Mobile Agent Trajectory Prediction using Bayesian Nonparametric Reachability Trees

This paper presents an efficient trajectory prediction algorithm that has been developed to improve the performance of future collision avoidance and detection systems. The main idea is to embed the inferred intention information of surrounding agents into their estimated reachability sets to obtain a probabilistic description of their future paths. More specifically, the proposed approach combines the recently developed RRT-Reach algorithm and mixtures of Gaussian Processes. RRT-Reach was introduced by the authors as an extension of the closed-loop rapidly-exploring random tree (CL-RRT) algorithm to compute reachable sets of moving objects in real-time. A mixture of Gaussian processes (GP) is a flexible nonparametric Bayesian model used to represent a distribution over trajectories and have been previously demonstrated by the authors in a UAV interception and tracking of ground vehicles planning scheme. The mixture is trained using typical maneuvers learned from statistical data, and RRT-Reach utilizes samples from the GP to grow probabilistically weighted feasible paths of the surrounding vehicles. The resulting approach, denoted as RR-GP, has RRTReach's benefits of computing trajectories that are dynamically feasible by construction, therefore efficiently approximating the reachability set of surrounding vehicles following typical patterns. RRT-GP also features the GP mixture's benefits of providing a probabilistic weighting on the feasible trajectories produced by RRTReach, allowing our system to systematically weight trajectories by their likelihood. A demonstrative example on a car-like vehicle illustrates the advantages of the RR-GP approach by comparing it to two other GP-based algorithms. © 2011 by Professor Jonathan P. How, Massachusetts Institute of Technology. Published by the American Institute of Aeronautics and Astronautics, Inc

Reinforcement learning with misspecified model classes

Real-world robots commonly have to act in complex, poorly understood environments where the true world dynamics are unknown. To compensate for the unknown world dynamics, we often provide a class of models to a learner so it may select a model, typically using a minimum prediction error metric over a set of training data. Often in real-world domains the model class is unable to capture the true dynamics, due to either limited domain knowledge or a desire to use a small model. In these cases we call the model class misspecified, and an unfortunate consequence of misspecification is that even with unlimited data and computation there is no guarantee the model with minimum prediction error leads to the best performing policy. In this work, our approach improves upon the standard maximum likelihood model selection metric by explicitly selecting the model which achieves the highest expected reward, rather than the most likely model. We present an algorithm for which the highest performing model from the model class is guaranteed to be found given unlimited data and computation. Empirically, we demonstrate that our algorithm is often superior to the maximum likelihood learner in a batch learning setting for two common RL benchmark problems and a third real-world system, the hydrodynamic cart-pole, a domain whose complex dynamics cannot be known exactly.United States. Office of Naval Research. Multidisciplinary University Research Initiative (N00014-11-1-0688

Connexin36 knockout mice display increased sensitivity to pentylenetetrazol-induced seizure-like behaviors

Large-scale synchronous firing of neurons during seizures is modulated by electrotonic coupling between neurons via gap junctions. To explore roles for connexin36 (Cx36) gap junctions in seizures, we examined the seizure threshold of connexin36 knockout (Cx36KO) mice using a pentylenetetrazol (PTZ) model

REAL: Resilience and Adaptation using Large Language Models on Autonomous Aerial Robots

Large Language Models (LLMs) pre-trained on internet-scale datasets have shown impressive capabilities in code understanding, synthesis, and general purpose question-and-answering. Key to their performance is the substantial prior knowledge acquired during training and their ability to reason over extended sequences of symbols, often presented in natural language. In this work, we aim to harness the extensive long-term reasoning, natural language comprehension, and the available prior knowledge of LLMs for increased resilience and adaptation in autonomous mobile robots. We introduce REAL, an approach for REsilience and Adaptation using LLMs. REAL provides a strategy to employ LLMs as a part of the mission planning and control framework of an autonomous robot. The LLM employed by REAL provides (i) a source of prior knowledge to increase resilience for challenging scenarios that the system had not been explicitly designed for; (ii) a way to interpret natural-language and other log/diagnostic information available in the autonomy stack, for mission planning; (iii) a way to adapt the control inputs using minimal user-provided prior knowledge about the dynamics/kinematics of the robot. We integrate REAL in the autonomy stack of a real multirotor, querying onboard an offboard LLM at 0.1-1.0 Hz as part the robot's mission planning and control feedback loops. We demonstrate in real-world experiments the ability of the LLM to reduce the position tracking errors of a multirotor under the presence of (i) errors in the parameters of the controller and (ii) unmodeled dynamics. We also show (iii) decision making to avoid potentially dangerous scenarios (e.g., robot oscillates) that had not been explicitly accounted for in the initial prompt design.Comment: 13 pages, 5 figures, conference worksho

The role of somatosensation in automatic visuo-motor control: a comparison of congenital and acquired sensory loss.

Studies of chronically deafferented participants have illuminated how regaining some motor control after adult-onset loss of proprioceptive and touch input depends heavily on cognitive control. In this study we contrasted the performance of one such man, IW, with KS, a woman born without any somatosensory fibres. We postulated that her life-long absence of proprioception and touch might have allowed her to automate some simple visually-guided actions, something IW appears unable to achieve. We tested these two, and two age-matched control groups, on writing and drawing tasks performed with and without an audio-verbal echoing task that added a cognitive demand. In common with other studies of skilled action, the dual task was shown to affect visuo-motor performance in controls, with less well-controlled drawing and writing, evident as increases in path speed and reduction in curvature and trial duration. We found little evidence that IW was able to automate even the simplest drawing tasks and no evidence for automaticity in his writing. In contrast, KS showed a selective increase in speed of signature writing under the dual-task conditions, suggesting some ability to automate her most familiar writing. We also tested tracing of templates under mirror-reversed conditions, a task that imposes a powerful cognitive planning challenge. Both IW and KS showed evidence of a visuo-motor planning conflict, as did the controls, for shapes with sharp corners. Overall, IW was much faster than his controls to complete tracing shapes, consistent with an absence of visuo-proprioceptive conflict, whereas KS was slower than her controls, especially as the corners became sharper. She dramatically improved after a short period of practice while IW did not. We conclude that KS, who developed from birth without proprioception, may have some visually derived control of movement not under cognitive control, something not seen in IW. This allowed her to automate some writing and drawing actions, but impaired her initial attempts at mirror-tracing. In contrast, IW, who lost somatosensation as an adult, cannot automate these visually guided actions

Optical extinction efficiency measurements on fine and accumulation mode aerosol using single particle cavity ring-down spectroscopy

We report a new single aerosol particle approach using cavity ringdown spectroscopy to accurately determine optical extinction cross sections at multiple wavelengths.</p

PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment

Fully decentralized, multiagent trajectory planners enable complex tasks like search and rescue or package delivery by ensuring safe navigation in unknown environments. However, deconflicting trajectories with other agents and ensuring collision-free paths in a fully decentralized setting is complicated by dynamic elements and localization uncertainty. To this end, this paper presents (1) an uncertainty-aware multiagent trajectory planner and (2) an image segmentation-based frame alignment pipeline. The uncertainty-aware planner propagates uncertainty associated with the future motion of detected obstacles, and by incorporating this propagated uncertainty into optimization constraints, the planner effectively navigates around obstacles. Unlike conventional methods that emphasize explicit obstacle tracking, our approach integrates implicit tracking. Sharing trajectories between agents can cause potential collisions due to frame misalignment. Addressing this, we introduce a novel frame alignment pipeline that rectifies inter-agent frame misalignment. This method leverages a zero-shot image segmentation model for detecting objects in the environment and a data association framework based on geometric consistency for map alignment. Our approach accurately aligns frames with only 0.18 m and 2.7 deg of mean frame alignment error in our most challenging simulation scenario. In addition, we conducted hardware experiments and successfully achieved 0.29 m and 2.59 deg of frame alignment error. Together with the alignment framework, our planner ensures safe navigation in unknown environments and collision avoidance in decentralized settings.Comment: 7 pages, 13 figures, conference pape

Perception of body shape and size without touch or proprioception: evidence from individuals with congenital and acquired neuropathy.

The degree to which mental representations of the body can be established and maintained without somatosensory input remains unclear. We contrast two "deafferented" adults, one who acquired large fibre sensory loss as an adult (IW) and another who was born without somatosensation (KS). We compared their responses to those of matched controls in three perceptual tasks: first accuracy of their mental image of their hands (assessed by testing recognition of correct hand length/width ratio in distorted photographs and by locating landmarks on the unseen hand); then accuracy of arm length judgements (assessed by judgement of reaching distance), and finally, we tested for an attentional bias towards peri-personal space (assessed by reaction times to visual target presentation). We hypothesised that IW would demonstrate responses consistent with him accessing conscious knowledge, whereas KS might show evidence of responses dependent on non-conscious mechanisms. In the first two experiments, both participants were able to give consistent responses about hand shape and arm length, but IW displayed a better awareness of hand shape than KS (and controls). KS demonstrated poorer spatial accuracy in reporting hand landmarks than both IW and controls, and appears to have less awareness of her hands. Reach distance was overestimated by both IW and KS, as it was for controls; the precision of their judgements was slightly lower than that of the controls. In the attentional task, IW showed no reaction time differences across conditions in the visual detection task, unlike controls, suggesting that he has no peri-personal bias of attention. In contrast, KS did show target location-dependent modulation of reaction times, when her hands were visible. We suggest that both IW and KS can access a conscious body image, although its accuracy may reflect their different experience of hand action. Acquired sensory loss has deprived IW of any subconscious body awareness, but the congenital absence of somatosensation may have led to its partial replacement by a form of visual proprioception in KS

The Strange Parton Distribution of the Nucleon: Global Analysis and Applications

The strangeness degrees of freedom in the parton structure of the nucleon are explored in the global analysis framework, using the new CTEQ6.5 implementation of the general mass perturbative QCD formalism of Collins. We systematically determine the constraining power of available hard scattering experimental data on the magnitude and shape of the strange quark and anti-quark parton distributions. We find that current data favor a distinct shape of the strange sea compared to the isoscalar non-strange sea. A new reference parton distribution set, CTEQ6.5S0, and representative sets spanning the allowed ranges of magnitude and shape of the strange distributions, are presented. Some applications to physical processes of current interest in hadron collider phenomenology are discussed.Comment: 19 pages; revised version submitted to JHE

Novel functional hepatitis C virus glycoprotein isolates identified using an optimised viral pseudotype entry assay

Retrovirus pseudotypes are a highly tractable model used to study the entry pathways of enveloped viruses. This model has been extensively applied to the study of the hepatitis C virus (HCV) entry pathway, pre-clinical screening of antiviral antibodies and for assessing the phenotype of patient-derived viruses using HCV pseudoparticles (HCVpp) possessing the HCV E1 and E2 glycoproteins. However, not all patient-isolated clones produce particles that are infectious in this model. This study investigated factors that might limit phenotyping of patient-isolated HCV glycoproteins. Genetically related HCV glycoproteins from individual patient quasispecies were discovered to behave very differently in this entry model. Empirical optimisation of the ratio of packaging construct and glycoprotein-encoding plasmid was required for successful HCVpp genesis for different clones. The selection of retroviral packaging construct also influenced the function of HCV pseudoparticles. Some glycoprotein constructs tolerated a wide range of assay parameters, while others were much more sensitive to alterations. Furthermore, glycoproteins previously characterised as unable to mediate entry were found to be functional. These findings were validated using chimeric cell-cultured HCV bearing these glycoproteins. Using the same empirical approach we demonstrated that generation of infectious ebolavirus pseudoviruses (EBOVpv) were also sensitive to the amount, and ratio, of plasmids used, and that protocols for optimal production of these pseudoviruses is dependent on the exact virus glycoprotein construct. These findings demonstrate that it is crucial for studies utilising pseudoviruses to conduct empirical optimisation of pseudotype production for each specific glycoprotein sequence to achieve optimal titres and facilitate accurate phenotyping