Michael Noseworthy

I am a graduate student in the Robust Robotics Group at MIT.

My research focuses on robust planning under uncertainty motivated by long-horizon manipulation tasks such as assembly or rearrangement. I am also a part-time intern at the NVIDIA Seattle Robotics Lab where I work on contact-rich manipulation. Before MIT, I studied dialogue systems at McGill University's Reasoning and Learning Lab.

Email / Scholar / Twitter / Github

Research

	Amortized Inference for Efficient Grasp Model Adaptation Michael Noseworthy, Seiji Shaw, Chad Kessens, Nicholas Roy ICRA 2024 Adaptively grasping objects without unknown dynamics properties (e.g., mass distribution or frictional coefficients). Paper
	Insights towards Sim2Real Contact-Rich Manipulation Michael Noseworthy, Iretiayo Akinola, Yashraj Narang, Fabio Ramos, Lucas Manuelli, Ankur Handa, Dieter Fox NeurIPS 2022: Robot Learning Workshop Training policies to solve contact-rich manipulation tasks with noisy pose estimates. Paper
	Object-Factored Models with Partially Observable State Isaiah Brand, Michael Noseworthy, Sebastian Castro, Nicholas Roy NeurIPS 2021: Bayesian Deep Learning Workshop Efficient adaptation for manipulating objects with non-visual parameters. Paper
	Active Learning of Abstract Plan Feasibility Michael Noseworthy, Caris Moses, Isaiah Brand*, Sebastian Castro, Leslie Kaelbling, Tomás Lozano-Pérez, Nicholas Roy RSS 2021 Efficient online learning of feasility models using ensembles of graph networks. Paper / Talk
	Visual Prediction of Priors for Articulated Object Interaction Caris Moses, Michael Noseworthy, Leslie Kaelbling, Tomás Lozano-Pérez, Nicholas Roy ICRA 2020 Efficient manipulation of articulated objects using visual priors to infer kinematic parameters. Paper / Talk / Code / Website
	Task-Conditioned Variational Autoencoders for Learning Movement Primitives Michael Noseworthy, Rohan Paul, Subhro Roy, Daehyung Park, Nicholas Roy CORL 2019 Learning interpretable movement primitives from demonstration. Paper
	Inferring Task Goals and Constraints using Bayesian Nonparametric Inverse Reinforcement Learning Daehyung Park, Michael Noseworthy, Rohan Paul, Subhro Roy, Nicholas Roy CORL 2019 Learning from demonstration in the presence of complex constraints. Paper
	Leveraging Past References for Robust Language Grounding Subhro Roy, Michael Noseworthy, Rohan Paul, Daehyung Park, Nicholas Roy CoNLL 2019 Natural language grounding in situated and temporally extended contexts. Paper
	Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses Ryan Lowe, Michael Noseworthy, Iulian Vlad Serban, Nicolas Angelard-Gontier, Yoshua Bengio, Joelle Pineau ACL 2017 Automatic metric for dialogue model response evaluation. Paper / Code / Talk
	Predicting Success in Goal-Driven Human-Human Dialogues Michael Noseworthy, Jackie Chi Kit Cheung, Joelle Pineau SIGDIAL 2017 Automatic success prediction for task-driven dialogue systems. Paper
	How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation Chia-Wei Liu, Ryan Lowe, Iulian Vlad Serban, Michael Noseworthy, Laurent Charlin, Joelle Pineau EMNLP 2017 A study of how common automatic metrics for evaluating dialogue responses correlate with human judgement. Paper / Talk

Miscellanea

Inclusion@CoRL Organizer, CoRL 2020
Queer in AI Organizer, RSS 2021
Queer in AI Organizer, CoRL 2021

Website template from Jon Barron.