382 citations · 666 across the 86 of their papers we have counts for
16 papers · 1 filter
V-SHARE: Provable Convergence Guarantees for Advantage-based Algorithms
Kintan Saha, Hamed Masoudi, Xinyi Sheng +3
In reinforcement learning, advantage-based value-learning methods, unlike standard Q-learning, share information across actions by decomposing the action-value function into a stat…
Why Cross-Skeleton Retargeting Is Non-Identifiable: Structural Limits of Generative Motion Models
Zhiyuan Li, Wenyan Yang, Pekka Marttinen +1
Cross-skeleton motion generation trains generative models to carry action structure and motion intention from one body to another. Yet a target motion that shows the right action h…
Does Attention-Guided Masking Really Help Object Discovery in Object-Centric Learning?
Youliang Tao, Yanhua Han, Bin Zhao +3
Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention to aggregate image features…
SUN: Reaching for Novelty in Reinforcement Learning
Wenyan Yang, Arsenii Mustafin, Dominik Baumann +2
Exploration in reinforcement learning (RL) remains a fundamental challenge. Recent goal-conditioned RL strategies (which select goals to encourage broader state coverage) have show…
Momba: Network Modernization Improves Multi-Objective Reinforcement Learning
Adam Štafa, Santeri Heiskanen, Petr Novotný +1
Recent advances in deep reinforcement learning (RL) have shown that improving neural network architectures can yield substantial gains in sample efficiency and asymptotic performan…
Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection
Hadrien Crassous, Mohamed Yassine Kabouri, Minahil Raza +2
This paper studies how to adapt a computer vision object detector to an unknown environment under both a robot navigation time and annotation budget constraint. Our approach select…
HSA: Hierarchical Slot Attention for Multi-granularity Scene-Decomposition
Neelu Madan, Rongzhen Zhao, Andreas Mogelmose +4
Slot attention is a powerful framework for object-centric learning, decomposing visual scenes into latent slots through iterative competitive attention. However, existing methods s…
Internalizing Temporal Consistency in Video Object-Centric Learning without Explicit Regularization
Rongzhen Zhao, Zhiyuan Li, Juho Kannala +1
Video Object-Centric Learning (OCL) aims to represent objects as \textit{slot} vectors and maintain their consistency across frames. Slot-Slot Contrastive (SSC) loss has become the…
Cycle Consistency in Video Object-Centric Learning
Rongzhen Zhao, Zhiyuan Li, Ruonan Wei +2
Self-supervised video Object-Centric Learning (OCL) aims to discover distinct objects and associate them across time, whereas self-supervised Multi-Object Tracking (MOT) focuses on…
Learning Multi-Timescale Abstractions for Hierarchical Combinatorial Planning
Vivienne Huiling Wang, Tinghuai Wang, Joni Pajarinen
The combination of exponentially large action spaces, stochastic dynamics, and long-horizon decision-making under limited resources makes Sequential Stochastic Combinatorial Optimi…
Rethinking Temporal Consistency in Video Object-Centric Learning: From Prediction to Correspondence
Zhiyuan Li, Rongzhen Zhao, Wenyan Yang +3
The de facto approach in video object-centric learning maintains temporal consistency through learned dynamics modules that predict future object representations, called slots. We…
Bridging the Embodiment Gap: Disentangled Cross-Embodiment Video Editing
Zhiyuan Li, Wenyan Yang, Wenshuai Zhao +4
Learning robotic manipulation from human videos is a promising solution to the data bottleneck in robotics, but the distribution shift between humans and robots remains a critical…
PAWS: Perception of Articulation in the Wild at Scale from Egocentric Videos
Yihao Wang, Yang Miao, Wenshuai Zhao +8
Articulation perception aims to recover the motion and structure of articulated objects (e.g., drawers and cupboards), and is fundamental to 3D scene understanding in robotics, sim…
Temporal Consistency Improves Generalization in Contextual Offline Meta Reinforcement Learning
Mohammadreza Nakheai, Aidan Scannell, Kevin Luck +1
Offline meta-reinforcement learning seeks to learn a policy that generalizes to new related tasks online. Context-based methods infer a task representation from transition historie…
Pareto-Conditioned Diffusion Models for Offline Multi-Objective Optimization
Jatan Shrestha, Santeri Heiskanen, Kari Hepola +3
Multi-objective optimization (MOO) arises in many real-world applications where trade-offs between competing objectives must be carefully balanced. In the offline setting, where on…
Sparsely Supervised Diffusion
Wenshuai Zhao, Zhiyuan Li, Yi Zhao +5
Diffusion models have shown remarkable success across a wide range of generative tasks. However, they often suffer from spatially inconsistent generation, arguably due to the inher…