Slow but flexible or fast but rigid? Discrete and continuous processes compared DOI Creative Commons
Matteo Priorelli, Ivilin Stoianov

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 21, 2023

A bstract tradeoff exists when dealing with complex tasks composed of multiple steps. High-level cognitive processes can find the best sequence actions to achieve a goal in uncertain environments, but they are slow and require significant computational demand. In contrast, lower-level processing allows reacting environmental stimuli rapidly, limited capacity determine optimal or replan expectations not met. Through reiteration same task, biological organisms tradeoff: from action primitives, composite trajectories gradually emerge by creating task-specific neural structures. The two frameworks active inference – recent brain paradigm that views perception as subject free energy minimization imperative well capture high-level low-level human behavior, how task specialization occurs these terms is still unclear. this study, we compare strategies on dynamic pick-and-place task: hybrid (discrete-continuous) model planning capabilities continuous-only fixed transitions. Both models rely hierarchical (intrinsic extrinsic) structure, suited for defining reaching grasping movements, respectively. Our results show perform better minimal resource expenditure at cost less flexibility. Finally, propose discrete might lead continuous attractors different motor learning phases, laying foundations further studies bio-inspired adaptation.

Language: Английский

Dynamic planning in hierarchical active inference DOI Creative Commons
Matteo Priorelli, Ivilin Stoianov

Neural Networks, Journal Year: 2025, Volume and Issue: 185, P. 107075 - 107075

Published: Jan. 8, 2025

By dynamic planning, we refer to the ability of human brain infer and impose motor trajectories related cognitive decisions. A recent paradigm, active inference, brings fundamental insights into adaptation biological organisms, constantly striving minimize prediction errors restrict themselves life-compatible states. Over past years, many studies have shown how animal behaviors could be explained in terms inference - either as discrete decision-making or continuous control inspiring innovative solutions robotics artificial intelligence. Still, literature lacks a comprehensive outlook on effectively planning realistic actions changing environments. Setting ourselves goal modeling complex tasks such tool use, delve topic keeping mind two crucial aspects behavior: capacity understand exploit affordances for object manipulation, learn hierarchical interactions between self environment, including other agents. We start from simple unit gradually describe more advanced structures, comparing recently proposed design choices providing basic examples. This study distances itself traditional views centered neural networks reinforcement learning, points toward yet unexplored direction inference: hybrid representations models.

Language: Английский

Citations

0
Slow but flexible or fast but rigid? Discrete and continuous processes compared DOI Creative Commons
Matteo Priorelli, Ivilin Stoianov

Heliyon, Journal Year: 2024, Volume and Issue: 10(20), P. e39129 - e39129

Published: Oct. 1, 2024

Language: Английский

Citations

2
Active Semantic Mapping for Household Robots: Rapid Indoor Adaptation and Reduced User Burden DOI

Tomochika Ishikawa,

Akira Taniguchi, Yoshinobu Hagiwara

et al.

2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Journal Year: 2023, Volume and Issue: 34, P. 3116 - 3123

Published: Oct. 1, 2023

Active semantic mapping is essential for service robots to quickly capture both the map of an environment and its spatial meaning, while also minimizing burden on users during robot operation data collection. SpCoSLAM, a method with place categorization simultaneous localization (SLAM), well suited environmental adaptation, as it not limited predefined labels. However, SpCoSLAM presents two issues that increase users: 1) struggle efficiently determine destination robot's quick 2) providing instructions becomes repetitive cumbersome. To address these challenges, we propose Active-SpCoSLAM, which enables actively explore uncharted areas employs CLIP image captioning provide flexible vocabulary replaces human instructions. The determines actions by calculating information gain integrated from semantics SLAM uncertainties. We conducted experiments in simulated environment, comparing proposed other methods terms efficiency applicability object discovery tasks. Additionally, tested method, combines user instruction CLIP, real environment. Our results demonstrated explored approximately five fewer iterations 11 minutes faster compared case random exploration. Moreover, our achieved higher success rate tasks earlier stages learning methods. In conclusion, rapidly covers gathering useful tasks, thus reducing enhancing adaptability. project website https://tomochika-ishikawa.github.io/Active-SpCoSLAM/.

Language: Английский

Citations

4
Slow but flexible or fast but rigid? Discrete and continuous processes compared DOI Creative Commons
Matteo Priorelli, Ivilin Stoianov

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Aug. 21, 2023

A bstract tradeoff exists when dealing with complex tasks composed of multiple steps. High-level cognitive processes can find the best sequence actions to achieve a goal in uncertain environments, but they are slow and require significant computational demand. In contrast, lower-level processing allows reacting environmental stimuli rapidly, limited capacity determine optimal or replan expectations not met. Through reiteration same task, biological organisms tradeoff: from action primitives, composite trajectories gradually emerge by creating task-specific neural structures. The two frameworks active inference – recent brain paradigm that views perception as subject free energy minimization imperative well capture high-level low-level human behavior, how task specialization occurs these terms is still unclear. this study, we compare strategies on dynamic pick-and-place task: hybrid (discrete-continuous) model planning capabilities continuous-only fixed transitions. Both models rely hierarchical (intrinsic extrinsic) structure, suited for defining reaching grasping movements, respectively. Our results show perform better minimal resource expenditure at cost less flexibility. Finally, propose discrete might lead continuous attractors different motor learning phases, laying foundations further studies bio-inspired adaptation.

Language: Английский

Citations

2