Algorithms

Dichotomous Diffusion Policy Optimization
Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior …
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Dichotomous Diffusion Policy Optimization
Discrete Diffusion for Reflective Vision-Language-Action Models in Autonomous Driving
End-to-End (E2E) solutions have emerged as a mainstream approach for autonomous driving systems, with Vision-Language-Action (VLA) …
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Sample Efficient Offline RL via T-Symmetry Enforced Latent State-Stitching
Offline reinforcement learning (RL) has achieved significant progress in recent years. However, most existing offline RL methods …
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Sample Efficient Offline RL via T-Symmetry Enforced Latent State-Stitching
X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model
Successful generalist Vision-Language-Action (VLA) models rely on effective training across diverse robotic platforms with large-scale, …
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X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model
xTED: Cross-Domain Adaptation via Diffusion-Based Trajectory Editing
Reusing pre-collected data from different domains is an appealing solution for decision-making tasks, especially when data in the …
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Flow Matching-Based Autonomous Driving Planning with Advanced Interactive Behavior Modeling
Modeling interactive driving behaviors in complex scenarios remains a fundamental challenge for autonomous driving planning. …
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Towards Robust Zero-Shot Reinforcement Learning
The recent development of zero-shot reinforcement learning (RL) has opened a new avenue for learning pre-trained generalist policies …
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Uni-RL: Unifying Online and Offline RL via Implicit Value Regularization
The practical implementations of reinforcement learning (RL) often face diverse settings, such as online, offline, and …
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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