382 citations · 666 across the 86 of their papers we have counts for
14 papers · 1 filter
Grouped Discrete Representation for Object-Centric Learning
Rongzhen Zhao, Vivienne Wang, Juho Kannala +1
Object-Centric Learning (OCL) aims to discover objects in images or videos by reconstructing the input. Representative methods achieve this by reconstructing the input as its Varia…
Bi-Level Motion Imitation for Humanoid Robots
Wenshuai Zhao, Yi Zhao, Joni Pajarinen +1
Imitation learning from human motion capture (MoCap) data provides a promising way to train humanoid robots. However, due to differences in morphology, such as varying degrees of j…
Multi-Scale Fusion for Object Representation
Rongzhen Zhao, Vivienne Wang, Juho Kannala +1
Representing images or videos as object-level feature vectors, rather than pixel-level feature maps, facilitates advanced visual tasks. Object-Centric Learning (OCL) primarily achi…
Organized Grouped Discrete Representation for Object-Centric Learning
Rongzhen Zhao, Vivienne Wang, Juho Kannala +1
Object-Centric Learning (OCL) represents dense image or video pixels as sparse object features. Representative methods utilize discrete representation composed of Variational Autoe…
ReMoBot: Retrieval-Based Few-Shot Imitation Learning for Mobile Manipulation with Vision Foundation Models
Yuying Zhang, Wenyan Yang, Francesco Verdoja +2
Imitation learning (IL) algorithms typically distill demonstrations into parametric policies to mimic expert behavior. However, with limited data and partial observability, such as…
RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands
Yi Zhao, Le Chen, Jan Schneider +5
It has been a long-standing research goal to endow robot hands with human-level dexterity. Bi-manual robot piano playing constitutes a task that combines challenges from dynamic ta…
ROER: Regularized Optimal Experience Replay
Changling Li, Zhang-Wei Hong, Pulkit Agrawal +2
Experience replay serves as a key component in the success of online reinforcement learning (RL). Prioritized experience replay (PER) reweights experiences by the temporal differen…
Grouped Discrete Representation Guides Object-Centric Learning
Rongzhen Zhao, Vivienne Wang, Juho Kannala +1
Similar to humans perceiving visual scenes as objects, Object-Centric Learning (OCL) can abstract dense images or videos into sparse object-level features. Transformer-based OCL ha…
Probabilistic Subgoal Representations for Hierarchical Reinforcement learning
Vivienne Huiling Wang, Tinghuai Wang, Wenyan Yang +2
In goal-conditioned hierarchical reinforcement learning (HRL), a high-level policy specifies a subgoal for the low-level policy to reach. Effective HRL hinges on a suitable subgoal…
Residual Learning and Context Encoding for Adaptive Offline-to-Online Reinforcement Learning
Mohammadreza Nakhaei, Aidan Scannell, Joni Pajarinen
Offline reinforcement learning (RL) allows learning sequential behavior from fixed datasets. Since offline datasets do not cover all possible situations, many methods collect addit…
iQRL -- Implicitly Quantized Representations for Sample-efficient Reinforcement Learning
Aidan Scannell, Kalle Kujanpää, Yi Zhao +3
Learning representations for reinforcement learning (RL) has shown much promise for continuous control. We propose an efficient representation learning method using only a self-sup…
Function-space Parameterization of Neural Networks for Sequential Learning
Aidan Scannell, Riccardo Mereu, Paul Chang +3
Sequential learning paradigms pose challenges for gradient-based deep learning due to difficulties incorporating new data and retaining prior knowledge. While Gaussian processes el…
Backpropagation Through Agents
Zhiyuan Li, Wenshuai Zhao, Lijun Wu +1
A fundamental challenge in multi-agent reinforcement learning (MARL) is to learn the joint policy in an extremely large search space, which grows exponentially with the number of a…
AgentMixer: Multi-Agent Correlated Policy Factorization
Zhiyuan Li, Wenshuai Zhao, Lijun Wu +1
In multi-agent reinforcement learning, centralized training with decentralized execution (CTDE) methods typically assume that agents make decisions based on their local observation…