13 citations · 14 across the 3 of their papers we have counts for
4 papers · 1 filter
Semi-Supervised Neural Processes for Articulated Object Interactions
Emily Liu, Michael Noseworthy, Nicholas Roy
The scarcity of labeled action data poses a considerable challenge for developing machine learning algorithms for robotic object manipulation. It is expensive and often infeasible…
FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
Michael Noseworthy, Bingjie Tang, Bowen Wen +7
We present FORGE, a method for sim-to-real transfer of force-aware manipulation policies in the presence of significant pose uncertainty. During simulation-based policy learning, F…
Active Learning of Abstract Plan Feasibility
Michael Noseworthy, Caris Moses, Isaiah Brand +4
Long horizon sequential manipulation tasks are effectively addressed hierarchically: at a high level of abstraction the planner searches over abstract action sequences, and when a…
Visual Prediction of Priors for Articulated Object Interaction
Caris Moses, Michael Noseworthy, Leslie Pack Kaelbling +2
Exploration in novel settings can be challenging without prior experience in similar domains. However, humans are able to build on prior experience quickly and efficiently. Childre…