8 papers
-EVA: Envision, Verify, and Act with Latent Interactive World Models
Zhenguo Sun, Yu Sun, Hande Huang +1
Embodied policies typically map current observations directly to actions, leaving candidate-action consequences implicit. World models provide predictive supervision, representatio…
Reinforcement learning in linear embedding space unlocks generalizable control across soft robot configurations
Xinglong Zhang, Cong Li, Hangjie Mo +13
Soft-bodied organisms such as octopuses and elephant trunks exhibit remarkable morphological adaptability, dynamically reconfiguring body shape and stiffness, and flexibly adjustin…
Dreaming the Unseen: World Model-regularized Diffusion Policy for Out-of-Distribution Robustness
Ziou Hu, Xiangtong Yao, Yuan Meng +2
Diffusion policies excel at visuomotor control but often fail catastrophically under severe out-of-distribution (OOD) disturbances, such as unexpected object displacements or visua…
From Flow to One Step: Real-Time Multi-Modal Trajectory Policies via Implicit Maximum Likelihood Estimation-based Distribution Distillation
Ju Dong, Liding Zhang, Lei Zhang +7
Generative policies based on diffusion and flow matching achieve strong performance in robotic manipulation by modeling multi-modal human demonstrations. However, their reliance on…
TSC: Topology-Conditioned Stackelberg Coordination for Multi-Agent Reinforcement Learning in Interactive Driving
Xiaotong Zhang, Gang Xiong, Yuanjing Wang +3
Safe and efficient autonomous driving in dense traffic is fundamentally a decentralized multi-agent coordination problem, where interactions at conflict points such as merging and…
Safe Continual Domain Adaptation after Sim2Real Transfer of Reinforcement Learning Policies in Robotics
Josip Josifovski, Shangding Gu, Mohammadhossein Malmir +5
Domain randomization has emerged as a fundamental technique in reinforcement learning (RL) to facilitate the transfer of policies from simulation to real-world robotic applications…