1 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
Gaze on the Prize: Shaping Visual Attention with Return-Guided Contrastive Learning
Andrew Lee, Ian Chuang, Dechen Gao +2
Visual Reinforcement Learning (RL) agents must learn to act based on high-dimensional image data where only a small fraction of the pixels is task-relevant. This forces agents to w…
RAMAC: Multimodal Risk-Aware Offline Reinforcement Learning and the Role of Behavior Regularization
Kai Fukazawa, Kunal Mundada, Iman Soltani
In safety-critical domains where online data collection is infeasible, offline reinforcement learning (RL) is attractive only if policies achieve high returns without catastrophic…
Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers
Ian Chuang, Jinyu Zou, Andrew Lee +2
Human vision is a highly active process driven by gaze, which directs attention to task-relevant regions through foveation, dramatically reducing visual processing. In contrast, ro…
VITA: Vision-to-Action Flow Matching Policy
Dechen Gao, Boqi Zhao, Andrew Lee +6
Conventional flow matching and diffusion-based policies sample via iterative denoising from standard noise distributions (e.g., Gaussian), and require conditioning modules to repea…
IN-RIL: Interleaved Reinforcement and Imitation Learning for Policy Fine-Tuning
Dechen Gao, Hang Wang, Hanchu Zhou +5
Imitation learning (IL) and reinforcement learning (RL) each offer distinct advantages for robotics policy learning: IL provides stable learning from demonstrations, and RL promote…