1 citations · 1 across the 5 of their papers we have counts for
5 papers
Prompt-Driven Exploration: Language as an Exploration Space for VLA Reinforcement Learning
Sunshine Jiang, John Marangola, David Zhang +6
Exploration is essential to RL since a policy cannot improve by repeatedly sampling the behaviors it already prefers. Standard methods inject stochasticity in the action space, but…
Learning from Hindsight for VLA Reinforcement Learning
Iris Xu, Sunshine Jiang, John Marangola +8
Reinforcement learning is increasingly used to fine-tune vision-language-action (VLA) models, but robot interaction is expensive and learning becomes highly sample inefficient when…
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation
Siddharth Ancha, Sunshine Jiang, Travis Manderson +4
In order to navigate safely and reliably in off-road and unstructured environments, robots must detect anomalies that are out-of-distribution (OOD) with respect to the training dat…
Streaming Flow Policy: Simplifying diffusion/flow-matching policies by treating action trajectories as flow trajectories
Sunshine Jiang, Xiaolin Fang, Nicholas Roy +3
Recent advances in diffusionflow-matching policies have enabled imitation learning of complex, multi-modal action trajectories. However, they are computationally expensive becau…
Breaking Neural Network Scaling Laws with Modularity
Akhilan Boopathy, Sunshine Jiang, William Yue +3
Modular neural networks outperform nonmodular neural networks on tasks ranging from visual question answering to robotics. These performance improvements are thought to be due to m…