RFK Jr taunts Donald Trump as he shares pointed'Thanksgiving dinner' photo with the president, Elon Musk and Don Jr Fans hail Cece Winans' 'best ever' rendition of the national anthem on Thanksgiving and beg the NFL to get her to the Super Bowl I've seen it too many times - I have to speak up: KENNEDY Trump plunged into security scandal over Afghan shooter's asylum - after president blamed Biden Bryan Kohberger becomes nightmare prison diva... as he throws huge tantrum over BANANAS behind bars My wife was blindsided when I asked for a divorce. There was no foul play or'other woman' but this is why I did it... and the six subtle signs your partner is planning on leaving you too: RICHARD WARNER My book on the Kennedys was used as a'mistress manual' by Olivia Nuzzi... then this wannabe Carolyn Bessette had the nerve to hound me with these outrageous texts: MAUREEN CALLAHAN Americans are finally realizing why we don't eat turkey eggs Plastic surgeon reveals secrets of Tom Brady's changing face, including'unnatural' procedure... and truth about Ozempic use Lilibet's locks steal the show! Meghan's daughter is every inch the little Princess with her fiery red locks in a neat plait at Thanksgiving outing Kimberly Guilfoyle leaves little to the imagination in a figure-hugging sheer lace gown for Thanksgiving dinner in Athens in her role as US Ambassador - after admitting she's'husband hunting' Hollywood stars who REFUSE to celebrate Thanksgiving over animal cruelty and its'blood-soaked' history Californians were shaken by multiple earthquakes on Thanksgiving morning, raising concerns in the seismically active region. At least 13 tremors, starting around 4:30am PT (7:30am ET) and ranging from magnitude 1.0 to 3.7, were reported near The Geysers geothermal field in Northern California . The last earthquake, a small 1.1 magnitude, was detected at 5:47am PT (8:47am ET).

Autonomous robots have real-world applications in diverse fields, such as mobile manipulation and environmental exploration, and many such tasks benefit from a hands-off approach in terms of human user involvement over a long task horizon. However, the level of autonomy achievable by a deployment is limited in part by the problem definition or task specification required by the system. Task specifications often require technical, low-level information that is unintuitive to describe and may result in generic solutions, burdening the user technically both before and after task completion. In this thesis, we aim to advance task specification abstraction toward the goal of increasing robot autonomy in real-world scenarios. We do so by tackling problems that address several different angles of this goal. First, we develop a way for the automatic discovery of optimal transition points between subtasks in the context of constrained mobile manipulation, removing the need for the human to hand-specify these in the task specification. We further propose a way to automatically describe constraints on robot motion by using demonstrated data as opposed to manually-defined constraints. Then, within the context of environmental exploration, we propose a flexible task specification framework, requiring just a set of quantiles of interest from the user that allows the robot to directly suggest locations in the environment for the user to study. We next systematically study the effect of including a robot team in the task specification and show that multirobot teams have the ability to improve performance under certain specification conditions, including enabling inter-robot communication. Finally, we propose methods for a communication protocol that autonomously selects useful but limited information to share with the other robots.

We investigate improving Monte Carlo Tree Search based solvers for Partially Observable Markov Decision Processes (POMDPs), when applied to adaptive sampling problems. We propose improvements in rollout allocation, the action exploration algorithm, and plan commitment. The first allocates a different number of rollouts depending on how many actions the agent has taken in an episode. We find that rollouts are more valuable after some initial information is gained about the environment. Thus, a linear increase in the number of rollouts, i.e. allocating a fixed number at each step, is not appropriate for adaptive sampling tasks. The second alters which actions the agent chooses to explore when building the planning tree. We find that by using knowledge of the number of rollouts allocated, the agent can more effectively choose actions to explore. The third improvement is in determining how many actions the agent should take from one plan. Typically, an agent will plan to take the first action from the planning tree and then call the planner again from the new state. Using statistical techniques, we show that it is possible to greatly reduce the number of rollouts by increasing the number of actions taken from a single planning tree without affecting the agent's final reward. Finally, we demonstrate experimentally, on simulated and real aquatic data from an underwater robot, that these improvements can be combined, leading to better adaptive sampling. The code for this work is available at https://github.com/uscresl/AdaptiveSamplingPOMCP