Here is the abstract of the work, directly in the words of the authors:

This paper presents a unique sim-to-real assessment environment for traffic signal control (TSC), LibSignal++, featuring a 14-ft by 14-ft scaled-down physical replica of a real-world urban roadway equipped with realistic traffic sensors such as cameras, and actual traffic signal controllers. Besides, it is supported by a precise indoor positioning system to track the actual trajectories of vehicles. To generate various plausible physical conditions that are difficult to replicate with computer simulations, this system supports automatic sensor manipulation to mimic observation changes and also supports manual adjustment of physical traffic network settings to reflect the influence of dynamic changes on vehicle behaviors. This system will enable the assessment of traffic policies that are otherwise extremely difficult to simulate or infeasible for full-scale physical tests, providing a reproducible and low-cost environment for sim-to-real transfer research on traffic signal control problems.

Here is the abstract of the work, directly in the words of the authors:

Simulation-to-reality (sim-to-real) reinforcement learning (RL) faces the critical challenge of reconciling discrepancies between simulated and real-world dynamics, which can severely degrade agent performance. A promising approach involves learning corrections to simulator forward dynamics represented as a residual error function, however this operation is impractical with high-dimensional states such as images. To overcome this, we propose ReDRAW, a latent-state autoregressive world model pretrained in simulation and calibrated to target environments through residual corrections of latent-state dynamics rather than of explicit observed states. Using this adapted world model, ReDRAW enables RL agents to be optimized with imagined rollouts under corrected dynamics and then deployed in the real world. In multiple vision-based MuJoCo domains and a physical robot visual lane-following task, ReDRAW effectively models changes to dynamics and avoids overfitting in low data regimes where traditional transfer methods fail.

In the author’s words:

Accurate obstacle recognition and avoidance are critical for ensuring the safety and operational efficiency of autonomous robots in dynamic and complex environments. Despite significant advances in deep-learning techniques in these areas, their adaptability in dynamic and complex environments remains a challenge. To address these challenges, we propose an improved Transformer-based architecture, GAS-H-Trans. 

This approach uses a grouped aggregation strategy to improve the robot’s semantic understanding of the environment and enhance the accuracy of its obstacle avoidance strategy. This method employs a Transformer-based dual-coupling grouped aggregation strategy to optimize feature extraction and improve global feature representation, allowing the model to capture both local and long-range dependencies. 

The Harris hawk optimization (HHO) algorithm is used for hyperparameter tuning, further improving model performance. A key innovation of applying the GAS-H-Trans model to obstacle avoidance tasks is the implementation of a secondary precise image segmentation strategy. By placing observation points near critical obstacles, this strategy refines obstacle recognition, thus improving segmentation accuracy and flexibility in dynamic motion planning. The particle swarm optimization (PSO) algorithm is incorporated to optimize the attractive and repulsive gain coefficients of the artificial potential field (APF) methods. 

This approach mitigates local minima issues and enhances the global stability of obstacle avoidance. Comprehensive experiments are conducted using multiple publicly available datasets and the Unity3D virtual robot environment. The results show that GAS-H-Trans significantly outperforms existing baseline models in image segmentation tasks, achieving the highest mIoU (85.2%). In virtual environment obstacle avoidance tasks, the GAS-H-Trans + PSO-optimized APF framework achieves an impressive obstacle avoidance success rate of 93.6%. These results demonstrate that the proposed approach provides superior performance in dynamic motion planning, offering a promising solution for real-world autonomous navigation applications.

In the author’s words:

Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions. However, OOD detectors are often implemented using deep neural networks, which makes it difficult to meet real-time deadlines on embedded systems with memory and power constraints. We consider the class of variational autoencoder (VAE) based OOD detectors where OOD detection is performed in latent space, and apply quantization, pruning, and knowledge distillation. 

These techniques have been explored for other deep models, but no work has considered their combined effect on latent space OOD detection. While these techniques increase the VAE’s test loss, this does not correspond to a proportional decrease in OOD detection performance and we leverage this to develop lean OOD detectors capable of real-time inference on embedded CPUs and GPUs. We propose a design methodology that combines all three compression techniques and yields a significant decrease in memory and execution time while maintaining AUROC for a given OOD detector. 

We demonstrate this methodology with two existing OOD detectors on a Jetson Nano and reduce GPU and CPU inference time by 20% and 28% respectively while keeping AUROC within 5% of the baseline.