49 citations · 194 across the 54 of their papers we have counts for
8 papers · 1 filter
A Model Predictive Approach for Online Mobile Manipulation of Nonholonomic Objects using Learned Dynamics
Roya Sabbagh Novin, Amir Yazdani, Andrew Merryweather +1
A particular type of assistive robots designed for physical interaction with objects could play an important role assisting with mobility and fall prevention in healthcare faciliti…
Contextual Reinforcement Learning of Visuo-tactile Multi-fingered Grasping Policies
Visak Kumar, Tucker Hermans, Dieter Fox +2
Using simulation to train robot manipulation policies holds the promise of an almost unlimited amount of training data, generated safely out of harm's way. One of the key challenge…
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
Mark Van der Merwe, Qingkai Lu, Balakumar Sundaralingam +2
Deep learning has enabled remarkable improvements in grasp synthesis for previously unseen objects from partial object views. However, existing approaches lack the ability to expli…
Learning to Manipulate Object Collections Using Grounded State Representations
Matthew Wilson, Tucker Hermans
We propose a method for sim-to-real robot learning which exploits simulator state information in a way that scales to many objects. We first train a pair of encoder networks to cap…
Assembly Planning by Subassembly Decomposition Using Blocking Reduction
James Watson, Tucker Hermans
The sequence in which a complex product is assembled directly impacts the ease and efficiency of the assembly process, whether executed by a human or a robot. A sequence that gives…
Active Learning of Probabilistic Movement Primitives
Adam Conkey, Tucker Hermans
A Probabilistic Movement Primitive (ProMP) defines a distribution over trajectories with an associated feedback policy. ProMPs are typically initialized from human demonstrations a…
Building 3D Object Models during Manipulation by Reconstruction-Aware Trajectory Optimization
Kanrun Huang, Tucker Hermans
Object shape provides important information for robotic manipulation; for instance, selecting an effective grasp depends on both the global and local shape of the object of interes…
Modeling Grasp Type Improves Learning-Based Grasp Planning
Qingkai Lu, Tucker Hermans
Different manipulation tasks require different types of grasps. For example, holding a heavy tool like a hammer requires a multi-fingered power grasp offering stability, while hold…