6 papers · 1 filter
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
Ricardo Cannizzaro, Michael Groom, Jonathan Routley +2
Manipulation tasks require robots to reason about cause and effect when interacting with objects. Yet, many data-driven approaches lack causal semantics and thus only consider corr…
Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks
Ricardo Cannizzaro, Rhys Howard, Paulina Lewinska +1
Causal modelling offers great potential to provide autonomous agents the ability to understand the data-generation process that governs their interactions with the world. Such mode…
Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks
Ricardo Cannizzaro, Jonathan Routley, Lars Kunze
Uncertainties in the real world mean that is impossible for system designers to anticipate and explicitly design for all scenarios that a robot might encounter. Thus, robots design…
CAR-DESPOT: Causally-Informed Online POMDP Planning for Robots in Confounded Environments
Ricardo Cannizzaro, Lars Kunze
Robots operating in real-world environments must reason about possible outcomes of stochastic actions and make decisions based on partial observations of the true world state. A ma…
Decentralised Intelligence, Surveillance, and Reconnaissance in Unknown Environments with Heterogeneous Multi-Robot Systems
Ki Myung Brian Lee, Felix H. Kong, Ricardo Cannizzaro +4
We present the design and implementation of a decentralised, heterogeneous multi-robot system for performing intelligence, surveillance and reconnaissance (ISR) in an unknown envir…
An Upper Confidence Bound for Simultaneous Exploration and Exploitation in Heterogeneous Multi-Robot Systems
Ki Myung Brian Lee, Felix H. Kong, Ricardo Cannizzaro +4
Heterogeneous multi-robot systems are advantageous for operations in unknown environments because functionally specialised robots can gather environmental information, while others…