12 citations · 21 across the 18 of their papers we have counts for
18 papers
Motion Forecasting via Model-Based Risk Minimization
Aron Distelzweig, Eitan Kosman, Andreas Look +3
Forecasting the future trajectories of surrounding agents is crucial for autonomous vehicles to ensure safe, efficient, and comfortable route planning. While model ensembling has i…
Panoptic-Depth Forecasting
Juana Valeria Hurtado, Riya Mohan, Abhinav Valada
Forecasting the semantics and 3D structure of scenes is essential for robots to navigate and plan actions safely. Recent methods have explored semantic and panoptic scene forecasti…
Learning Robotic Manipulation Policies from Point Clouds with Conditional Flow Matching
Eugenio Chisari, Nick Heppert, Max Argus +3
Learning from expert demonstrations is a promising approach for training robotic manipulation policies from limited data. However, imitation learning algorithms require a number of…
Reinforcement Learning as a Robotics-Inspired Framework for Insect Navigation: From Spatial Representations to Neural Implementation
Stephan Lochner, Daniel Honerkamp, Abhinav Valada +1
Bees are among the master navigators of the insect world. Despite impressive advances in robot navigation research, the performance of these insects is still unrivaled by any artif…
CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects
Sassan Mokhtar, Eugenio Chisari, Nick Heppert +1
Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D…
A Point-Based Approach to Efficient LiDAR Multi-Task Perception
Christopher Lang, Alexander Braun, Lars Schillingmann +1
Multi-task networks can potentially improve performance and computational efficiency compared to single-task networks, facilitating online deployment. However, current multi-task a…