Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance

There are two major challenges for scaling up robot navigation around dynamic obstacles: the complex interaction dynamics of the obstacles can be hard to model analytically, and the complexity of planning and control grows exponentially in the number of obstacles. Data-driven and learning-based methods are thus particularly valuable in this context. However, data-driven methods are sensitive to distribution drift, making it hard to train and generalize learned models across different obstacle densities. We propose a novel method for compositional learning of Sequential Neural Control Barrier models (SNCBFs) to achieve scalability. Our approach exploits an important observation: the spatial interaction patterns of multiple dynamic obstacles can be decomposed and predicted through temporal sequences of states for each obstacle. Through decomposition, we can generalize control policies trained only with a small number of obstacles, to environments where the obstacle density can be 100x higher. We demonstrate the benefits of the proposed methods in improving dynamic collision avoidance in comparison with existing methods including potential fields, end-to-end reinforcement learning, and model-predictive control. We also perform hardware experiments and show the practical effectiveness of the approach in the supplementary video.Comment: To be published in IROS 202

Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality

Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to accelerate it by introducing various heuristics. However, whether these heuristics can apply to non-grid-based problem settings while maintaining their effectiveness remains an open question. In this work, we find that the answer is prone to be no. To this end, we propose a learning-based component, i.e., the Graph Transformer, as a heuristic function to accelerate the planning. The proposed method is provably complete and bounded-suboptimal with any desired factor. We conduct extensive experiments on two environments with dense graphs. Results show that the proposed Graph Transformer can be trained in problem instances with relatively few agents and generalizes well to a larger number of agents, while achieving better performance than state-of-the-art methods.Comment: Accepted by ICRA 202

Iterative Reachability Estimation for Safe Reinforcement Learning

Ensuring safety is important for the practical deployment of reinforcement learning (RL). Various challenges must be addressed, such as handling stochasticity in the environments, providing rigorous guarantees of persistent state-wise safety satisfaction, and avoiding overly conservative behaviors that sacrifice performance. We propose a new framework, Reachability Estimation for Safe Policy Optimization (RESPO), for safety-constrained RL in general stochastic settings. In the feasible set where there exist violation-free policies, we optimize for rewards while maintaining persistent safety. Outside this feasible set, our optimization produces the safest behavior by guaranteeing entrance into the feasible set whenever possible with the least cumulative discounted violations. We introduce a class of algorithms using our novel reachability estimation function to optimize in our proposed framework and in similar frameworks such as those concurrently handling multiple hard and soft constraints. We theoretically establish that our algorithms almost surely converge to locally optimal policies of our safe optimization framework. We evaluate the proposed methods on a diverse suite of safe RL environments from Safety Gym, PyBullet, and MuJoCo, and show the benefits in improving both reward performance and safety compared with state-of-the-art baselines.Comment: Accepted in NeurIPS 202

The Water-Saving Management Contract in China: Current Status, Existing Problems, and Countermeasure Suggestions

This study analyzed the policies and summarized the current status of the national water-saving management contract (WSMC) development as well as its implementation between 2016 and 2020. Several main problems affecting and restricting the implementation of WSMC projects were identified including the lack of awareness of the importance of water conservation among water users, the limited number and scale of water conservation service enterprises, and the inadequacy of relevant policies and systems. Subsequently, 11 countermeasure suggestions were proposed, including stimulating the endogenous power of the WSMC, strengthening policy support for the WSMC, improving the supporting systems and the service systems, increasing investment and innovation of water conservation technologies, improving technical standards, exploring innovative WSMC models, promoting pilot demonstrations, deepening water price system reforms, increasing the publicity and training of the WSMC, strengthening coordination, and linkage between multiple departments. These suggestions can provide a reference for the relevant departments to develop and promote WSMC policies

The Annual Cycle in Mid-Latitude Stratospheric and Mesospheric Ozone Associated with Quasi-Stationary Wave Structure by the MLS Data 2011–2020

The purpose of this work is to study quasi-stationary wave structure in the mid-latitude stratosphere and mesosphere (40–50°N) and its role in the formation of the annual ozone cycle. Geopotential height and ozone from Aura MLS data are used and winter climatology for January–February 2011–2020 is considered. The 10-degree longitude segment centered on Longfengshan Brewer station (44.73°N, 127.60°E), China, is examined in detail. The station is located in the region of the Aleutian Low associated with the quasi-stationary zonal maximum of total ozone. Annual and semi-annual oscillations in ozone using units of ozone volume mixing ratio and concentration, as well as changes in ozone peak altitude and in time series of ozone at individual pressure levels between 316 hPa (9 km) and 0.001 hPa (96 km) were compared. The ozone maximum in the vertical profile is higher in volume mixing ratio (VMR) values than in concentration by about 15 km (5 km) in the stratosphere (mesosphere), consistent with some previous studies. We found that the properties of the annual cycle are better resolved in the altitude range of the main ozone maximum: middle–upper stratosphere in VMR and lower stratosphere in concentration. Both approaches reveal annual and semi-annual changes in the ozone peak altitudes in a range of 4–6 km during the year. In the lower-stratospheric ozone of the Longfengshan domain, an earlier development of the annual cycle takes place with a maximum in February and a minimum in August compared to spring and autumn, respectively, in zonal means. This is presumably due to the higher rate of dynamical ozone accumulation in the region of the quasi-stationary zonal ozone maximum. The “no-annual-cycle” transition layers are found in the stratosphere and mesosphere. These layers with undisturbed ozone volume mixing ratio are of interest for more detailed future study

Zonal Asymmetry of the Stratopause in the 2019/2020 Arctic Winter

The aim of this work is to study the zonally asymmetric stratopause that occurred in the Arctic winter of 2019/2020, when the polar vortex was particularly strong and there was no sudden stratospheric warming. Aura Microwave Limb Sounder temperature data were used to analyze the evolution of the stratopause with a particular focus on its zonally asymmetric wave 1 pattern. There was a rapid descent of the stratopause height below 50 km in the anticyclone region in mid-December 2019. The descended stratopause persisted until mid-January 2020 and was accompanied by a slow descent of the higher stratopause in the vortex region. The results show that the stratopause in this event was inclined and lowered from the mesosphere in the polar vortex to the stratosphere in the anticyclone. It was found that the vertical amplification of wave 1 between 50 km and 60 km closely coincides in time with the rapid stratopause descent in the anticyclone. Overall, the behavior contrasts with the situation during sudden stratospheric warmings when the stratopause reforms at higher altitudes following wave amplification events. We link the mechanism responsible for coupling between the vertical wave 1 amplification and this form of zonally asymmetric stratopause descent to the unusual disruption of the quasi-biennial oscillation that occurred in late 2019

Characterization of the Active Ingredient and Prediction of the Potential Mechanism of Dahuoluo Pill via Mass Spectrometry with the Network Pharmacology Method

The Dahuoluo pill (DHLP) is a classic Chinese patent medicine used to treat rheumatoid arthritis and other conditions. However, there has been no research on the chemical components of DHLP and the mechanisms by which it ameliorates rheumatoid arthritis. Hence, we analysed the chemical components of DHLP and the DHLP components absorbed in blood by using ultraperformance liquid chromatography-Q-exactive-orbitrap-mass spectrometry. We then used network pharmacology to predict the underlying mechanisms by which DHLP ameliorates rheumatoid arthritis. We identified 153 chemical compounds from DHLP, together with 27 prototype components absorbed in blood. We selected 48 of these compounds as potential active ingredients to explore the mechanism. These compounds are related to 88 significant pathways, which are linked to 18 core targets. This study preliminarily reveals the potential mechanisms by which DHLP ameliorates rheumatoid arthritis and provides a basis for further evaluation of the drug’s efficacy

Diffusion of Cement Kiln Co-Processing of Contaminated Soil in Selected Provinces of China: Engineering Practices, Modeling, and Driving Factors

Promoting the diffusion of remediation technologies is an attractive solution to environmental protection and urban sustainability challenges. To better understand technology diffusion, we reviewed the engineering practices of cement kiln co-processing (CKC) of contaminated soil and obtained diffusion parameters using the Bass model in three provinces of China. Our results show that CKC has been adopted for the disposal of multiple contaminants and that the optimal feed rate of contaminated soil is 4–5%. The obtained diffusion parameters can be used to analyze and predict CKC diffusion. Driving factors analysis suggest that CKC diffusion is regulation-driven and obeys the S-curve pattern. Policies at the national level shape the basic pattern of the diffusion curve, while local policies, market scales, and contaminant types produce variations in diffusion rates across provinces. Results also reveal that the co-processing quota management on contaminated soil has little impact on CKC adoption. This study provides insights into contaminated soil remediation technology diffusion and the effectiveness of environmental policy implementation at home and abroad