Merge pull request #17 from iamlab-cmu/shivam

Add act delegate learn paper

Author: markmlee

_data/pubs.yml

@@ -244,10 +244,32 @@
   links: 
       '[Pdf]': https://www.ri.cmu.edu/app/uploads/2023/03/submission_camera_ready.pdf
   short_id: vats2023
-  site: https://www.ri.cmu.edu/publications/carnegie-mellon-team-tartan-mission-level-robustness-with-rapidly-deployed-autonomous-aerial-vehicles-in-the-mbzirc-2020/
+  site: https://sites.google.com/view/recoverylearning
   title: "Efficient Recovery Learning using Model Predictive Meta-Reasoning"
   venue: "International Conference on Robotics and Automation (ICRA), May 2023"
   video_embed: null
+  
+- abs: "We consider the problem of completing a set of n tasks with a human-robot team using minimum effort. In many domains, teaching a robot to be fully autonomous can be counterproductive if there are finitely many tasks to be done. Rather, the optimal strategy is to weigh the cost of teaching a robot and its benefit -- how many new tasks it allows the robot to solve autonomously. We formulate this as a planning problem where the goal is to decide what tasks the robot should do autonomously (act), what tasks should be delegated to a human (delegate) and what tasks the robot should be taught (learn) so as to complete all the given tasks with minimum effort. This planning problem results in a search tree that grows exponentially with n -- making standard graph search algorithms intractable. We address this by converting the problem into a mixed integer program that can be solved efficiently using off-the-shelf solvers with bounds on solution quality. To predict the benefit of learning, we propose a precondition prediction classifier. Given two tasks, this classifier predicts whether a skill trained on one will transfer to the other. Finally, we evaluate our approach on peg insertion and Lego stacking tasks, both in simulation and real-world, showing substantial savings in human effort." 
+  authors: Shivam Vats, Oliver Kroemer and Maxim Likhachev
+  award: <award>Outstanding Interaction Paper Finalist at ICRA 2022</award>
+  bib: >
+    @inproceedings{vats2022synergistic,
+      title={Synergistic Scheduling of Learning and Allocation of Tasks in Human-robot Teams},
+      author={Vats, Shivam and Kroemer, Oliver and Likhachev, Maxim},
+      booktitle={2022 International Conference on Robotics and Automation (ICRA)},
+      pages={2789--2795},
+      year={2022},
+      organization={IEEE}
+    }
+  img: ../pics/2022_icra_adl.png
+  links: 
+      '[arXiv]': https://arxiv.org/abs/2203.07478
+      '[Video]': https://youtu.be/FyjrkHKF1mM
+  short_id: vats2022
+  site: https://sites.google.com/view/actdelegateorlearn/home
+  title: "Synergistic Scheduling of Learning and Allocation of Tasks in Human-Robot Teams"
+  venue: "International Conference on Robotics and Automation (ICRA), May 2022"
+  video_embed: null
 
 - abs: "Research on Interactive Robot Learning has yielded several modalities for querying a human for training data, including demonstrations, preferences, and corrections. While prior work in this space has focused on optimizing the robot’s queries within each interaction type, there has been little work on optimizing over the selection of the interaction type itself. We present INQUIRE, the first algorithm to implement and optimize over a generalized representation of information gain across multiple interaction types. Our evaluations show that INQUIRE can dynamically optimize its interaction type (and respective optimal query) based on its current learning status and the robot’s state in the world, resulting in more robust performance across tasks in comparison to state-of-the-art baseline methods. Additionally, INQUIRE allows for customizable cost metrics to bias its selection of interaction types, enabling this algorithm to be tailored to a robot’s particular deployment domain and formulate cost-aware, informative queries."
   authors: Tesca Fitzgerald, Pallavi Koppol, Patrick Callaghan, Russell Q. Wong, Reid Simmons, Oliver Kroemer, and Henny Admoni