Vedant Dave

Hey! I'm a Ph.D. student at Cyber-Physical-Systems Lab at Montanuniversität Leoben in Austria and advised by Elmar Rueckert. I'm broadly interested in finding better representations from multiple sensory modalities under noisy conditions. My work involves unsupervised RL, robust representation in RL, Multimodality, and Robot Manipulation.

I received my Master's degree in Automation and Robotics from Technische Universität Dortmund in 2021, focusing on Robotics. My thesis, entitled “Model-agnostic Reinforcement Learning Solution for Autonomous Programming of Robotic Motion,” was completed at Mercedes-Benz AG, where I implemented reinforcement learning for motion planning of manipulators in complex environments. Prior to this, I was fortunate to work as a research intern with Leonel Rozo at the Bosch Center for Artificial Intelligence, where I worked on Probabilistic Movement Primitives on Riemannian Manifolds.

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Publications and Preprints

DPI is an information-theoretic approach for reinforcement learning that minimizes past information while retaining future-relevant data, enabling denoised predictions. Using Soft Actor-Critic agents with an auxiliary loss, the method outperforms nine state-of-the-art approaches in complex environments with natural video backgrounds.

M2CURL builds on MViTac to improves RL by efficiently integrating visual and tactile representations. It accelerates learning in downstream manipulation tasks.

MViTac is self-supervised approach that integrates vision and tactile modalities using contrastive learning. MViTac utilizes intra- and inter-modality relationships to learn a shared representation space. Experiments show that MViTac outperforms state-of-the-art methods in downstream tasks, which are performed by linear probing.

This paper contains late-breaking results for TacProMPs, which learns and predicts complex arm movements based on tactile responses. Experiments were conducted on the real robot with a wide variety of objects.

TacProMPs learns and predicts complex arm movements based on tactile responses, particularly for manipulating non-uniform objects, demonstrating adaptability diverse grasping scenarios.

This paper introduces a Riemannian formulation of ProMPs for encoding and retrieving quaternion trajectories, enabling full-pose robot motions in operational spaces. This method builds on Riemannian manifold theory and exploits multilinear geodesic regression for estimating the ProMPs parameters.

Industrial Collaborations

Consultant
2022/03-2023/02

Predicting Yield Strength of different materials from production process. Designing Neural Networks and optimization. Found out error in measurement inaccuracy from data analysis.

Academic Collaborations

This research evaluates hole conditioning operations in wellbore drilling, focusing on activities like circulation, reaming, and washing to ensure well integrity. An agent is trained with model-free RL coming from the online data from the real-world scenarios.

Coding and Supervision

This study predicts the floor level from the distribution of TCEs (Li, Be, V, Ga, Ge, Nb, Sb, Te, Ta, Tl, Bi, and REYs) in Vienna's urban aerosol.

Coding and Supervision

A physics-informed neural network combined with the CALPHAD formalism predicts Gibbs energy in alloys by determining the Redlich-Kister parameter using novel descriptors. This method enhances CALPHAD parameterization, expediting materials development and phase stability determination with high accuracy and potential.

Teaching Experience

Reviewing Services

2024: ICLR, NeurIPS, ECAI, CoRL, IROS, BioRob
2023: ECAI, CoRL, IROS, RAL
2022: ECAI, CoRL, IROS, RAL, Humanoids

Media