11 citations · 15 across the 7 of their papers we have counts for
7 papers
Recursive Decomposition with Dependencies for Generic Divide-and-Conquer Reasoning
Sergio Hernández-Gutiérrez, Minttu Alakuijala, Alexander V. Nikitin +1
Reasoning tasks are crucial in many domains, especially in science and engineering. Although large language models (LLMs) have made progress in reasoning tasks using techniques suc…
ViPlan: A Benchmark for Visual Planning with Symbolic Predicates and Vision-Language Models
Matteo Merler, Nicola Dainese, Minttu Alakuijala +5
Integrating Large Language Models with symbolic planners is a promising direction for obtaining verifiable and grounded plans, with recent works extending this idea to visual domai…
Memento No More: Coaching AI Agents to Master Multiple Tasks via Hints Internalization
Minttu Alakuijala, Ya Gao, Georgy Ananov +4
As the general capabilities of artificial intelligence (AI) agents continue to evolve, their ability to learn to master multiple complex tasks through experience remains a key chal…
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics
Minttu Alakuijala, Reginald McLean, Isaac Woungang +4
Natural language is often the easiest and most convenient modality for humans to specify tasks for robots. However, learning to ground language to behavior typically requires impra…
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search
Nicola Dainese, Matteo Merler, Minttu Alakuijala +1
In this work we consider Code World Models, world models generated by a Large Language Model (LLM) in the form of Python code for model-based Reinforcement Learning (RL). Calling c…
Learning Reward Functions for Robotic Manipulation by Observing Humans
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal +2
Observing a human demonstrator manipulate objects provides a rich, scalable and inexpensive source of data for learning robotic policies. However, transferring skills from human vi…
Residual Reinforcement Learning from Demonstrations
Minttu Alakuijala, Gabriel Dulac-Arnold, Julien Mairal +2
Residual reinforcement learning (RL) has been proposed as a way to solve challenging robotic tasks by adapting control actions from a conventional feedback controller to maximize a…