4 research outputs found
Inferring Hierarchical Structure in Multi-Room Maze Environments
Cognitive maps play a crucial role in facilitating flexible behaviour by
representing spatial and conceptual relationships within an environment. The
ability to learn and infer the underlying structure of the environment is
crucial for effective exploration and navigation. This paper introduces a
hierarchical active inference model addressing the challenge of inferring
structure in the world from pixel-based observations. We propose a three-layer
hierarchical model consisting of a cognitive map, an allocentric, and an
egocentric world model, combining curiosity-driven exploration with
goal-oriented behaviour at the different levels of reasoning from context to
place to motion. This allows for efficient exploration and goal-directed search
in room-structured mini-grid environments.Comment: ICML 2023 Worksho
Learning to Navigate from Scratch using World Models and Curiosity: the Good, the Bad, and the Ugly
Learning to navigate unknown environments from scratch is a challenging
problem. This work presents a system that integrates world models with
curiosity-driven exploration for autonomous navigation in new environments. We
evaluate performance through simulations and real-world experiments of varying
scales and complexities. In simulated environments, the approach rapidly and
comprehensively explores the surroundings. Real-world scenarios introduce
additional challenges. Despite demonstrating promise in a small controlled
environment, we acknowledge that larger and dynamic environments can pose
challenges for the current system. Our analysis emphasizes the significance of
developing adaptable and robust world models that can handle environmental
changes to prevent repetitive exploration of the same areas.Comment: IROS 2023 workshop World Models and Predictive Coding in Cognitive
Robotics and IROS 2023 workshop Learning Robot Super Autonom
Home run : finding your way home by imagining trajectories
When studying unconstrained behaviour and allowing mice to leave their cage to navigate a complex labyrinth, the mice exhibit foraging behaviour in the labyrinth searching for rewards, returning to their home cage now and then, e.g. to drink. Surprisingly, when executing such a “home run”, the mice do not follow the exact reverse path, in fact, the entry path and home path have very little overlap. Recent work proposed a hierarchical active inference model for navigation, where the low level model makes inferences about hidden states and poses that explain sensory inputs, whereas the high level model makes inferences about moving between locations, effectively building a map of the environment. However, using this “map” for planning, only allows the agent to find trajectories that it previously explored, far from the observed mice’s behaviour. In this paper, we explore ways of incorporating before-unvisited paths in the planning algorithm, by using the low level generative model to imagine potential, yet undiscovered paths. We demonstrate a proof of concept in a grid-world environment, showing how an agent can accurately predict a new, shorter path in the map leading to its starting point, using a generative model learnt from pixel-based observations