5 citations · 5 across the 10 of their papers we have counts for
10 papers
ASK in the Dark: Uncertainty-Gated LLM Assistance under Partial Observability
Juarez Monteiro, Nathan Gavenski, Guilherme Lima +3
Reinforcement learning agents operating under partial observability must act on incomplete information, making them natural candidates for guidance from small language models (SLMs…
When in Doubt, Plan It Out: Committed Small Language Model Deliberation for Reactive Reinforcement Learning
Nathan Gavenski, Juarez Monteiro, Francisco Galuppo +2
Reinforcement Learning (RL) policies often degrade in unfamiliar environments because they lack explicit deliberation. We propose Plan, Align, Commit, Think (PACT), a hybrid archit…
Zero-Shot Goal Recognition with Large Language Models
Kin Max Piamolini Gusmão, Nathan Gavenski, Nir Oren +1
Large language models have recently reached near-parity with classical planners on well-known planning domains, yet this competence relies on world-knowledge exploitation rather th…
Online Goal Recognition using Path Signature and Dynamic Time Warping
Douglas Tesch, Nathan Gavenski, Leonardo Amado +2
Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Recent work addresses these chal…
When to ASK: Uncertainty-Gated Language Assistance for Reinforcement Learning
Juarez Monteiro, Nathan Gavenski, Gianlucca Zuin +1
Reinforcement learning (RL) agents often struggle with out-of-distribution (OOD) scenarios, leading to high uncertainty and random behavior. While language models (LMs) contain val…
Beyond Mimicry: Toward Lifelong Adaptability in Imitation Learning
Nathan Gavenski, Felipe Meneguzzi, Odinaldo Rodrigues
Imitation learning stands at a crossroads: despite decades of progress, current imitation learning agents remain sophisticated memorisation machines, excelling at replay but failin…
Towards Generalisable Imitation Learning Through Conditioned Transition Estimation and Online Behaviour Alignment
Nathan Gavenski, Matteo Leonetti, Odinaldo Rodrigues
State-of-the-art imitation learning from observation methods (ILfO) have recently made significant progress, but they still have some limitations: they need action-based supervised…
Quantifying Generalisation in Imitation Learning
Nathan Gavenski, Odinaldo Rodrigues
Imitation learning benchmarks often lack sufficient variation between training and evaluation, limiting meaningful generalisation assessment. We introduce Labyrinth, a benchmarking…
Explorative Imitation Learning: A Path Signature Approach for Continuous Environments
Nathan Gavenski, Juarez Monteiro, Felipe Meneguzzi +2
Some imitation learning methods combine behavioural cloning with self-supervision to infer actions from state pairs. However, most rely on a large number of expert trajectories to…
A Survey of Imitation Learning Methods, Environments and Metrics
Nathan Gavenski, Felipe Meneguzzi, Michael Luck +1
Imitation learning is an approach in which an agent learns how to execute a task by trying to mimic how one or more teachers perform it. This learning approach offers a compromise…