17 accepted papers
Characterizing the Hamiltonians of continuous-variable (CV) quantum systems remains a fundamental challenge due to the infinite-dimensional Hilbert space and the presence of unbounded operators. Existing learning protocols are often restricted to low-order Hamiltonian structures and can be sensitive…
Accurate prediction of extreme wind velocities has substantial significance in industry, particularly for the operation management of wind power plants. Although the state-of-the-art data-driven models perform well for general meteorological forecasting, they may exhibit large errors for extreme wea…
We propose MoRe-ERL, a framework that combines Episodic Reinforcement Learning (ERL) and residual learning, which refines preplanned reference trajectories into safe, feasible, and efficient task-specific trajectories. This framework is general enough to incorporate into arbitrary ERL methods and mo…
We present the B-spline Encoded Action Sequence Tokenizer (BEAST), a novel action tokenizer that encodes action sequences into compact discrete or continuous tokens using B-splines. In contrast to existing action tokenizers based on vector quantization or byte pair encoding, BEAST requires no separ…
This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redundancy of the entire system, specifically in tasks where only the relative pose of the robots is const
Robotic manipulation systems benefit from complementary sensing modalities, where each provides unique environmental information. Point clouds capture detailed geometric structure, while RGB images provide rich semantic context. Current point cloud methods struggle to capture fine-grained detail, es…
We address the problem of robotic grasping of known and unknown objects using implicit behavior cloning. We train a grasp evaluation model from a small number of demonstrations that outputs higher values for grasp candidates that are more likely to succeed in grasping. This evaluation model serves a…
Federated learning (FL) has emerged as a promising paradigm for decentralized machine learning while preserving data privacy. However, under communication constraints, the standard FL protocol faces the risk of client dropout. Although some research has focused on the risk from the perspectives of c…
Path planning in a changing environment is a challenging task in robotics, as moving objects impose time-dependent constraints. Recent planning methods primarily focus on the spatial aspects, lacking the capability to directly incorporate time constraints. In this paper, we propose a method that lev…
Electrocardiogram(ECG) reconstruction is studied to rebuild ECG from heterogeneous biosignal, such as Photoplethys-mography(PPG), to combine the diagnosis experience of ECG and the convenient collection of PPG. Heterogeneous biosignals are information-lost channels compared with ECG, so simple mappi…
Precise pick-and-place is essential in robotic applications. To this end, we define an exact training method and an iterative inference method that improve pick-and-place precision with Transporter Networks [1]. We conduct a large scale experiment on 8 simulated tasks. A systematic analysis shows, t…
With the goal of efficiently computing collisionfree robot motion trajectories in dynamically changing environments, we present results of a novel method for Heuristics Informed Robot Online Path Planning (HIRO). Dividing robot environments into static and dynamic elements, we use the static part fo…
Deep convolutional neural network has demonstrated its out-standing performance in the field of image semantic segmentation. However, the enormous computational complexity of existing high-precision networks limits the application of the model in real-time segmentation tasks. How to achieve a good t…
Channel pruning is a promising technique of model compression and acceleration because it reduces the space and time complexity of convolutional neural networks (CNNs) while maintaining their performance. In existing methods, channel pruning is performed by iterative optimization or training with sp…
Although lots of arrhythmia classification models based on deep neural networks have been proposed, most of them can only be directly applied to inputs of a fixed length, which means the raw ECG records need to be padded or truncated before being put into the model. However, this process brings two…
In this paper, we focus on the problem of online content placement with unknown content popularity in caching-enabled fog computing systems, i.e., how to decide and update cached content on resourcelimited edge fog nodes to maximize cache hit rate and minimize switching costs of content update. Face…
Existing approaches for robotic control of magnetic swarms are not capable of generating magnetic aggregates precisely in an arbitrarily specified target region in a fluidic flow environment. Such a swarm control capability is demanded by medical applications such as clinical embolization (i.e., loc…