arXiv subjects

Bassel El Mabsout

Publications and source records attributed to Bassel El Mabsout.

5 recordsLinked to original sources

arXiv2026

NeoRacer: An Open, Standardized 1:12 Scale Autonomous Race Car for Benchmarking and Education

Many scientific fields rely on standard benchmarks and shared platforms to improve review and reproducibility, but autonomous systems research still lacks widely accepted open hardware. Where standardization has emerged, progress has accelerated. This is especially evident in autonomous racing, where teams often build custom systems or buy niche, expensive vehicles, making control and robotics research and education hard to compare and reproduce. High costs also limit access outside well-funded labs, while affordable educational robots are often underpowered. To address this gap, we present NeoRacer, an open-source 1:12 scale autonomous racing platform. It is built around an NVIDIA Jetson Orin Nano (67 TOPS), a 270{\deg} LiDAR, a 120 fps global-shutter camera, and a 9-axis IMU. NeoRacer ships pre-assembled for USD 2,699, offering over 3x the compute of comparable platforms at less than half the cost of the nearest pre-assembled alternative. Co-developed by the Neobotics Foundation and Seeed Studio, and manufactured by Seeed Studio, NeoRacer combines open hardware and software design with scalable, repeatable production. The modular, extensible platform provides a standardized benchmarking environment for autonomous racing algorithms across institutions. We describe the hardware/software architecture, design decisions from two pilot deployments (MIT IAP, 15 students; BU CPS Lab, 10 students), and key cost-performance tradeoffs. Hardware is licensed under CERN-OHL-S v2 and software under GPLv3, with all design files, firmware, and ROS2 packages publicly accessible.

cs.RO↗

arXiv2025

Closing the Intent-to-Behavior Gap via Fulfillment Priority Logic

Practitioners designing reinforcement learning policies face a fundamental challenge: translating intended behavioral objectives into representative reward functions. This challenge stems from behavioral intent requiring simultaneous achievement of multiple competing objectives, typically addressed through labor-intensive linear reward composition that yields brittle results. Consider the ubiquitous robotics scenario where performance maximization directly conflicts with energy conservation. Such competitive dynamics are resistant to simple linear reward combinations. In this paper, we present the concept of objective fulfillment upon which we build Fulfillment Priority Logic (FPL). FPL allows practitioners to define logical formula representing their intentions and priorities within multi-objective reinforcement learning. Our novel Balanced Policy Gradient algorithm leverages FPL specifications to achieve up to 500\% better sample efficiency compared to Soft Actor Critic. Notably, this work constitutes the first implementation of non-linear utility scalarization design, specifically for continuous control problems.

cs.LG↗

arXiv2025

Sim-Anchored Learning for On-the-Fly Adaptation

Fine-tuning simulation-trained RL agents with real-world data often degrades crucial behaviors due to limited or skewed data distributions. We argue that designer priorities exist not just in reward functions, but also in simulation design choices like task selection and state initialization. When adapting to real-world data, agents can experience catastrophic forgetting in important but underrepresented scenarios. We propose framing live-adaptation as a multi-objective optimization problem, where policy objectives must be satisfied both in simulation and reality. Our approach leverages critics from simulation as "anchors for design intent" (anchor critics). By jointly optimizing policies against both anchor critics and critics trained on real-world experience, our method enables adaptation while preserving prioritized behaviors from simulation. Evaluations demonstrate robust behavior retention in sim-to-sim benchmarks and a sim-to-real scenario with a racing quadrotor, allowing for power consumption reductions of up to 50% without control loss. We also contribute SwaNNFlight, an open-source firmware for enabling live adaptation on similar robotic platforms.

cs.RO↗

arXiv2024

Survey on safe robot control via learning

Control systems are critical to modern technological infrastructure, spanning industries from aerospace to healthcare. This survey explores the landscape of safe robot learning, investigating methods that balance high-performance control with rigorous safety constraints. By examining classical control techniques, learning-based approaches, and embedded system design, the research seeks to understand how robotic systems can be developed to prevent hazardous states while maintaining optimal performance across complex operational environments.

cs.RO↗

arXiv2024

Scrap Your Schedules with PopDescent

In contemporary machine learning workloads, numerous hyper-parameter search algorithms are frequently utilized to efficiently discover high-performing hyper-parameter values, such as learning and regularization rates. As a result, a range of parameter schedules have been designed to leverage the capability of adjusting hyper-parameters during training to enhance loss performance. These schedules, however, introduce new hyper-parameters to be searched and do not account for the current loss values of the models being trained. To address these issues, we propose Population Descent (PopDescent), a progress-aware hyper-parameter tuning technique that employs a memetic, population-based search. By merging evolutionary and local search processes, PopDescent proactively explores hyper-parameter options during training based on their performance. Our trials on standard machine learning vision tasks show that PopDescent converges faster than existing search methods, finding model parameters with test-loss values up to 18% lower, even when considering the use of schedules. Moreover, we highlight the robustness of PopDescent to its initial training parameters, a crucial characteristic for hyper-parameter search techniques.

cs.LG↗