Jaesung Lee

Chanyoung Ahn, Jaesung Lee, Sungwoo Park et al.

Dexterous in-hand manipulation requires continuous 6D pose tracking, yet the manipulating fingers inevitably occlude the object from the camera. We study how to structure the sparse haptic signals already available on multi-fingered hands, including proprioception, proximal force/torque, and binary contact, to complement a pretrained visual pose tracker under occlusion. We propose a kinematic-aware finger-level encoder and systematically compare it against four alternative designs through three levels of evaluation: per-frame refinement, sequential open-loop tracking, and closed-loop manipulation. Our experiments reveal that (i) per-frame evaluation cannot distinguish encoder quality, while sequential tracking amplifies architectural differences by up to 15 times; (ii) the structured encoder learns task-specific cross-modal gating, using vision exclusively for translation and dedicating one attention head to haptics for rotation, without explicit supervision; and (iii) compact finger-level tokenization with 4 tokens outperforms both flat fusion and joint-level representations, which suppress vision through norm dominance. We validate that improved tracking yields higher success in a downstream reorientation task and provide qualitative real-world demonstrations. Our project page is available at https://cold-young.github.io/kine-fuse/.

Chanyoung Ahn, Jaesung Lee, Donhyun Hwang

Multisensory integration, particularly through visual and tactile feedback, plays a crucial role in enhancing audience engagement with artworks. Although recent research has increasingly explored tactile experiences in art, existing systems often lack real-time variable stiffness modulation and depend on bulky mechanical infrastructures. In this work, we propose a novel tangible display based on a magnetic jamming mechanism, enabling real-time, low-noise, and low-voltage stiffness modulation integrated into traditional sculptural artworks. Our system combines visual motion and dynamic tactile feedback within a compact standalone module, allowing audiences to interactively experience variations in the rigidity and form of features such as those found in the traditional Korean mask Hahoetal. This approach offers a new paradigm for interactive art, enabling more immersive, multisensory engagement through the fusion of cultural artifacts and modern technology. Our project page is available at https://cold-young.github.io/jamming_tangible/.

Tatthapong Srikitrungruang, Jaesung Lee

Estimating spatially heterogeneous elastic properties from low-resolution displacement measurements is a severely ill-posed inverse elasticity problem because low resolution obscures spatial details needed to distinguish heterogeneous property variations, and small measurement perturbations or fitting errors are amplified through inverse estimation. Existing inverse methods often rely on high-fidelity observations and manually prespecified loss weights, limiting their adaptability and making them sensitive to noise and resolution degradation. We propose a Probabilistic Inverse Elasticity Physics-Informed Neural Network (PIE-PINN) framework for robust estimation of Young's modulus and Poisson's ratio from noisy, low-resolution displacement data. PIE-PINN models displacement observation, strain-discrepancy, and equilibrium residuals using Laplace distributions within a unified probabilistic model. To improve robustness, the framework combines a B-spline-guided displacement network with a hierarchical half-Cauchy model for displacement residual scales. The B-spline provides a smooth global representation of the displacement field, while the neural network correction captures local variations. The hierarchical scale model adaptively downweights severe displacement fitting errors, enabling more robust recovery of the latent mean displacement field. An alternating maximum-likelihood training strategy updates the mean through weighted residual minimization and updates the scales to adjust the loss weights. Systematic case studies across varying noise levels and observation resolutions demonstrate the robustness of PIE-PINN.