1 citations · 2 across the 4 of their papers we have counts for
7 papers
Getting Out and Getting Back: World and Behavior Grounding in Real2Sim2Real Co-Training
Samuel Liu, Youngsun Kim, Martin Matak +1
Simulation can expand scarce real demonstrations for co-training, yet how world fidelity and similarity to human behavior affect policy performance remains unclear. We distinguish…
First Plan Then Evaluate: Multi-Target Planning with Post-Planning Success Evaluation Improves Learning-Based Grasping Pipelines
Martin Matak, Mohanraj Devendran Shanthi, Karl Van Wyk +1
Autonomous multi-finger grasping is a fundamental capability in robotic manipulation. Optimization-based approaches show strong performance, but tend to be sensitive to initializat…
23 DoF Grasping Policies from a Raw Point Cloud
Martin Matak, Karl Van Wyk, Tucker Hermans
Coordinating the motion of robots with high degrees of freedom (DoF) to grasp objects gives rise to many challenges. In this paper, we propose a novel imitation learning approach t…
DextrAH-G: Pixels-to-Action Dexterous Arm-Hand Grasping with Geometric Fabrics
Tyler Ga Wei Lum, Martin Matak, Viktor Makoviychuk +5
A pivotal challenge in robotics is achieving fast, safe, and robust dexterous grasping across a diverse range of objects, an important goal within industrial applications. However,…
Planning Visual-Tactile Precision Grasps via Complementary Use of Vision and Touch
Martin Matak, Tucker Hermans
Reliably planning fingertip grasps for multi-fingered hands lies as a key challenge for many tasks including tool use, insertion, and dexterous in-hand manipulation. This task beco…
Comparing Piezoresistive Substrates for Tactile Sensing in Dexterous Hands
Rebecca Miles, Martin Matak, Sarah Hood +3
While tactile skins have been shown to be useful for detecting collisions between a robotic arm and its environment, they have not been extensively used for improving robotic grasp…
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…