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Found 29 papers, 6 papers with code
Bridging Model-based Safety and Model-free Reinforcement Learning through System Identification of Low Dimensional Linear Models
Furthermore, we illustrate that the found linear model is able to provide guarantees by safety-critical optimal control framework, e. g., Model Predictive Control with Control Barrier Functions, on an example of autonomous navigation using Cassie while taking advantage of the agility provided by the RL-based controller.
Bayesian Optimization Meets Hybrid Zero Dynamics: Safe Parameter Learning for Bipedal Locomotion Control
We leverage BO to learn the control parameters used in the HZD-based controller.
Teaching Robots to Span the Space of Functional Expressive Motion
As a result 1) all user feedback can contribute to learning about every emotion; 2) the robot can generate trajectories for any emotion in the space instead of only a few predefined ones; and 3) the robot can respond emotively to user-generated natural language by mapping it to a target VAD.
Vision-Aided Autonomous Navigation of Underactuated Bipedal Robots in Height-Constrained Environments
This demonstrates reliable autonomy to drive the robot to safely avoid obstacles while walking to the goal location in various kinds of height-constrained cluttered environments.
Unsupervised Domain Adaptation for the Histopathological Cell Segmentation through Self-Ensembling
Meanwhile, we present a self-ensembling model to consider the source and the target domain together as a semi-supervised segmentation task to reduce the differences of outputs.
Autonomous Navigation for Quadrupedal Robots with Optimized Jumping through Constrained Obstacles
The resulted jumping mode is utilized in an autonomous navigation pipeline that leverages a search-based global planner and a local planner to enable the robot to reach the goal location by walking.
CRT-Net: A Generalized and Scalable Framework for the Computer-Aided Diagnosis of Electrocardiogram Signals
Subsequently, a novel deep neural network, namely CRT-Net, is designed for the fine-grained and comprehensive representation and recognition of 1-D ECG signals.
Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functions
In the existing approaches, the feasibility of the optimization and the system safety cannot be enhanced at the same time theoretically.
Reinforcement Learning for Robust Parameterized Locomotion Control of Bipedal Robots
Developing robust walking controllers for bipedal robots is a challenging endeavor.
3DMNDT:3D multi-view registration method based on the normal distributions transform
Under the NDT framework, this paper proposes a novel multi-view registration method, named 3D multi-view registration based on the normal distributions transform (3DMNDT), which integrates the K-means clustering and Lie algebra solver to achieve multi-view registration.
Effective multi-view registration of point sets based on student's t mixture model
What's more, the t-distribution takes the noise with heavy-tail into consideration, which makes the proposed method be inherently robust to noises and outliers.
Tensor-based Intrinsic Subspace Representation Learning for Multi-view Clustering
It can be seen that specific information contained in different views is fully investigated by the rank preserving decomposition, and the high-order correlations of multi-view data are also mined by the low-rank tensor constraint.
Multi-view Subspace Clustering Networks with Local and Global Graph Information
Furthermore, underlying graph information of multi-view data is always ignored in most existing multi-view subspace clustering methods.
Towards Automatic Manipulation of Intra-cardiac Echocardiography Catheter
We present a simplified calibration approach for error compensation and verify with complex rotation of the catheter in benchtop and phantom experiments under varying realistic curvature conditions.
Graph Neural Networks for UnsupervisedDomain Adaptation of Histopathological ImageAnalytics
To thisend, we present a novel method for the unsupervised domain adaptationin histopathological image analysis, based on a backbone for embeddinginput images into a feature space, and a graph neural layer for propa-gating the supervision signals of images with labels.
Contrastive Learning
Histopathological Image Classification
+3
Bidirectional Loss Function for Label Enhancement and Distribution Learning
More specifically, this novel loss function not only considers the mapping errors generated from the projection of the input space into the output one but also accounts for the reconstruction errors generated from the projection of the output space back to the input one.
Robust Motion Averaging under Maximum Correntropy Criterion
Recently, the motion averaging method has been introduced as an effective means to solve the multi-view registration problem.
Generalized Label Enhancement with Sample Correlations
Recently, label distribution learning (LDL) has drawn much attention in machine learning, where LDL model is learned from labelel instances.
Consistent and Complementary Graph Regularized Multi-view Subspace Clustering
The complementary graph regularizer investigates the specific information of multiple views.
Registration of multi-view point sets under the perspective of expectation-maximization
Registration of multi-view point sets is a prerequisite for 3D model reconstruction.
CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation
Our proposed CFEA is an interactive paradigm which presents an exquisite of collaborative adaptation through both adversarial learning and ensembling weights.
Visual Space Optimization for Zero-shot Learning
The other is to optimize the visual feature structure in an intermediate embedding space, and in this method we successfully devise a multilayer perceptron framework based algorithm that is able to learn the common intermediate embedding space and meanwhile to make the visual data structure more distinctive.
Constrained Bilinear Factorization Multi-view Subspace Clustering
Multi-view clustering is an important and fundamental problem.
Feature Concatenation Multi-view Subspace Clustering
To this end, this paper proposes a novel multi-view subspace clustering approach dubbed Feature Concatenation Multi-view Subspace Clustering (FCMSC), which boosts the clustering performance by exploring the consensus information of multi-view data.
Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors
This paper proposes a global approach for the multi-view registration of unordered range scans.
Adaptive Co-weighting Deep Convolutional Features For Object Retrieval
Aggregating deep convolutional features into a global image vector has attracted sustained attention in image retrieval.
Simultaneous merging multiple grid maps using the robust motion averaging
The main idea of this approach is to recover all global motions for map merging from a set of relative motions.
An Effective Approach for Point Clouds Registration Based on the Hard and Soft Assignments
For the registration of partially overlapping point clouds, this paper proposes an effective approach based on both the hard and soft assignments.
Weighted Motion Averaging for the Registration of Multi-View Range Scans
With the overlapping percentage available, it views the overlapping percentage as the corresponding weight of each scan pair and proposes the weight motion averaging algorithm, which can pay more attention to reliable and accurate relative motions.
