5 papers
Adaptive Expert Guidance for Efficient On-Policy Reinforcement Learning
Daniele Affinita, Ming Xu, Rudolf Reiter +2
With massively parallel simulation, on-policy Reinforcement Learning methods such as PPO have become standard in many domains. However, learning from scratch is sample-inefficient…
PACT: End-to-End Learning of Human Pose, Contacts, and Forces from Video
Rikhat Akizhanov, Yangsong Zhang, Nikolai Kaliazin +5
Human motion, environmental contacts, and interaction forces are governed by common physical laws, yet existing approaches typically separate visual pose reconstruction from contac…
GenMed: A Pairwise Generative Reformulation of Medical Diagnostic Tasks
Hantao Zhang, Weidong Guo, Yuhe Liu +5
Data-driven medical AI is traditionally formulated as a discriminative mapping from input to output via a learned function , which does not generalize well across hetero…
WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
Yi Xiao, Qilong Jia, Hang Fan +4
Many decisions in complex terrain require fast wind estimates at specific locations and heights rather than dense forecasts on fixed grids. We present WindINR, a latent-state impli…
PhysMoDPO: Physically-Plausible Humanoid Motion with Preference Optimization
Yangsong Zhang, Anujith Muraleedharan, Rikhat Akizhanov +5
Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Building on this progress, recent…