27 citations · 86 across the 44 of their papers we have counts for
11 papers · 1 filter
Higher-Order Action Supervision Makes A Strong Policy Class
Peng Cheng, Yunxian Hou, Zhi Zhou +3
Modern data-driven decision-making methods, such as imitation learning (IL) and reinforcement learning (RL), have achieved great success in solving many complex tasks. However, the…
ODEWorld: A Continuous Predictive Architecture via Physical-Time Flow
Dongxiu Liu, Haoyi Niu, Peng Cheng +5
In the physical world we inhabit, space and time are fundamentally continuous. However, existing machine learning paradigms for world modeling are largely confined to discrete-time…
Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL
Ruiming Liang, Yi Zhong, Yizhen Yuan +6
Modern large language models (LLMs) are expected not just to answer correctly, but to adapt their behavior to different human values and use cases. As a result, multi-reward reinfo…
X-Tokenizer: A Multimodal Action Tokenizer for Vision-Language-Action Pretraining
Miracle Kang, Lights Shi, Lucy Liang +10
Modern Vision-Language-Action (VLA) models must bridge pretrained vision-language reasoning and precise continuous robot control. Existing action tokenizers discretize actions prim…
LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models
Rongxu Cui, Zongzheng Zhang, Jingrui Pang +11
Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strict constraints remains largely unverified. To address th…
World Value Models for Robotic Manipulation
Zhihao Wang, Jianxiong Li, Yu Cui +4
Generalist value models play a pivotal role in scaling robotic policy learning from large-scale, mixed-quality data. Mathematically, accurate value estimation demands deep temporal…
Horizon Adaptive Offline Policy Learning via Value Stitching
Kexin Zheng, Xianyuan Zhan, Xintao Yan
Learning accurate value functions plays a decisive role for reinforcement learning (RL) agents to solve long-horizon, complex tasks. Conventional temporal-difference (TD) learning…
Demystifying Action Space Design for Robotic Manipulation Policies
Yuchun Feng, Jinliang Zheng, Zhihao Wang +5
The specification of the action space plays a pivotal role in imitation-based robotic manipulation policy learning, fundamentally shaping the optimization landscape of policy learn…
Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
Yinan Zheng, Tianyi Tan, Bin Huang +11
Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving autonomous driving tasks. However, the…
Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies
Jin Li, Yue Wu, Mengsha Huang +3
Recurrent neural policies are widely used in partially observable control and meta-RL tasks. Their abilities to maintain internal memory and adapt quickly to unseen scenarios have…
Dichotomous Diffusion Policy Optimization
Ruiming Liang, Yinan Zheng, Kexin Zheng +9
Diffusion-based policies have gained growing popularity in solving a wide range of decision-making tasks due to their superior expressiveness and controllable generation during inf…