43 accepted papers
Hyperspectral imaging is an advanced technique for precisely identifying and analyzing materials or objects. However, its integration with robotic grasping systems has so far been explored due to the deployment complexities and prohibitive costs. Within this paper, we introduce a novel hyperspectral…
Teleoperation presents a promising paradigm for remote control and robot proprioceptive data collection. Despite recent progress, current teleoperation systems still suffer from limitations in efficiency and ergonomics, particularly in challenging scenarios. In this paper, we propose CaFe-TeleVision…
Teleoperation presents a promising paradigm for remote control and robot proprioceptive data collection. Despite recent progress, current teleoperation systems still suffer from limitations in efficiency and ergonomics, particularly in challenging scenarios. In this paper, we propose CaFe-TeleVision
As autonomous systems such as drones, become increasingly deployed in high-stakes, human-centric domains, it is critical to evaluate the ethical alignment since failure to do so imposes imminent danger to human lives, and long term bias in decision-making. Automated ethical benchmarking of these sys…
Vision-Language-Action (VLA) models have demonstrated significant potential in real-world robotic manipulation. However, pre-trained VLA policies still suffer from substantial performance degradation during downstream deployment. Although fine-tuning can mitigate this issue, its reliance on costly d
Hyperspectral imaging is an advanced technique for precisely identifying and analyzing materials or objects. However, its integration with robotic grasping systems has so far been explored due to the deployment complexities and prohibitive costs. Within this paper, we introduce a novel hyperspectral
Minimum Variance Distortionless Response (MVDR) is a classical adaptive beamformer that theoretically ensures the distortionless transmission of signals in the target direction, which makes it popular in real applications. Its noise reduction performance actually depends on the accuracy of the noise…
Autonomous aerial vehicles can provide efficient and effective solutions for radio frequency (RF) source tracking and localizing problems with applications ranging from wildlife conservation to search and rescue operations. Existing lightweight, low-cost, bearing measurements-based methods with a si
This paper introduces an upper limb postural optimization method for enhancing physical ergonomics and force manipulability during bimanual human-robot co-carrying tasks. Existing research typically emphasizes human safety or manipulative efficiency, whereas our proposed method uniquely integrates b
Recent work has demonstrated the potential of diffusion models in robot bimanual skill learning. However, existing methods ignore the learning of posture-dependent task features, which are crucial for adapting dual-arm configurations to meet specific force and velocity requirements in dexterous bima
Open-world task planning, characterized by handling unstructured and dynamic environments, has been increasingly explored to integrate with long-horizon robotic manipulation tasks. However, existing evaluations of the capabilities of these planners primarily focus on single-arm systems in structured
Autonomous robotic assembly of flexible printed circuit (FPC) for electronic devices requires accurate characterization of contact states between terminals and slots. This task becomes particularly challenging in scenarios with limited visibility and high precision demands. While visual methods ofte
Target speech extraction remains a significant challenge in speech separation, particularly within complex multi-speaker environments where accurate isolation of the target speech and suppression of the interfering speech are crucial. Despite the progress driven by deep learning in neural network-ba…
This paper proposes a novel whole-body impedance control method for the Collaborative dUal-arm Robot manIpulator (CURI) in Human-Robot Collaboration (HRC). The method enables CURI to adapt its physical behavior to human motion while following trajectories learned from human-human demonstrations. A w
Target speaker extraction (TSE) aims to isolate a specific voice from multiple mixed speakers relying on a registerd sample. Since voiceprint features usually vary greatly, current end-to-end neural networks require large model parameters which are computational intensive and impractical for real-ti…
Diffusion probabilistic models (DPMs) have shown remarkable performance in high-resolution image synthesis but their sampling efficiency is still to be desired due to the typically large number of sampling steps. Recent advancements in high-order numerical ODE solvers for DPMs have enabled the gener…
Text-to-image generation has achieved astonishing results yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed through cumbersome prompt engineering scene layout conditioning or image editing techniques which often require hand dra…
The disposal of waste electrical and electronic equipment (WEEE) presents a sustainability challenge, particularly for waste printed circuit boards (PCBs). PCBs are challenging to sort out from other waste materials in part because traditional industrial end-effectors struggle to reliably grip these…
We revisit the well-studied image segmentation problem from a soft labeling perspective: instead of estimating integer labels per pixel indicating a finite set of classes, each pixel is assigned a real number that conveys the level of uncertainty in the estimated class label. Soft labels are useful,…
Traditional strength and conditioning training relies on the utilization of free weights, such as weighted implements, to elicit external stimuli. However, this approach poses a significant challenge when attempting to modify or adjust the loads within a single training set. This paper introduces an…
In generative modeling, numerous successful approaches leverage a low-dimensional latent space, e.g., Stable Diffusion models the latent space induced by an encoder and generates images through a paired decoder. Although the selection of the latent space is empirically pivotal, determining the optim…
Learning-based methods for robotic grasping have been shown to yield high performance. However, they rely on expensive-to-acquire and well-labeled datasets. In addition, how to generalize the learned grasping ability across different scenarios is still unsolved. In this paper, we present a novel gra…
Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics. Since learning new manipulation skills from human demonstration is an effective way for robot applications,…
Tactile sensing plays a vital role for human hand function in terms of object exploration, grasping and manipulation. Different types of tactile sensors have been designed during the past decades, which are mainly focused on either the fingertips for grasping or the upper-body for human-robot intera
Enabling household robots to passively learn task-level skills from human demonstration could substantially boost their application in daily life. In this letter, we propose a Learning from Demonstration (LfD) scheme capturing human uni/bimanual demonstrations with motion capture suit and virtual re
This letter describes an approach to achieve well-known Chinese cooking art stir-fry on a bimanual robot system. Stir-fry requires a sequence of highly dynamic coordinated movements, which is usually difficult to learn for a chef, let alone transfer to robots. In this letter, we define a canonical s
Automated data augmentation has shown superior performance in image recognition. Existing works search for dataset-level augmentation policies without considering individual sample variations, which are likely to be sub-optimal. On the other hand, learning different policies for different samples na…
A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforcing regularization in local optimization or improving the model aggregation scheme at the server. Other works also share…
Humans in contrast to robots are excellent in performing fine manipulation tasks owing to their remarkable dexterity and sensorimotor organization. Enabling robots to acquire such capabilities, necessitates a framework that not only replicates the human behaviour but also integrates the multi-sensor…
Robotic hands with anthropomorphism considerations are of prominent popularity in human-centered environment. Existing anthropomorphic robotic hands achieving part or most of human hand comparable dexterity have been applied as various robotic end-effectors and prosthetics. However, two deficiencies…
Tracking and locating radio-tagged wildlife is a labor-intensive and time-consuming task necessary in wildlife conservation. In this article, we focus on the problem of achieving embedded autonomy for a resource-limited aerial robot for the task capable of avoiding undesirable disturbances to wildli…
A robot in the future may initially has a good learning capability but an empty library of movements. It gradually enriches its library of movements through human demonstrations. Dynamic Movement Primitives (DMPs) has been proved to be an effective way to represent trajectories. Trajectories are cla…
The traditional blockchain has the shortcoming that a single-chain can only deal with one or a few specific data types. The research question of how to make blockchain be able to deal with various data types has not been well studied. In this paper, we propose a single-chain based extension model of…
To actively assist human walking and balance recovery, a unified active assistance control framework of the hip exoskeleton is proposed in this paper. At the beginning of this paper, the condition of active assistance is analyzed. And then, a novel virtual stiffness model is proposed based on the an…
In this work, we propose the Depth Generation Network (DGN) to address the problem of dense depth estimation by exploiting the variational method and the deep-learning technique. In particular, we focus on improving the feasibility of depth estimation under complex scenarios given stereo RGB images,…
The speech intelligibility index (SII) has been widely used as an objective method of predicting speech intelligibility, but its traditional form is most effective predicting speech intelligibility scores under stationary noise but not more challenging conditions (e.g., competing noise interference)…
Wearable robots aim to understand and capitalize on the increasingly coupled relationships between human and robots. To this end, the field of wearable robotics considers robotic systems that work either for rehabilitation purpose to provide therapy for persons seeking to recover their physical, soc
Reverberation, which is generally caused by sound reflections from walls, ceilings, and floors, can result in severe performance degradation of acoustic applications. Due to a complicated combination of attenuation and time-delay effects, the reverberation property is difficult to characterize, and…
Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous system has been limited due to various sources of variability in expression. Moreover, the intermingled anatomy of neurona…
In this paper we present an interactive system to enhance bilateral teleoperation through online virtual fixtures generation and task switching. This is achieved using a stereo camera system which provides accurate information of the surrounding environment of the robot and of the tasks that have to…
The 3D talking head has been developed fast, in which both external and internal articulators were demonstrated. For Mandarin pronunciation, the aspiration airflow is crucial to discriminate confusable Mandarin consonants. In this paper, we present a 3D talking head system for articulatory and aspir…
Natural image modeling plays a key role in many vision problems such as image denoising. Image priors are widely used to regularize the denoising process, which is an illposed inverse problem. One category of denoising methods exploit the priors (e.g., TV, sparsity) learned from external clean image…