47 citations · 114 across the 86 of their papers we have counts for
25 papers · 1 filter
Hölder Signed Distance: A Differentiable, Signed, Parallelizable Metric for Robotics
Felipe Bartelt, Ali Umut Kaypak, Anthony Tzes +3
Computing distances between sets is essential in robotic motion planning and control, where differentiable gradients enable real-time optimization. The Euclidean Signed Distance Fu…
VANDERER: Map-Free Exploration using Future-Aware and Visual-Curiosity-Guided Diffusion Policy
Venkata Naren Devarakonda, Raktim Gautam Goswami, Prashanth Krishnamurthy +1
Mobile agents require efficient exploration strategies to map unseen environments and autonomously plan tasks. Traditional methods rely on generating occupancy maps and optimizing…
Unifying Object-Centric World Models and Diffusion Policy: A Hierarchical Framework for Multi-Stage Robotic Tasks
Raktim Gautam Goswami, Prashanth Krishnamurthy, Yann LeCun +1
Visual world models have shown great potential in learning complex system dynamics. Recent advancements leverage these models as transition functions within Model Predictive Contro…
World Models for Learning Dexterous Hand-Object Interactions from Human Videos
Raktim Gautam Goswami, Amir Bar, David Fan +6
Modeling dexterous hand-object interactions is challenging as it requires understanding how subtle finger motions influence the environment through contact with objects. While rece…
A Differentiable Distance Metric for Robotics Through Generalized Alternating Projection
Vinicius M. Gonçalves, Shiqing Wei, Eduardo Malacarne S. de Souza +3
In many robotics applications, it is necessary to compute not only the distance between the robot and the environment, but also its derivative - for example, when using control bar…
MapleGrasp: Mask-guided Feature Pooling for Language-driven Efficient Robotic Grasping
Vineet Bhat, Naman Patel, Prashanth Krishnamurthy +2
Robotic manipulation of unseen objects via natural language commands remains challenging. Language driven robotic grasping (LDRG) predicts stable grasp poses from natural language…