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- 6D Pose Estimation with Correlation Fusion18
A novel method to effectively consider the correlation within and across both modalities with attention mechanism to learn discriminative and compact multi-modal features and to explore effective intra- and inter-modality fusion in 6D pose estimation is presented.
- Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning17
A learning-based sampling strategy for constrained motion planning problems by investigating the use of two well-known deep generative models, the Conditional Variational Autoencoder and theConditional Generative Adversarial Net, to generate constraint-satisfying sample configurations.
- Visual-Policy Learning Through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks14
A novel approach to enhance the generalization performance of vision-based Reinforcement Learning (RL) algorithms for robotic manipulation tasks using a technique known as knowledge distillation, in which a “teacher” policy, pre-trained with multiple camera viewpoints, guides a ‘student’ policy in learning from a single camera viewpoint.
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- Visuo-Tactile Manipulation Planning Using Reinforcement Learning with Affordance Representation2
A reinforcement learning-based motion planning framework for object manipulation which makes use of both on-the-fly multisensory feedback and a learned attention-guided deep affordance model as perceptual states is proposed.
- GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping2
An algorithm is proposed that creates a curriculum for an agent to learn multiple discrete tasks, based on clustering tasks according to their evaluation scores, that is able to learn to grasp 100% of the objects, whereas other approaches achieve at most 86% despite being given 1.5× longer training time.
- Condensed Data Expansion Using Model Inversion for Knowledge Distillation1
This work proposes a method that expands condensed datasets using model inversion, a technique for generating synthetic data based on the impressions of a pre-trained model on its training data, which demonstrates significant gains in KD accuracy compared to using condensed datasets alone and outperforms standard model inversion-based KD methods.
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- Efficient Text-to-Image Generation: An Adaptive Step Schedule Controller for Diffusion Models–
This work proposes an adaptive diffusion controller that dynamically adjusts the number of steps to generate high-quality images efficiently, without additional model training, by leveraging a mixture of step schedules with varying step sizes.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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