1 citations · 1 across the 8 of their papers we have counts for
8 papers
InfiNoVA: Infinite Novel View Augmentation for Viewpoint Invariant Robot Policies
Sai Puneeth Reddy Gottam, Elmar Rueckert, Vedant Dave
Vision-Language-Action (VLA) policies often rely strongly on the camera viewpoints seen during training, causing substantial performance degradation when deployed from unseen persp…
Sandwich-Residuals: Parameter-Efficient Test-time Adaptation of World Models
Krishnam Soni, Aditya Sehgal, Vedant Dave +1
Latent world models enable planning by predicting the effects of actions in a learned representation space, but their predictions can become unreliable when test-time conditions di…
VLEM: Real-Time 3D Vision-Language Embedding Mapping
Christian Rauch, Björn Ellensohn, Linus Nwankwo +2
Semantic scene understanding in robotics requires representations that are both metric-accurate and queryable via natural language in real-time. While recent Vision-Language Models…
Amending CALPHAD databases using a neural network for predicting mixing enthalpy of liquids
Clement Vincely, Amin Reiners-Sakic, Vedant Dave +4
In order to establish the thermodynamic stability of a system, knowledge of its Gibbs free energy is essential. Most often, the Gibbs free energy is predicted within the CALPHAD fr…
EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments
Linus Nwankwo, Bjoern Ellensohn, Vedant Dave +5
To ensure the efficiency of robot autonomy under diverse real-world conditions, a high-quality heterogeneous dataset is essential to benchmark the operating algorithms' performance…
Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training
Vedant Dave, Fotios Lygerakis, Elmar Rueckert
The rapidly evolving field of robotics necessitates methods that can facilitate the fusion of multiple modalities. Specifically, when it comes to interacting with tangible objects,…
M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation
Fotios Lygerakis, Vedant Dave, Elmar Rueckert
One of the most critical aspects of multimodal Reinforcement Learning (RL) is the effective integration of different observation modalities. Having robust and accurate representati…
Orientation Probabilistic Movement Primitives on Riemannian Manifolds
Leonel Rozo, Vedant Dave
Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces. Probabil…