Paper-Conference

Efficient Robotic Policy Learning via Latent Space Backward Planning
Current robotic planning methods often rely on predicting multi-frame images with full pixel details. While this fine-grained approach …
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Efficient Robotic Policy Learning via Latent Space Backward Planning
Universal Actions for Enhanced Embodied Foundation Models
Training on diverse, internet-scale data is a key factor in the success of recent large foundation models. Yet, using the same recipe …
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Universal Actions for Enhanced Embodied Foundation Models
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of …
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Robo-MUTUAL: Robotic Multimodal Task Specification via Unimodal Learning
Multimodal task specification is essential for enhanced robotic performance, where Cross-modality Alignment enables the robot to …
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Data Center Cooling System Optimization Using Offline Reinforcement Learning
The recent advances in information technology and artificial intelligence have fueled a rapid expansion of the data center (DC) …
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Data Center Cooling System Optimization Using Offline Reinforcement Learning
Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
Achieving human-like driving behaviors in complex open-world environments is a critical challenge in autonomous driving. Contemporary …
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Diffusion-Based Planning for Autonomous Driving with Flexible Guidance
Skill Expansion and Composition in Parameter Space
Humans excel at reusing prior knowledge to address new challenges and developing skills while solving problems. This paradigm becomes …
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Skill Expansion and Composition in Parameter Space
Are Expressive Models Truly Necessary for Offline RL?
Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained …
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Are Expressive Models Truly Necessary for Offline RL?
Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
One important property of DIstribution Correction Estimation (DICE) methods is that the solution is the optimal stationary distribution …
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Diffusion-DICE: In-Sample Diffusion Guidance for Offline Reinforcement Learning
Instruction-Guided Visual Masking
Instruction following is crucial in contemporary LLM. However, when extended to multimodal setting, it often suffers from misalignment …
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Instruction-Guided Visual Masking