65 citations · 79 across the 7 of their papers we have counts for
9 papers
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…
Structured Latent Variable Models for Articulated Object Interaction
Emily Liu, Michael Noseworthy, Nicholas Roy
In this paper, we investigate a scenario in which a robot learns a low-dimensional representation of a door given a video of the door opening or closing. This representation can be…
Queer In AI: A Case Study in Community-Led Participatory AI
Organizers Of QueerInAI, :, Anaelia Ovalle +48
We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community…
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…
The RLLChatbot: a solution to the ConvAI challenge
Nicolas Gontier, Koustuv Sinha, Peter Henderson +4
Current conversational systems can follow simple commands and answer basic questions, but they have difficulty maintaining coherent and open-ended conversations about specific topi…
Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses
Ryan Lowe, Michael Noseworthy, Iulian V. Serban +3
Automatically evaluating the quality of dialogue responses for unstructured domains is a challenging problem. Unfortunately, existing automatic evaluation metrics are biased and co…
On the Evaluation of Dialogue Systems with Next Utterance Classification
Ryan Lowe, Iulian V. Serban, Mike Noseworthy +2
An open challenge in constructing dialogue systems is developing methods for automatically learning dialogue strategies from large amounts of unlabelled data. Recent work has propo…