6 papers
Belief-Aware Influence and Trust (BAIT): Shaping Human Belief During Repeated Human-Robot Interaction
Ye-Ji Mun, Mahsa Golchoubian, Shahabedin Sagheb +4
Repeated human-robot interaction (HRI) requires proactively accounting for humans who continually adapt to evolving beliefs about the robot. Prior frameworks often treat encounters…
CIVIL: Causal and Intuitive Visual Imitation Learning
Yinlong Dai, Robert Ramirez Sanchez, Ryan Jeronimus +4
Today's robots attempt to learn new tasks by imitating human examples. These robots watch the human complete the task, and then try to match the actions taken by the human expert.…
Counterfactual Behavior Cloning: Offline Imitation Learning from Imperfect Human Demonstrations
Shahabedin Sagheb, Dylan P. Losey
Learning from humans is challenging because people are imperfect teachers. When everyday humans show the robot a new task they want it to perform, humans inevitably make errors (e.…
Should Collaborative Robots be Transparent?
Shahabedin Sagheb, Soham Gandhi, Dylan P. Losey
We often assume that robots which collaborate with humans should behave in ways that are transparent (e.g., legible, explainable). These transparent robots intentionally choose act…
A Unified Framework for Robots that Influence Humans over Long-Term Interaction
Shahabedin Sagheb, Sagar Parekh, Ravi Pandya +4
Robot actions influence the decisions of nearby humans. Here influence refers to intentional change: robots influence humans when they shift the human's behavior in a way that help…
RECON: Reducing Causal Confusion with Human-Placed Markers
Robert Ramirez Sanchez, Heramb Nemlekar, Shahabedin Sagheb +2
Imitation learning enables robots to learn new tasks from human examples. One fundamental limitation while learning from humans is causal confusion. Causal confusion occurs when th…