11 citations · 21 across the 8 of their papers we have counts for
8 papers
Reinforcement Learning for Ballbot Navigation in Uneven Terrain
Achkan Salehi
Ballbot (i.e. Ball balancing robot) navigation usually relies on methods rooted in control theory (CT), and works that apply Reinforcement learning (RL) to the problem remain rare…
Integrating LLMs and Decision Transformers for Language Grounded Generative Quality-Diversity
Achkan Salehi, Stephane Doncieux
Quality-Diversity is a branch of stochastic optimization that is often applied to problems from the Reinforcement Learning and control domains in order to construct repertoires of…
Data-efficient, Explainable and Safe Box Manipulation: Illustrating the Advantages of Physical Priors in Model-Predictive Control
Achkan Salehi, Stephane Doncieux
Model-based RL/control have gained significant traction in robotics. Yet, these approaches often remain data-inefficient and lack the explainability of hand-engineered solutions. T…
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations
Achkan Salehi, Steffen Rühl, Stephane Doncieux
Model-based Reinforcement Learning and Control have demonstrated great potential in various sequential decision making problem domains, including in robotics settings. However, rea…
Towards QD-suite: developing a set of benchmarks for Quality-Diversity algorithms
Achkan Salehi, Stephane Doncieux
While the field of Quality-Diversity (QD) has grown into a distinct branch of stochastic optimization, a few problems, in particular locomotion and navigation tasks, have become de…
Geodesics, Non-linearities and the Archive of Novelty Search
Achkan Salehi, Alexandre Coninx, Stephane Doncieux
The Novelty Search (NS) algorithm was proposed more than a decade ago. However, the mechanisms behind its empirical success are still not well formalized/understood. This short not…
Few-shot Quality-Diversity Optimization
Achkan Salehi, Alexandre Coninx, Stephane Doncieux
In the past few years, a considerable amount of research has been dedicated to the exploitation of previous learning experiences and the design of Few-shot and Meta Learning approa…
BR-NS: an Archive-less Approach to Novelty Search
Achkan Salehi, Alexandre Coninx, Stephane Doncieux
As open-ended learning based on divergent search algorithms such as Novelty Search (NS) draws more and more attention from the research community, it is natural to expect that its…