6 citations · 9 across the 4 of their papers we have counts for
6 papers · 1 filter
Excavation Reinforcement Learning Using Geometric Representation
Qingkai Lu, Yifan Zhu, Liangjun Zhang
Excavation of irregular rigid objects in clutter, such as fragmented rocks and wood blocks, is very challenging due to their complex interaction dynamics and highly variable geomet…
Excavation Learning for Rigid Objects in Clutter
Qingkai Lu, Liangjun Zhang
Autonomous excavation for hard or compact materials, especially irregular rigid objects, is challenging due to high variance of geometric and physical properties of objects, and la…
Multi-Fingered Active Grasp Learning
Qingkai Lu, Mark Van der Merwe, Tucker Hermans
Learning-based approaches to grasp planning are preferred over analytical methods due to their ability to better generalize to new, partially observed objects. However, data collec…
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…
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…
Planning Multi-Fingered Grasps as Probabilistic Inference in a Learned Deep Network
Qingkai Lu, Kautilya Chenna, Balakumar Sundaralingam +1
We propose a novel approach to multi-fingered grasp planning leveraging learned deep neural network models. We train a convolutional neural network to predict grasp success as a fu…