Institution profile

Aalto University

Academic institutioneurope · fi
Official website
Research library689linked papers
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Selected work

Representative Papers

DeMoBot: Deformable Mobile Manipulation with Vision-based Sub-goal Retrieval

Aug 28, 2024arXiv.org

To address the weak generalization of few-shot (20 demonstrations) imitation learning in partially observable environments, this paper proposes a novel demonstration-driven framework for mobile manipulation tasks. Methodologically, it introduces—first in the literature—a vision foundation model–based (e.g., CLIP) demonstration snippet retrieval mechanism that matches observations via visual similarity; integrates trajectory similarity and forward-reachability constraints to filter feasible subgoals; and employs a goal-conditioned diffusion-based motion policy for action generation. The core contribution lies in abandoning end-to-end fitting in favor of a modular, interpretable, and verifiable architecture that ensures subgoal feasibility and policy robustness. Evaluated on both simulation and real-world Spot robot platforms, the framework achieves significantly higher success rates than state-of-the-art baselines: 85%/80% (sim/real) for gap coverage, 87.5%/70% for tabletop cleanup, and 47.5%/35% for curtain opening.

3 citations1 influentialRead paper

Citizen science games on the timeline of quantum games

Aug 23, 2024The European Physical Journal Plus

This study addresses low public engagement in quantum science communication and the lack of systematic gamification frameworks for quantum education. Methodologically, it introduces an interdisciplinary quantum gaming timeline integrating serious game design principles, collaborative annotation platforms, and lightweight quantum conceptual modeling tools. The proposed “citizen science–quantum博弈” coupling paradigm combines timeline-based visualization, gamified learning design, and multilingual task deployment to develop an interactive quantum gaming platform. Empirical evaluation shows that participants achieve over 72% mastery of foundational quantum logic reasoning concepts. The platform supports open quantum hardware integration and human–machine co-experimentation, thereby unifying scientific outreach, pedagogical practice, and frontier research. It delivers a reusable methodology and infrastructure for quantum citizen science, advancing participatory quantum literacy through structured, scalable, and empirically validated design.

3 citationsRead paper

On Geometric Bipartite Graphs with Asymptotically Smallest Zarankiewicz Numbers

Oct 23, 2025

This paper investigates the Zarankiewicz problem for bipartite graphs of low Ferrers dimension—i.e., maximizing the number of edges while forbidding a $K_{k,k}$ subgraph. Focusing on Ferrers dimensions 3 and 4, we establish the first phase-transition phenomenon: edge bounds are linear in $n$ for dimension 3, whereas dimension 4 triggers a sharp complexity jump. Our approach integrates extremal graph theory, combinatorial geometry, and Ferrers-structure decomposition, augmented by a novel bichromatic diagonal argument. We derive tight asymptotic upper bounds: $2n(k-1)$ for chordal bipartite graphs and $54n(k-1)$ for grid intersection graphs—substantially improving prior exponential dependencies of the form $O(2^{O(k)}n)$. This work provides the first fine-grained threshold analysis of extremal behavior driven by Ferrers dimension and establishes optimal asymptotic orders for the Zarankiewicz problem on chordal bipartite and intersection graph classes.

2 citationsRead paper

Recursive Decomposition with Dependencies for Generic Divide-and-Conquer Reasoning

May 05, 2025

Large language models (LLMs) suffer from poor scalability, low inference efficiency, and heavy reliance on abundant task-specific supervision for complex reasoning tasks. Method: This paper proposes Recursive Decomposition and Dependency (RDD), a general divide-and-conquer framework that operates without task-specific examples. RDD integrates explicit subtask dependency modeling and an automatic backtracking-based error recovery mechanism into an unsupervised reasoning pipeline, enabling end-to-end problem solving via LLM-driven recursive decomposition, dependency graph construction, and ordered subtask scheduling. Contribution/Results: Evaluated across two benchmarks spanning six difficulty levels, RDD significantly outperforms chain-of-thought and other baselines under identical computational budgets—especially on high-complexity tasks—while simultaneously improving inference efficiency and cross-task generalization.

2 citationsRead paper

Post-hoc Probabilistic Vision-Language Models

Dec 08, 2024arXiv.org

Vision-language models (VLMs) such as CLIP lack principled uncertainty quantification under domain shift, as their deterministic input-to-embedding mappings cannot capture posterior uncertainty induced by distributional shifts. To address this, we propose the first post-hoc Bayesian approximation framework for VLMs that requires no retraining. By imposing learnable Gaussian priors on the final-layer text and image embedding parameters, we analytically derive the posterior distribution of cosine similarity—enabling rigorous uncertainty quantification. Our method supports plug-and-play uncertainty calibration and interpretable analysis, and guides high-quality support-set selection in active learning. Experiments demonstrate significant improvements: a 32% reduction in expected calibration error (ECE) and a +4.7% gain in downstream task accuracy under equal sample budgets. The approach delivers reliable, calibrated uncertainty estimates critical for safety-critical applications.

2 citationsRead paper
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