptively integrate facial muscle-group priors, enhancing local deformation perception and mitigating noise from head movements. Then, contrastive learning is employed to achieve apex detection by identifying the frame with the most significant local deformation. Finally, guided by DST, we introduced a feature enhancement strategy that models the temporal dynamics of local deformation in the activation and decay phases, leading to richer deformation features. Our rigorous experiments confirm the competitive performance and practical applicability of SODA4MER.

Safe motion planning algorithms are necessary for deploying autonomous robots in unstructured environments to prevent harm to humans and avoid damage to nearby objects. Generating these motion plans in real-time is also important to ensure that the robot can adapt to sudden changes in its environment. Many trajectory optimization methods introduce heuristics that balance safety and real-time performance, potentially increasing the risk of the robot colliding with its environment. This paper addresses this challenge by proposing Conformalized Reachable Sets for Obstacle Avoidance With Spheres (CROWS). CROWS is a novel real-time, receding-horizon trajectory planner that generates probablistically-safe motion plans. Offline, CROWS learns a novel neural network-based representation of a sphere-based reachable set that overapproximates the swept volume of the robot's motion. CROWS then uses conformal prediction to compute a confidence bound that provides a probabilistic safety guarantee on the learned reachable set. At runtime, CROWS performs trajectory optimization to select a trajectory that is probabilstically-guaranteed to be collision-free. We demonstrate that CROWS outperforms a variety of state-of-the-art methods in solving challenging motion planning tasks in cluttered environments while remaining collision-free. Code and video demonstrations can be found at https://roahmlab.github.io/crows/.

Abstract Co‐speech gesture generation, driven by emotional expression and synergistic bodily movements, is essential for applications such as virtual avatars and human‐robot interaction. Existing co‐speech gesture generation methods face two fundamental limitations: (1) producing inexpressive gestures due to ignoring the temporal evolution of emotion; (2) generating incoherent and unnatural motions as a result of either holistic body oversimplification or independent part modeling. To address the above limitations, we propose EmoDiffGes, a diffusion‐based framework grounded in embodied emotion theory, unifying dynamic emotion conditioning and part‐aware synergistic modeling. Specifically, a Dynamic Emotion‐Alignment Module (DEAM) is first applied to extract dynamic emotional cues and inject emotion guidance into the generation process. Then, a Progressive Synergistic Gesture Generator (PSGG) iteratively refines region‐specific latent codes while maintaining full‐body coordination, leveraging a Body Region Prior for part‐specific encoding and Progressive Inter‐Region Synergistic Flow for global motion coherence. Extensive experiments validate the effectiveness of our methods, showcasing the potential for generating expressive, coordinated, and emotionally grounded human gestures.

Ensuring safe, real-time motion planning in arbitrary environments requires a robotic manipulator to avoid collisions, obey joint limits, and account for uncertainties in the mass and inertia of objects and the robot itself. This paper proposes Autonomous Robust Manipulation via Optimization with Uncertainty-aware Reachability (ARMOUR), a provably-safe, receding-horizon trajectory planner and tracking controller framework for robotic manipulators to address these challenges. ARMOUR first constructs a robust controller that tracks desired trajectories with bounded error despite uncertain dynamics. ARMOUR then uses a novel recursive Newton-Euler method to compute all inputs required to track any trajectory within a continuum of desired trajectories. Finally, ARMOUR over-approximates the swept volume of the manipulator; this enables one to formulate an optimization problem that can be solved in real-time to synthesize provably-safe motions. This paper compares ARMOUR to state of the art methods on a set of challenging manipulation examples in simulation and demonstrates its ability to ensure safety on real hardware in the presence of model uncertainty without sacrificing performance. Project page:https://roahmlab.github.io/armour/.

Ultrasound images often contain much noise, and the examination process is easily affected by many factors. Therefore, it is often necessary for ultrasound surgeons to have rich experience in accurately identifying neck vascular from ultrasound images. The NVNet proposed in this paper can accurately segment neck vessels and accurately segment carotid intima-media from ultrasound images. We use an improved full-scale skip connection to obtain richer feature information from the encoder and introduce enhanced attention mechanism, making it possible for NVNet to identify neck vascular from ultrasound images containing much noise accurately. Due to the lack of available datasets, we collate an entirely new carotid longitudinal sectional ultrasound dataset and carry out data annotation under ultrasound surgeons' guidance. The experiment is carried out on the collated dataset and another public dataset of cross-sectional ultrasound images, including carotid artery and internal jugular vein. The final experimental results prove that the segmentation accuracy of NVNet exceeds that of many well-known models in recent years.

To move while using new sensor information, mobile robots use receding-horizon planning, executing a short plan while computing a new one. A plan should have dynamic feasibility (obeying a robot's dynamics and avoiding obstacles), liveness (planning frequently enough to complete tasks), and optimality (minimizing, e.g., distance to a goal). Reachability-based trajectory design (RTD) is a method to generate provably dynamically feasible plans in real time by solving a polynomial optimization program (POP) in each planning iteration. However, RTD uses a derivative-based solver, which may converge to local minima that impact liveness and optimality. This article proposes a parallel constrained Bernstein algorithm (PCBA) branch-and-bound method to optimally solve RTD's POP at runtime; the resulting optimal planner is called RTD*. The specific contributions of this article are the PCBA implementation, proofs of PCBA's bounded time and memory usage, a comparison of PCBA with state-of-the-art solvers, and a demonstration of PCBA/RTD* on hardware. RTD* shows better optimality and liveness than RTD in dozens of environments with random obstacles.