4 papers
Learning More from Less: Reinforcement Learning from Hindsight
Iris Xu, Sunshine Jiang, John Marangola +8
Reinforcement learning (RL) is increasingly used to post-train vision-language-action (VLA) models, but every update consumes robot rollouts that are slow and costly to collect, ma…
Prompt-Driven Exploration
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…
DexWrist: A Robotic Wrist for Constrained and Dynamic Manipulation
Martin Peticco, Gabriella Ulloa, John Marangola +2
Development of dexterous manipulation hardware has primarily focused on hands and grippers. However, these end-effectors are often paired with bulky and highly stiff wrists that li…
Constraints on the perfect phylogeny mixture model and their effect on reducing degeneracy
John Marangola, Azadeh Sheikholeslami, José Bento
The perfect phylogeny mixture (PPM) model is useful due to its simplicity and applicability in scenarios where mutations can be assumed to accumulate monotonically over time. It is…