3 citations · 3 across the 9 of their papers we have counts for
9 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…
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…
Reward Compatibility: A Framework for Inverse RL
Filippo Lazzati, Mirco Mutti, Alberto Metelli
We provide an original theoretical study of Inverse Reinforcement Learning (IRL) through the lens of reward compatibility, a novel framework to quantify the compatibility of a rewa…
Robustness in the Face of Partial Identifiability in Reward Learning
Filippo Lazzati, Alberto Maria Metelli
In Reward Learning (ReL), we are given feedback on an unknown target reward, and the goal is to use this information to recover it in order to carry out some downstream application…
Learning Utilities from Demonstrations in Markov Decision Processes
Filippo Lazzati, Alberto Maria Metelli
Our goal is to extract useful knowledge from demonstrations of behavior in sequential decision-making problems. Although it is well-known that humans commonly engage in risk-sensit…