From the 1 of 10 linked papers with an AI index.
10 papers
Why Does Action Chunking Improve Behavioral Cloning Performance in Robotic Control?
Filippo Lazzati, Kyle Stachowicz, William Chen +3
Action chunking---predicting and executing multiple actions instead of a single action---has proven to be a critical component for learning effective robotic control policies. Howe…
Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability
Simone Drago, Marco Mussi, Leonardo Bianconi +1
The paper extends preference‑based reinforcement learning by allowing human experts to label trajectory pairs as incomparable, and introduces a Bradley‑Terry‑inspired rationality m…
Mind Your Steps: A General Learning Framework for Accurate Humanoid Foothold Tracking
Alessandro Montenegro, Shihao Li, Puze Liu +2
Enabling humanoid robots to operate in complex, dynamic environments remains a critical challenge, fundamentally limited by the ability to navigate robustly, safely, and accurately…
Reusing Trajectories in Policy Gradients Enables Fast Convergence
Alessandro Montenegro, Federico Mansutti, Marco Mussi +2
Policy gradient (PG) methods are a class of effective reinforcement learning algorithms, particularly when dealing with continuous control problems. They rely on fresh on-policy da…
Imitation Learning as Return Distribution Matching
Filippo Lazzati, Alberto Maria Metelli
We study the problem of training a risk-sensitive reinforcement learning (RL) agent through imitation learning (IL). Unlike standard IL, our goal is not only to train an agent that…
Generalizing Behavior via Inverse Reinforcement Learning with Closed-Form Reward Centroids
Filippo Lazzati, Alberto Maria Metelli
We study the problem of generalizing an expert agent's behavior, provided through demonstrations, to new environments and/or additional constraints. Inverse Reinforcement Learning…