Shen Li

= Ph.D. candidate at MIT, advised by Prof. Julie A. Shah.
Interactive Robotics Group, CSAIL.
Research goal = enable robots to:
    - safely interact with humans
    - efficiently accomplish collaborative tasks.

Previously,
- M.S. in Robotics at CMU,
     Co-advised by Prof. Siddhartha Srinivasa + Prof. Stephanie Rosenthal.
     ∈ Personal Robotics Lab (now at UW), Robotics Institute.
- Internship at CMU, advised by Prof. Katia Sycara.
- B.S. in Computer Science + Psychology at Penn State.

Email  /  CV  /  Google Scholar  /  Twitter  /  Github

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News

Publications
Robust Control and Estimation for Safe Human-Robot Collaboration
Set-based State Estimation with Probabilistic Consistency Guarantee under Epistemic Uncertainty
Shen Li*, Theodoros Stouraitis*,
Michael Gienger, Sethu Vijayakumar, Julie A. Shah
RAL 2022
PDF/ code (in preparation) / video / MIT news / MIT Instagram

Set-based state estimation (1) with a probabilistic consistency guarantee, when dynamic and observation models are learned via GPs; (2) that can be formally reduced to its probabilistic counterpart - GP-EKF; robot-assisted dressing.

Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics
Shen Li, Nadia Figueroa, Ankit Shah, Julie A. Shah
RSS 2021
Project page: PDF / code / videos / talk at RSS (5:04) / slides / poster
/ MIT CSAIL news / MIT Instagram / talk at MIT Horizon (30min, less technical)
/ featured in the Next Byte Podcast with the article

Human-aware motion planning with (1) a probabilistic guarantee on human physical safety, and (2) an improved task efficiency thanks to a two-pronged definition of safety: collision avoidance OR safe impact; robot-assisted dressing.

Safe and Efficient High Dimensional Motion Planning in Space-Time with Time Parameterized Prediction
Shen Li, Julie A. Shah
ICRA 2019
PDF / poster

Robot motion-planning for safe (via collision avoidance) and efficient (via planning in both space-time) human-robot collaboration.

Decision-Making for Human-Robot (Mutual) Adaptation
Reactive Task and Motion Planning under Temporal Logic Specifications
Shen Li*, Daehyung Park*, Yoonchang Sung*, Julie A. Shah, Nicholas Roy
ICRA 2021
arXiv / video (2:28) / slides

Task-and-motion planning under robust against a human operator's cooperative or adversarial interventions.

Decision-Making for Bidirectional Communication in Sequential Human-Robot Collaborative Tasks
Vaibhav Unhelkar*, Shen Li*, Julie A. Shah
HRI 2020 (23.6%)
PDF / video (2:16) / talk video (9:27) / ZDNet news

Robot decision-making on if, when, and what to communicate during collaboration.

Semi-Supervised Learning of Decision-Making Models for Human-Robot Collaboration
Vaibhav Unhelkar*, Shen Li*, Julie A. Shah
CoRL 2019, Oral Presentation (5%)
PDF / video (4:16)

Semi-supervised human model learning => low efforts on parameter specification & high performance on robot collaborative decision-making.

Fast Online Segmentation of Activities from Partial Trajectories
Tariq Iqbal, Shen Li, Christopher Fourie, Bradley Hayes, Julie A. Shah
ICRA 2019
PDF / video (2:49) / poster / PBS NewsHour (from 2:58)

Fast online human activity recognition + robot assistance at the appropriate time.

Trust of Humans in Supervisory Control of Swarm Robots with Varied Levels of Autonomy
Changjoo Nam, Huao Li, Shen Li, Michael Lewis, Katia Sycara,
SMC 2018
PDF / project page

Trust and performance in supervisory control of swarm robots with varied levels of autonomy.

Learning Task Specifications from Human Demonstrations
Planning With Uncertain Specifications (PUnS)
Ankit Shah, Shen Li, Julie A. Shah
RA-L & ICRA 2020
PDF / video (2:11) / MIT news

Robot decision-making under uncertain & non-Markovian task specifications.

Supervised Bayesian Specification Inference from Demonstrations
Ankit Shah, Pritish Kamath, Shen Li,
Patrick Craven, Kevin Landers, Kevin Oden, Julie A. Shah
IJRR 2021 (accepted)

Robot learning non-Markovian task specifications from human demonstrations.

Bayesian Inference of Temporal Task Specifications from Demonstrations
Ankit Shah, Pritish Kamath, Shen Li, Julie A. Shah
NeurIPS 2018
PDF / project page / video (3:05) / poster

Robot learning non-Markovian task specifications from human demonstrations.

Interpretable Robot Behavior
Natural Language Instructions for Human-Robot Collaborative Manipulation
Rosario Scalise*, Shen Li*, Henny Admoni, Stephanie Rosenthal, Siddhartha Srinivasa
IJRR 2018
PDF / project page

A dataset of natural language instructions for object specification in manipulation scenarios (1582 instructions from online crowdsourcing).

Evaluating Critical Points in Trajectories
Shen Li*, Rosario Scalise*, Henny Admoni, Stephanie Rosenthal, Siddhartha Srinivasa
RO-MAN 2017
PDF

Effectiveness of critical way-points in conveying info about robot objectives.

Spatial References and Perspective in Natural Language Instructions for Collaborative Manipulation
Shen Li*, Rosario Scalise*, Henny Admoni, Stephanie Rosenthal, Siddhartha Srinivasa
RO-MAN 2016
PDF / slides / CMU SEI blog

Correlations between clarity of spatial reference instructions and 1) perspective taking 2) spatial features.

Perspective in Natural Language Instructions for Collaborative Manipulation
Shen Li*, Rosario Scalise*, Henny Admoni, Stephanie Rosenthal, Siddhartha Srinivasa
R:SS Workshop on Model Learning for Human-Robot Communication 2016
PDF / slides / poster

Theses
Automatically Evaluating and Generating Clear Robot Explanations
Shen Li
Master's thesis. Carnegie Mellon University. 2017
PDF / slides

Thesis committee: Prof. Siddhartha Srinivasa (co-chair), Prof. Stephanie Rosenthal (co-chair), Prof. Reid Simmons, Prof. Stefanos Nikolaidis

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