18 accepted papers
Pectoral-fin-based (labriform) swimming combines high-speed propulsion with agile maneuverability through rigid-flexible fin partitioning. Inspired by this principle, we present a multi-stable soft robotic swimmer composed of two wedge-shaped bistable actuators integrated with fin-like rigid-flexibl
Exoskeletons, as wearable human–robot collaborative devices, can effectively reduce muscle fatigue caused by prolonged material handling and overhead tasks. However, most existing active exoskeletons adopt tightly coupled serial structures, which generally suffer from insufficient wearing comfort, l…
Embodied AI research is undergoing a shift toward vision-centric perceptual paradigms. While massively parallel simulators have catalyzed breakthroughs in proprioception-based locomotion, their potential remains largely untapped for vision-centric tasks due to the prohibitive computational overhead …
Graphical user interface (GUI) agents powered by large vision-language models (VLMs) have shown remarkable potential in automating digital tasks, highlighting the need for high-quality trajectory data to support effective agent training. Yet existing trajectory synthesis pipelines often yield agents
Temporal context is essential for robotic manipulation because such tasks are inherently non-Markovian, yet mainstream VLA models typically overlook it and struggle with long-horizon, temporally dependent tasks. Cognitive science suggests that humans rely on working memory to buffer short-lived repr…
Robotic manipulation requires precise spatial understanding to interact with objects in the real world. Point-based methods suffer from sparse sampling, leading to the loss of fine-grained semantics. Image-based methods typically feed RGB and depth into 2D backbones pre-trained on 3D auxiliary tasks
Inference-time alignment methods have gained significant attention for their efficiency and effectiveness in aligning large language models (LLMs) with human preferences. However, existing dominant approaches using reward-guided search (RGS) primarily rely on outcome reward models (ORMs), which suff…
GUI automation faces critical challenges in dynamic environments. MLLMs suffer from two key issues: misinterpreting UI components and outdated knowledge. Traditional fine-tuning methods are costly for app-specific knowledge updates. We propose GUI-explorer, a training-free GUI agent that incorporate…
The generation and simulation of diverse real-world scenes have significant application value in the field of autonomous driving, especially for the corner cases. Recently, researchers have explored employing neural radiance fields or diffusion models to generate novel views or synthetic data under…
Modern robots are required to operate in complex environments and perform diverse tasks, resulting in redundant degrees of freedom (DoF) for flexibility. However, managing redundancy is challenging due to the high-dimensional and non-convex nature of robotic kinematics. When executing complex tracki
The Segment Anything Model (SAM), a prompt-driven foundation model for natural image segmentation, has demonstrated impressive zero-shot performance. However, SAM is not directly applicable to medical image segmentation due to its inability to predict semantic labels, reliance on additional prompts,…
Smartphone agents are increasingly important for helping users control devices efficiently, with (Multimodal) Large Language Model (MLLM)-based approaches emerging as key contenders. Fairly comparing these agents is essential but challenging, requiring a varied task scope, the integration of agents…
Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-trained vision-language models in which the key is to adopt the image-level model for pixel-level segmentation task. In this…
Brain vessel image segmentation can be used as a promising biomarker for better prevention and treatment of different diseases. One successful approach is to consider the segmentation as an image-to-image translation task and perform a conditional Generative Adversarial Network (cGAN) to learn a tra…
The redundant manipulators' analytical solutions can be obtained by the parameterization method. Multiple parameterized joints and their corresponding parametric representations exist for a redundant manipulator. However, how to select the optimal parameterized joints has yet to be well-addressed. T…
Denoising diffusion (score-based) generative models have recently achieved significant accomplishments in generating realistic and diverse data. These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise. Unfortu…
This letter proposes a new method combined by the parameterization method and T-IK to solve the inverse kinematics problem of redundant manipulators in the position domain. T-IK is an improved multi-objective optimization algorithm based on NSGA-II. By adding population migration strategy and adapti
In this paper, a new method is proposed to solve the inverse kinematics problem of redundant manipulators. This method demonstrates superior performance on continuous motion by combining interval search genetic algorithm based on trajectory which we propose with parametric joint angle method. In thi…