arXiv subjects

Leonidas Askianakis

Publications and source records attributed to Leonidas Askianakis.

3 recordsLinked to original sources

arXiv2026

Towards Space-Based Environmentally-Adaptive Grasping

Robotic manipulation in unstructured environments requires reliable execution under diverse conditions, yet many state-of-the-art systems still struggle with high-dimensional action spaces, sparse rewards, and slow generalization beyond carefully curated training scenarios. We study these limitations through the example of grasping in space environments. We learn control policies directly in a learned latent manifold that fuses (grammarizes) multiple modalities into a structured representation for policy decision-making. Building on GPU-accelerated physics simulation, we instantiate a set of single-shot manipulation tasks and achieve over 95% task success with Soft Actor-Critic (SAC)-based reinforcement learning in less than 1M environment steps, under continuously varying grasping conditions from step 1. This empirically shows faster convergence than representative state-of-the-art visual baselines under the same open-loop single-shot conditions. Our analysis indicates that explicitly reasoning in latent space yields more sample-efficient learning and improved robustness to novel object and gripper geometries, environmental clutter, and sensor configurations compared to standard baselines. We identify remaining limitations and outline directions toward fully adaptive and generalizable grasping in the extreme conditions of space.

cs.RO↗

arXiv2025

Continuous In-Situ and Remote Sun Observation for Space Weather Monitoring and Mitigation of Infrastructure Threats Through an Optimized Heliocentric Satellite Constellation

Although vital for life on Earth, solar activity poses questions and increasing threats to humanity due to the Sun's unknown dynamics, intensified by our dependence on terrestrial and space-based infrastructure. This situation is compounded by significant gaps in our understanding of space weather phenomena, the Sun's magnetic field, and the need for rapid responses to unpredicted solar events. To address these issues, an optimized heliocentric satellite constellation is proposed that leverages satellites in an Elliptical Walker Constellation. This system offers (among others) equally distributed arguments of periapsis separations and cross-coupled true anomalies with respect to the Sun-centric coordinate frame. In this paper it is also demonstrated that this strategic multi-spacecraft configuration makes it possible to distinguish spatial and temporal changes in solar wind phenomena, reconstruct, in 3D, Coronal Mass Ejections (CMEs), predict which space or ground-based infrastructure and when it will be affected by CMEs, maintain continuous coverage of the critical Sun-Earth line throughout the mission's duration, and protect future missions by providing simultaneously in-situ and remote measurements from small and cost-effective satellites.

physics.space-ph↗

arXiv2024

Grammarization-Based Grasping with Deep Multi-Autoencoder Latent Space Exploration by Reinforcement Learning Agent

Grasping by a robot in unstructured environments is deemed a critical challenge because of the requirement for effective adaptation to a wide variation in object geometries, material properties, and other environmental factors. In this paper, we propose a novel framework for robotic grasping based on the idea of compressing high-dimensional target and gripper features in a common latent space using a set of autoencoders. Our approach simplifies grasping by using three autoencoders dedicated to the target, the gripper, and a third one that fuses their latent representations. This allows the RL agent to achieve higher learning rates at the initial stages of exploration of a new environment, as well as at non-zero shot grasp attempts. The agent explores the latent space of the third autoencoder for better quality grasp without explicit reconstruction of objects. By implementing the PoWER algorithm into the RL training process, updates on the agent's policy will be made through the perturbation in the reward-weighted latent space. The successful exploration efficiently constrains both position and pose integrity for feasible executions of grasps. We evaluate our system on a diverse set of objects, demonstrating the high success rate in grasping with minimum computational overhead. We found that approach enhances the adaptation of the RL agent by more than 35 % in simulation experiments.

cs.RO↗