12 citations · 16 across the 13 of their papers we have counts for
13 papers · 1 filter
A Pivot-Based Kirigami Utensil for Hand-Held and Robot-Assisted Feeding
Keone Leao, Grace Brotherson, Iain Mischel +2
Eating is a daily challenge for over 60 million adults with essential tremors and other mobility limitations. For these users, traditional utensils like forks or spoons are difficu…
TransMASK: Masked State Representation through Learned Transformation
Sagar Parekh, Preston Culbertson, Dylan P. Losey
When humans learn new manipulation skills, they are able to generalize these skills to new contexts and environments. In particular, when learning, humans can easily separate task-…
Fine-Tuning Robot Policies While Maintaining User Privacy
Benjamin A. Christie, Sagar Parekh, Dylan P. Losey
Recent works introduce general-purpose robot policies. These policies provide a strong prior over how robots should behave -- e.g., how a robot arm should manipulate food items. Bu…
Safe and Transparent Robots for Human-in-the-Loop Meat Processing
Sagar Parekh, Casey Grothoff, Ryan Wright +2
Labor shortages have severely affected the meat processing sector. Automated technology has the potential to support the meat industry, assist workers, and enhance job quality. How…
Towards Balanced Behavior Cloning from Imbalanced Datasets
Sagar Parekh, Heramb Nemlekar, Dylan P. Losey
Robots should be able to learn complex behaviors from human demonstrations. In practice, these human-provided datasets are inevitably imbalanced: i.e., the human demonstrates some…
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…
Using High-Level Patterns to Estimate How Humans Predict a Robot will Behave
Sagar Parekh, Lauren Bramblett, Nicola Bezzo +1
Humans interacting with robots often form predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might p…
VIEW: Visual Imitation Learning with Waypoints
Ananth Jonnavittula, Sagar Parekh, Dylan P. Losey
Robots can use Visual Imitation Learning (VIL) to learn manipulation tasks from video demonstrations. However, translating visual observations into actionable robot policies is cha…
Safely and Autonomously Cutting Meat with a Collaborative Robot Arm
Ryan Wright, Sagar Parekh, Robin White +1
Labor shortages in the United States are impacting a number of industries including the meat processing sector. Collaborative technologies that work alongside humans while increasi…
Learning Latent Representations to Co-Adapt to Humans
Sagar Parekh, Dylan P. Losey
When robots interact with humans in homes, roads, or factories the human's behavior often changes in response to the robot. Non-stationary humans are challenging for robot learners…
RILI: Robustly Influencing Latent Intent
Sagar Parekh, Soheil Habibian, Dylan P. Losey
When robots interact with human partners, often these partners change their behavior in response to the robot. On the one hand this is challenging because the robot must learn to c…
Learning Latent Actions without Human Demonstrations
Shaunak A. Mehta, Sagar Parekh, Dylan P. Losey
We can make it easier for disabled users to control assistive robots by mapping the user's low-dimensional joystick inputs to high-dimensional, complex actions. Prior works learn t…
On the Human Control of a Multiple Quadcopters with a Cable-suspended Payload System
Pratik Prajapati, Sagar Parekh, Vineet Vashista
A quadcopter is an under-actuated system with only four control inputs for six degrees of freedom, and yet the human control of a quadcopter is simple enough to be learned with som…