Dimitrios Bekris, Marios Glytsos, Panagiotis P. Filntisis, Niki Efthymiou, George Retsinas, Petros Maragos
Robotics and AI systems are increasingly deploying large vision-language models (LVLMs) to follow high-level instructions, from routine household chores to more complex organizational tasks. Yet, broad directives like "clean the room" or "organize the desk" frequently omit crucial details about user preferences and specific arrangement of objects that influence the desired outcome. Without any further guidance, such models tend to overestimate their grasp of ambiguous instructions and frequently assume they have sufficient understanding despite missing important details. In this paper, we propose CLARA (Conversational cLArification for Robotic Arrangement), a cognitively-driven clarification framework that proactively elicits key information from users, in a similar fashion as humans' clarification seeking behavior. Through targeted questioning, CLARA refines ambiguous requests into structured, detailed instructions describing precisely how objects should be organized or rearranged, according to the user's preferences. These clarifications then guide a diffusion-based image generation pipeline, producing realistic visual renderings of the final, desired scene. This final rendering also serves as a visual confirmation of the system's understanding from the user. Experimental results and user studies demonstrate that CLARA leads to more accurate and consistent outcomes compared to another State-of-the-Art LVLM system, highlighting the importance of interactive clarification for bridging the gap between vague directives and fully grounded task specifications.
Vasiliki Vasileiou, Panagiotis P. Filntisis, Petros Maragos, Kostas Daniilidis
Monocular head pose estimation is usually formulated as absolute regression from a single face crop, yet many applications observe the same person over time and can supply an explicit anchor image. Recently, relative head pose estimation was proposed, which casts the problem as estimation of the relative geometric change between an anchor and a query image. However, existing methods still require the anchor image pose to be known at inference, significantly limiting their practical use. We first extend relative head pose estimation to full 6DoF by additionally predicting a depth-normalized relative translation, which composes with the anchor to recover metric query pose in the camera frame. To eliminate the need for a ground-truth anchor pose, we reconstruct a personalized avatar from a single unlabeled enrollment image and render a bank of virtual anchors with known poses. The bank provides robust rotation hypotheses through multi-anchor consensus, while the real enrollment image serves as the real anchor in the translation branch via landmark PnP. We also introduce an efficient variant that amortizes the bank cost once at enrollment, after which each query requires a single real-to-real relative prediction, with limited accuracy sacrifice. The relative pose estimator is trained exclusively on synthetic face renderings, without real pose supervision, pseudo-labels, domain adaptation, or fine-tuning on the evaluation sets. On real 6DoF benchmarks, ReVA-HPE achieves state-of-the-art rotation accuracy on BIWI and, given a near-frontal enrollment image, on FaMoS, while remaining competitive in metric translation despite relying solely on synthetic pose supervision. Project page and code: https://vasilikivas.github.io/ReVA-HPE/
Panagiotis Roditis, Panagiotis P. Filntisis, Petros Maragos
Actor--critic methods in reinforcement learning control overestimation bias by taking the minimum of two critics. This uses the same aggregation rule everywhere, regardless of how the critics disagree. We propose Geometric Zonotope Soft Actor-Critic (GeZo-SAC), which uses auxiliary geometric representations to adapt critic pessimism to the state and action. Alongside its scalar value, each critic predicts a set of generators defining a zonotope. Probing this zonotope along sampled directions provides a geometric width, subtracted from each critic value as a pessimistic offset, and a measure of disagreement between the two critics, aggregated with log-sum-exp as a smooth approximation to the tropical (max-plus) addition. This disagreement controls how the critics are combined, moving from a width-weighted average toward the usual minimum as disagreement increases. At inference, the deployed policy is an unmodified SAC actor, since the generators are used only on the critic side during training. Across four MuJoCo-v5 locomotion benchmarks and six off-policy baselines, GeZo-SAC achieves the highest mean return on Ant-v5 and Hopper-v5 and remains competitive with other methods on the remaining tasks. Our analysis further shows that GeZo-SAC achieves the lowest average actuator work and action effort per metre among the evaluated methods, while maintaining near-zero measured overestimation frequency across all four environments.