1 citations · 1 across the 8 of their papers we have counts for
9 papers
Integration of the TIAGo Robot into Isaac Sim with Mecanum Drive Modeling and Learned S-Curve Velocity Profiles
Vincent Schoenbach, Marvin Wiedemann, Raphael Memmesheimer +2
Efficient physics simulation has significantly accelerated research progress in robotics applications such as grasping and assembly. The advent of GPU-accelerated simulation framew…
Interactive Shaping of Granular Media Using Reinforcement Learning
Benedikt Kreis, Malte Mosbach, Anny Ripke +3
Autonomous manipulation of granular media, such as sand, is crucial for applications in construction, excavation, and additive manufacturing. However, shaping granular materials pr…
Prompt-responsive Object Retrieval with Memory-augmented Student-Teacher Learning
Malte Mosbach, Sven Behnke
Building models responsive to input prompts represents a transformative shift in machine learning. This paradigm holds significant potential for robotics problems, such as targeted…
SOLD: Slot Object-Centric Latent Dynamics Models for Relational Manipulation Learning from Pixels
Malte Mosbach, Jan Niklas Ewertz, Angel Villar-Corrales +1
Learning a latent dynamics model provides a task-agnostic representation of an agent's understanding of its environment. Leveraging this knowledge for model-based reinforcement lea…
Grasp Anything: Combining Teacher-Augmented Policy Gradient Learning with Instance Segmentation to Grasp Arbitrary Objects
Malte Mosbach, Sven Behnke
Interactive grasping from clutter, akin to human dexterity, is one of the longest-standing problems in robot learning. Challenges stem from the intricacies of visual perception, th…
Learning Generalizable Tool Use with Non-rigid Grasp-pose Registration
Malte Mosbach, Sven Behnke
Tool use, a hallmark feature of human intelligence, remains a challenging problem in robotics due the complex contacts and high-dimensional action space. In this work, we present a…
Accelerating Interactive Human-like Manipulation Learning with GPU-based Simulation and High-quality Demonstrations
Malte Mosbach, Kara Moraw, Sven Behnke
Dexterous manipulation with anthropomorphic robot hands remains a challenging problem in robotics because of the high-dimensional state and action spaces and complex contacts. Neve…
Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies
Malte Mosbach, Sven Behnke
Grasping objects of different shapes and sizes - a foundational, effortless skill for humans - remains a challenging task in robotics. Although model-based approaches can predict s…
Fourier-based Video Prediction through Relational Object Motion
Malte Mosbach, Sven Behnke
The ability to predict future outcomes conditioned on observed video frames is crucial for intelligent decision-making in autonomous systems. Recently, deep recurrent architectures…