arXiv subjects

Ahmed Abdelrahman

Publications and source records attributed to Ahmed Abdelrahman.

7 recordsLinked to original sources

arXiv2026

SPECTRA: Context-Conditioned Spectral Movement Primitives for Robot Skill Generalization

Robot imitation learning for manipulation should preserve demonstrated task geometry while producing dynamically admissible robot motions. Existing pipelines often learn task-dependent trajectories and impose execution limits afterward through filtering, smoothing, clipping, or time scaling, which may distort task-critical end-effector paths. We propose the Spectral Movement Primitive (SMP), a frequency-domain imitation learning framework that couples task-space skill generation with joint-space execution regulation. Demonstrations are represented by truncated finite-horizon Fourier coefficients. An empirically selected low-frequency task band captures the dominant motion geometry, while higher harmonics contribute disproportionately to derivative growth. A frame-aware context-conditioned GMM/GMR prior predicts the task-band coefficients in a canonical task frame, and the resulting Cartesian trajectory is mapped to joint space through sequential inverse kinematics. A phase-coupled regulator then limits the requested phase progression without modifying the spectral coefficients, thereby enforcing joint velocity and acceleration limits while preserving the represented path. Experiments evaluate task-band reconstruction, robustness to composite demonstration corruption, out-of-distribution cross-board generalization, joint-space dynamic admissibility, end-effector path preservation, and deployment on a Franka Panda robot. Results show compact geometric reconstruction, consistent transfer across unseen task frames, substantial reductions in dynamic violations and jerk, and preservation of the intended end-effector path during phase regulation.

cs.RO↗

arXiv2023

Scalable Decentralized Cooperative Platoon using Multi-Agent Deep Reinforcement Learning

Cooperative autonomous driving plays a pivotal role in improving road capacity and safety within intelligent transportation systems, particularly through the deployment of autonomous vehicles on urban streets. By enabling vehicle-to-vehicle communication, these systems expand the vehicles environmental awareness, allowing them to detect hidden obstacles and thereby enhancing safety and reducing crash rates compared to human drivers who rely solely on visual perception. A key application of this technology is vehicle platooning, where connected vehicles drive in a coordinated formation. This paper introduces a vehicle platooning approach designed to enhance traffic flow and safety. Developed using deep reinforcement learning in the Unity 3D game engine, known for its advanced physics, this approach aims for a high-fidelity physical simulation that closely mirrors real-world conditions. The proposed platooning model focuses on scalability, decentralization, and fostering positive cooperation through the introduced predecessor-follower "sharing and caring" communication framework. The study demonstrates how these elements collectively enhance autonomous driving performance and robustness, both for individual vehicles and for the platoon as a whole, in an urban setting. This results in improved road safety and reduced traffic congestion.

cs.RO↗

arXiv2023

Development and Assessment of Autonomous Vehicles in Both Fully Automated and Mixed Traffic Conditions

Autonomous Vehicle (AV) technology is advancing rapidly, promising a significant shift in road transportation safety and potentially resolving various complex transportation issues. With the increasing deployment of AVs by various companies, questions emerge about how AVs interact with each other and with human drivers, especially when AVs are prevalent on the roads. Ensuring cooperative interaction between AVs and between AVs and human drivers is critical, though there are concerns about possible negative competitive behaviors. This paper presents a multi-stage approach, starting with the development of a single AV and progressing to connected AVs, incorporating sharing and caring V2V communication strategy to enhance mutual coordination. A survey is conducted to validate the driving performance of the AV and will be utilized for a mixed traffic case study, which focuses on how the human drivers will react to the AV driving alongside them on the same road. Results show that using deep reinforcement learning, the AV acquired driving behavior that reached human driving performance. The adoption of sharing and caring based V2V communication within AV networks enhances their driving behavior, aids in more effective action planning, and promotes collaborative behavior amongst the AVs. The survey shows that safety in mixed traffic cannot be guaranteed, as we cannot control human ego-driven actions if they decide to compete with AV. Consequently, this paper advocates for enhanced research into the safe incorporation of AVs on public roads.

cs.MA↗

arXiv2023

Automated Deception Detection from Videos: Using End-to-End Learning Based High-Level Features and Classification Approaches

Deception detection is an interdisciplinary field attracting researchers from psychology, criminology, computer science, and economics. We propose a multimodal approach combining deep learning and discriminative models for automated deception detection. Using video modalities, we employ convolutional end-to-end learning to analyze gaze, head pose, and facial expressions, achieving promising results compared to state-of-the-art methods. Due to limited training data, we also utilize discriminative models for deception detection. Although sequence-to-class approaches are explored, discriminative models outperform them due to data scarcity. Our approach is evaluated on five datasets, including a new Rolling-Dice Experiment motivated by economic factors. Results indicate that facial expressions outperform gaze and head pose, and combining modalities with feature selection enhances detection performance. Differences in expressed features across datasets emphasize the importance of scenario-specific training data and the influence of context on deceptive behavior. Cross-dataset experiments reinforce these findings. Despite the challenges posed by low-stake datasets, including the Rolling-Dice Experiment, deception detection performance exceeds chance levels. Our proposed multimodal approach and comprehensive evaluation shed light on the potential of automating deception detection from video modalities, opening avenues for future research.

cs.CV↗

arXiv2018

Surface trap with dc-tunable ion-electrode distance

We describe the design, fabrication, and operation of a novel surface-electrode Paul trap that produces a radio-frequency-null along the axis perpendicular to the trap surface. This arrangement enables control of the vertical trapping potential and consequentially the ion-electrode distance via dc-electrodes only. We demonstrate confinement of single $^{40}$Ca$^+$ ions at heights between $50~μ$m and $300~μ$m above planar copper-coated aluminium electrodes. We investigate micromotion in the vertical direction and show cooling of both the planar and vertical motional modes into the ground state. This trap architecture provides a platform for precision electric-field noise detection, trapping of vertical ion strings without excess micromotion, and may have applications for scalable quantum computers with surface ion traps.

quant-ph↗

arXiv2017

Local probe of single phonon dynamics in warm ion crystals

The detailed characterization of non-trivial coherence properties of composite quantum systems of increasing size is an indispensable prerequisite for scalable quantum computation, as well as for understanding of nonequilibrium many-body physics. Here we show how autocorrelation functions in an interacting system of phonons as well as the quantum discord between distinct degrees of freedoms can be extracted from a small controllable part of the system. As a benchmark, we show this in chains of up to 42 trapped ions, by tracing a single phonon excitation through interferometric measurements of only a single ion in the chain. We observe the spreading and partial refocusing of the excitation in the chain, even on a background of thermal excitations. We further show how this local observable reflects the dynamical evolution of quantum discord between the electronic state and the vibrational degrees of freedom of the probe ion.

quant-ph↗

arXiv2014

Macroscopic quantum information processing using spin coherent states

Previously a new scheme of quantum information processing based on spin coherent states of two component Bose-Einstein condensates was proposed (Byrnes {\it et al.} Phys. Rev. A 85, 40306(R)). In this paper we give a more detailed exposition of the scheme, expanding on several aspects that were not discussed in full previously. The basic concept of the scheme is that spin coherent states are used instead of qubits to encode qubit information, and manipulated using collective spin operators. The scheme goes beyond the continuous variable regime such that the full space of the Bloch sphere is used. We construct a general framework for quantum algorithms to be executed using multiple spin coherent states, which are individually controlled. We illustrate the scheme by applications to quantum information protocols, and discuss possible experimental implementations. Decoherence effects are analyzed under both general conditions and for the experimental implementation proposed.

quant-ph↗