For robotic decision-making under uncertainty, the balance between exploitation and exploration of available options must be carefully taken into account. In this study, we introduce a new variant of contextual multi-armed bandits called observation-augmented CMABs (OA-CMABs) wherein a decision-making agent can utilize extra outcome observations from an external information source. CMABs model the expected option outcomes as a function of context features and hidden parameters, which are inferred from previous option outcomes. In OA-CMABs, external observations are also a function of context features and thus provide additional evidence about the hidden parameters. Yet, if an external information source is error-prone, the resulting posterior updates can harm decision-making performance unless the presence of errors is considered. To this end, we propose a robust Bayesian inference process for OA-CMABs that is based on the concept of probabilistic data validation. Our approach handles complex mixture model parameter priors and hybrid observation likelihoods for semantic data sources, allowing us to develop validation algorithms based on recently develop probabilistic semantic data association techniques. Furthermore, to more effectively cope with the combined sources of uncertainty in OA-CMABs, we derive a new active inference algorithm for option selection based on expected free energy minimization. This generalizes previous work on active inference for bandit-based robotic decision-making by accounting for faulty observations and non-Gaussian inference. Our approaches are demonstrated on a simulated asynchronous search site selection problem for space exploration. The results show that even if incorrect observations are provided by external information sources, efficient decision-making and robust parameter inference are still achieved in a wide variety of experimental conditions.

We present the effect of adapting to human preferences on trust in a human-robot teaming task. The team performs a task in which the robot acts as an action recommender to the human. It is assumed that the behavior of the human and the robot is based on some reward function they try to optimize. We use a new human trust-behavior model that enables the robot to learn and adapt to the human's preferences in real-time during their interaction using Bayesian Inverse Reinforcement Learning. We present three strategies for the robot to interact with a human: a non-learner strategy, in which the robot assumes that the human's reward function is the same as the robot's, a non-adaptive learner strategy that learns the human's reward function for performance estimation, but still optimizes its own reward function, and an adaptive-learner strategy that learns the human's reward function for performance estimation and also optimizes this learned reward function. Results show that adapting to the human's reward function results in the highest trust in the robot.

Antarctic scientists seeking to locate the wreck of Sir Ernest Shackleton's lost ship, the Endurance, have arrived at the search site. The team broke through thick pack ice on Sunday to reach the vessel's last known position in the Weddell Sea. Robotic submersibles will now spend the next few days scouring the ocean floor for the maritime icon. Shackleton and his crew had to abandon Endurance in 1915 when it was crushed by sea ice and sank in 3,000m of water. Their escape across the frozen floes on foot and in lifeboats is an extraordinary story that has resonated down through the years - and makes the wooden polar yacht perhaps the most sought-after of all undiscovered wrecks.

A U.S. company will be deploying the world's most advanced undersea search vessels in a renewed bid to search for missing Malaysia Airlines Flight MH370, which went missing on March 8, 2014, with 239 people on board. Texas-based Ocean Infinity -- which has signed a "no cure, no fee" deal with the Malaysian government to find the jetliner -- will for the first time use a swarm of eight drone-like autonomous underwater vehicles (AUVs) to scour a remote part of the southern Indian Ocean, where the ill-fated plane is believed to have gone down. The company will be paid only if it succeeds in locating the plane, which is believed to have gone down while on its way from Kuala Lumpur to Beijing. According to the Daily Beast, Ocean Infinity will conduct the new search with the latest technology north of the original search area, where an underwater operation for more than three years yielded no concrete clues. Talking about the technology that the company will use, the Daily Beast reported that the system was being used for the first time and that while en route from the Caribbean to the search site, the command ship, Seabed Constructor, paused several times to carry out trials at depths similar to those at the Indian Ocean search site.