Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning

We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system is learned by optimizing a combinational objective encompassing three key designs. First, we propose that an agent conceives the next observation before making an action decision. This is achieved by learning a variational generative module from expert demonstrations. We then propose predicting static collision in advance, as an auxiliary task to improve safety during navigation. Moreover, to alleviate the training data imbalance problem of termination action prediction, we also introduce a target checking module to differentiate from augmenting navigation policy with a termination action. The three proposed designs all contribute to the improved training data efficiency, static collision avoidance, and navigation generalization performance, resulting in a novel target-driven mapless navigation system. Through experiments on a TurtleBot, we provide evidence that our model can be integrated into a robotic system and navigate in the real world. Videos and models can be found in the supplementary material.Comment: 11 pages, accepted by IEEE Robotics and Automation Letter

Reinforcement Learning-based Visual Navigation with Information-Theoretic Regularization

To enhance the cross-target and cross-scene generalization of target-driven visual navigation based on deep reinforcement learning (RL), we introduce an information-theoretic regularization term into the RL objective. The regularization maximizes the mutual information between navigation actions and visual observation transforms of an agent, thus promoting more informed navigation decisions. This way, the agent models the action-observation dynamics by learning a variational generative model. Based on the model, the agent generates (imagines) the next observation from its current observation and navigation target. This way, the agent learns to understand the causality between navigation actions and the changes in its observations, which allows the agent to predict the next action for navigation by comparing the current and the imagined next observations. Cross-target and cross-scene evaluations on the AI2-THOR framework show that our method attains at least a $10\%$ improvement of average success rate over some state-of-the-art models. We further evaluate our model in two real-world settings: navigation in unseen indoor scenes from a discrete Active Vision Dataset (AVD) and continuous real-world environments with a TurtleBot.We demonstrate that our navigation model is able to successfully achieve navigation tasks in these scenarios. Videos and models can be found in the supplementary material.Comment: 11 pages, corresponding author: Kai Xu ([email protected]) and Jun Wang ([email protected]

SurrealDriver: Designing Generative Driver Agent Simulation Framework in Urban Contexts based on Large Language Model

Simulation plays a critical role in the research and development of autonomous driving and intelligent transportation systems. However, the current simulation platforms exhibit limitations in the realism and diversity of agent behaviors, which impede the transfer of simulation outcomes to the real world. In this paper, we propose a generative driver agent simulation framework based on large language models (LLMs), capable of perceiving complex traffic scenarios and providing realistic driving maneuvers. Notably, we conducted interviews with 24 drivers and used their detailed descriptions of driving behavior as chain-of-thought prompts to develop a `coach agent' module, which can evaluate and assist driver agents in accumulating driving experience and developing human-like driving styles. Through practical simulation experiments and user experiments, we validate the feasibility of this framework in generating reliable driver agents and analyze the roles of each module. The results show that the framework with full architect decreased the collision rate by 81.04% and increased the human-likeness by 50%. Our research proposes the first urban context driver agent simulation framework based on LLMs and provides valuable insights into the future of agent simulation for complex tasks.Comment: 12 pages, 8 figure

Extremely strong tubular stacking of aromatic oligoamide macrocycles

As the third-generation rigid macrocycles evolved from progenitor 1, cyclic aromatic oligoamides 3, with a backbone of reduced constraint, exhibit extremely strong stacking with an astoundingly high affinity (estimated lower limit of Kdimer \u3e 1013 M–1 in CHCl3), which leads to dispersed tubular stacks that undergo further assembly in solution. Computational study reveals a very large binding energy (–49.77 kcal mol–1) and indicates highly cooperative local dipole interactions that account for the observed strength and directionality for the stacking of 3. In the solid-state, X-ray diffraction (XRD) confirms that the aggregation of 3 results in well-aligned tubular stacks. The persistent tubular assemblies of 3, with their non-deformable sub-nm pore, are expected to possess many interesting functions. One such function, transmembrane ion transport, is observed for 3. Includes supplemental material

Extremely strong tubular stacking of aromatic oligoamide macrocycles

As the third-generation rigid macrocycles evolved from progenitor 1, cyclic aromatic oligoamides 3, with a backbone of reduced constraint, exhibit extremely strong stacking with an astoundingly high affinity (estimated lower limit of Kdimer \u3e 1013 M–1 in CHCl3), which leads to dispersed tubular stacks that undergo further assembly in solution. Computational study reveals a very large binding energy (–49.77 kcal mol–1) and indicates highly cooperative local dipole interactions that account for the observed strength and directionality for the stacking of 3. In the solid-state, X-ray diffraction (XRD) confirms that the aggregation of 3 results in well-aligned tubular stacks. The persistent tubular assemblies of 3, with their non-deformable sub-nm pore, are expected to possess many interesting functions. One such function, transmembrane ion transport, is observed for 3. Includes supplemental material

Self-assembling subnanometer pores with unusual mass-transport properties

A long-standing aim in molecular self-assembly is the development of synthetic nanopores capable of mimicking the mass-transport characteristics of biological channels and pores. Here we report a strategy for enforcing the nanotubular assembly of rigid macrocycles in both the solid state and solution based on the interplay of multiple hydrogen-bonding and aromatic π − π stacking interactions. The resultant nanotubes have modifiable surfaces and inner pores of a uniform diameter defined by the constituent macrocycles. The self-assembling hydrophobic nanopores can mediate not only highly selective transmembrane ion transport, unprecedented for a synthetic nanopore, but also highly efficient transmembrane water permeability. These results establish a solid foundation for developing synthetically accessible, robust nanostructured systems with broad applications such as reconstituted mimicry of defined functions solely achieved by biological nanostructures, molecular sensing, and the fabrication of porous materials required for water purification and molecular separations

Aromatic Oligoamide Macrocycles with a Backbone of Reduced Constraint

Oligoamide macrocycles with a backbone partially constrained by hydrogen bonds have been prepared. These macrocycles, carrying multiple H-bonding side chains, underwent strong aggregation in solution and form long fibers in the solid state. In contrast to the strong and specific complexation of the guanidinium ion by analogous macrocycles with fully H-bond-constrained backbones, these macrocycles failed to recognize the same cation, indicating that reducing backbone constraint has led to a drastic change in their cavity

Toward understanding non-coding RNA roles in intracranial aneurysms and subarachnoid hemorrhage

Subarachnoid hemorrhage (SAH) is a common and frequently life-threatening cerebrovascular disease, which is mostly related with a ruptured intracranial aneurysm. Its complications include rebleeding, early brain injury, cerebral vasospasm, delayed cerebral ischemia, chronic hydrocephalus, and also non neurological problems. Non-coding RNAs (ncRNAs), comprising of microRNAs (miRNAs), small interfering RNAs (siRNAs) and long non-coding RNAs (lncRNAs), play an important role in intracranial aneurysms and SAH. Here, we review the non-coding RNAs expression profile and their related mechanisms in intracranial aneurysms and SAH. Moreover, we suggest that these non-coding RNAs function as novel molecular biomarkers to predict intracranial aneurysms and SAH, and may yield new therapies after SAH in the future

Psychometric validation of the fear of progression questionnaire-short form in acute pancreatitis patientsAccessible Summary

Introduction: Fear of progression (FoP) is associated with the quality of life and behavioral change in acute pancreatitis (AP) patients, but lack of assessment tools. Aim: This study aimed to develop and evaluate the psychometric properties of the Fear of Progression Questionnaire-Short Form in AP patients (AP-FoP-Q-SF). Methods: Internal consistency, factorial structure, convergent validity, and criterion validity of AP-FoP-Q-SF were assessed. A receiver operating characteristic (ROC) curve analysis was performed to identify the cutoff value for high FoP. Associations between patient variables and FoP were evaluated using multiple logistic regression. Wilcox rank sum test was used to analyses the costs and length of hospital stay of the patients with high FoP. Results: The two-factor structure showed a good fit. Internal consistency was acceptable (Cronbach's α = 0.771). The cutoff of 26 identified 35.3% of patients with high FoP. High FoP scores were associated with age (OR = 0.96, 95%CI: 0.94–0.98), recurrence times (OR = 1.22, 95%CI: 1.02–1.45) and anxiety (OR = 1.27, 95%CI: 1.16–1.40). Patients with high FoP spent more cost and time in the hospital. Conclusions: The AP-FoP-Q-SF is a good FoP tool for AP patients in China. Implications for practice: Clinicians can use the AP-FoP-Q-SF to assess FoP and take promotion programs to avoid worse effects