👨🏻💻 I’m a fifth year robotics PhD student at University of Michigan in the MMINT Lab, where I work under the guidance of Prof. Nima Fazeli.
🔬 My research focuses on tactile-based manipulation, combining learning, optimization, and controls to enable robots to interact more intelligently and effectively with their environments.
🎓 Excited to be approaching graduation (March 2025) and eager to explore opportunities to push the boundaries of AI, machine learning, and robotics!
We introduce Tactile Neural De-rendering, a novel approach that leverages a generative model to reconstruct a local 3D representation of an object based solely on its tactile signature.
Samanta Rodriguez, Yiming Dou, William van den Bogert, Miquel Oller, Kevin So, Andrew Owens, Nima Fazeli
Under Review, 2024
We present a contrastive self-supervised learning method to unify tactile feedback across different sensors, using paired tactile data. By treating paired signals as positives and unpaired ones as negatives, our approach learns a sensor-agnostic latent representation, capturing shared information without relying on reconstruction or task-specific supervision.
Samanta Rodriguez, Yiming Dou, Miquel Oller, Andrew Owens, Nima Fazeli
Under Review, 2024
The diversity of touch sensor designs complicates general-purpose tactile processing. We address this by training a diffusion model for cross-modal prediction, translating tactile signals between GelSlim and Soft Bubble sensors. This enables sensor-specific methods to be applied across sensor types.
Youngsun Wi, Jayjun Lee, Miquel Oller, Nima Fazeli
8th Conference on Robotic Learning (CoRL), 2024
We propose a Physics-Informed Neural Network (PINN) approach for solving inverse source problems in robotics, jointly identifying unknown source functions and system states from partial, noisy observations. Our method integrates diverse constraints, avoids complex discretizations, accommodates real measurement gradients, and is not limited by training data quality.
We consider the problem of non-prehensile manipulation with highly compliant and high-resolution tactile sensors. Our approach considers contact mechanics and sensor dynamics to achive desired object poses and transmitted forces and is amenable for gradient-based optimization.
Our paper introduces TactileVAD, a decoder-only control method that resolves tactile geometric aliasing, improving performance and reliability in touch-based manipulation across various tactile sensors.
Miquel Oller, Mireia Planas, Dmitry Berenson, Nima Fazeli
6th Conference on Robotic Learning (CoRL), 2022
Our method learns soft tactile sensor membrane deformation dynamics to control a grasped object’s pose and force transmitted to the environment during contact-rich manipulation tasks such as drawing and in-hand pivoting.
Nima Fazeli, Miquel Oller, Jiajun Wu, Zheng Wu, J. B. Tenenbaum, Alberto Rodriguez
Science Robotics, 2019
This work introduces a methodology for robots to learn complex manipulation skills, such as playing Jenga, by emulating hierarchical reasoning and multisensory fusion through a temporal hierarchical Bayesian model. By leveraging learned tactile and visual representations, the robot adapts its actions and strategies similar to human gameplay.
