1 citations · 2 across the 5 of their papers we have counts for
5 papers
Global Planning for Contact-Rich Manipulation via Local Smoothing of Quasi-dynamic Contact Models
Tao Pang, H. J. Terry Suh, Lujie Yang +1
The empirical success of Reinforcement Learning (RL) in the setting of contact-rich manipulation leaves much to be understood from a model-based perspective, where the key difficul…
SEED: Series Elastic End Effectors in 6D for Visuotactile Tool Use
H. J. Terry Suh, Naveen Kuppuswamy, Tao Pang +3
We propose the framework of Series Elastic End Effectors in 6D (SEED), which combines a spatially compliant element with visuotactile sensing to grasp and manipulate tools in the w…
Bundled Gradients through Contact via Randomized Smoothing
H. J. Terry Suh, Tao Pang, Russ Tedrake
The empirical success of derivative-free methods in reinforcement learning for planning through contact seems at odds with the perceived fragility of classical gradient-based optim…
Heterogeneous Vehicles Routing for Water Canal Damage Assessment
Di Deng, Tao Pang, Prasanth Palli +2
In Japan, inspection of irrigation water canals has been mostly conducted manually. However, the huge demand for more regular inspections as infrastructure ages, coupled with the l…
Towards Long-endurance Flight: Design and Implementation of a Variable-pitch Gasoline-engine Quadrotor
T. Pang, K. Peng, F. Lin +1
Majority of today's fixed-pitch, electric-power quadrotors have short flight endurance ( 1 hour) which greatly limits their applications. This paper presents a design methodolog…