2 citations · 6 across the 27 of their papers we have counts for
29 papers
GraspTwin: Zero-Shot Task-Oriented Grasp Optimization via a Digital Twin
Daniel J. Evans, Yinlong Dai, Simon Stepputtis +1
As robots transition from structured factory settings into homes, they are required to interact with an ever-increasing variety of objects. Many tasks require grasping, and often i…
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…
VOLT: Vision and Language Trajectory Segmentation for Faster-than-Demonstration Policies
Robert Ramirez Sanchez, Daniel J. Evans, Dylan P. Losey +1
Humans often take longer to demonstrate a task than a robot would need to execute it. Rather than learning to replicate the demonstration at the same pace, many industrial and prac…
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-…
From Local Corrections to Generalized Skills: Improving Neuro-Symbolic Policies with MEMO
Benjamin A. Christie, Yinlong Dai, Mohammad Bararjanianbahnamiri +2
Recent works use a neuro-symbolic framework for general manipulation policies. The advantage of this framework is that -- by applying off-the-shelf vision and language models -- th…
Language Movement Primitives: Grounding Language Models in Robot Motion
Yinlong Dai, Benjamin A. Christie, Daniel J. Evans +2
Enabling robots to perform novel manipulation tasks from natural language instructions remains a fundamental challenge in robotics, despite significant progress in generalized prob…
Teleoperator-Aware and Safety-Critical Adaptive Nonlinear MPC for Shared Autonomy in Obstacle Avoidance of Legged Robots
Ruturaj Sambhus, Muneeb Ahmad, Basit Muhammad Imran +3
Ensuring safe and effective collaboration between humans and autonomous legged robots is a fundamental challenge in shared autonomy, particularly for teleoperated systems navigatin…
Prepare Before You Act: Learning From Humans to Rearrange Initial States
Yinlong Dai, Andre Keyser, Dylan P. Losey
Imitation learning (IL) has proven effective across a wide range of manipulation tasks. However, IL policies often struggle when faced with out-of-distribution observations; for in…
Combining Performance and Passivity in Linear Control of Series Elastic Actuators
Shaunak A. Mehta, Dylan P. Losey
When humans physically interact with robots, we need the robots to be both safe and performant. Series elastic actuators (SEAs) fundamentally advance safety by introducing complian…
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 Modular Haptic Display with Reconfigurable Signals for Personalized Information Transfer
Antonio Alvarez Valdivia, Benjamin A. Christie, Dylan P. Losey +1
We present a customizable soft haptic system that integrates modular hardware with an information-theoretic algorithm to personalize feedback for different users and tasks. Our pla…
L2D2: Robot Learning from 2D Drawings
Shaunak A. Mehta, Heramb Nemlekar, Hari Sumant +1
Robots should learn new tasks from humans. But how do humans convey what they want the robot to do? Existing methods largely rely on humans physically guiding the robot arm through…
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.…
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.…
Safe Interactions via Monte Carlo Linear-Quadratic Games
Benjamin A. Christie, Dylan P. Losey
Safety is critical during human-robot interaction. But -- because people are inherently unpredictable -- it is often difficult for robots to plan safe behaviors. Instead of relying…
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…
Kiri-Spoon: A Kirigami Utensil for Robot-Assisted Feeding
Maya Keely, Brandon Franco, Casey Grothoff +4
For millions of adults with mobility limitations, eating meals is a daily challenge. A variety of robotic systems have been developed to address this societal need. Unfortunately,…
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…
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…
PECAN: Personalizing Robot Behaviors through a Learned Canonical Space
Heramb Nemlekar, Robert Ramirez Sanchez, Dylan P. Losey
Robots should personalize how they perform tasks to match the needs of individual human users. Today's robot achieve this personalization by asking for the human's feedback in the…
Combining and Decoupling Rigid and Soft Grippers to Enhance Robotic Manipulation
Maya Keely, Yeunhee Kim, Shaunak A. Mehta +6
For robot arms to perform everyday tasks in unstructured environments, these robots must be able to manipulate a diverse range of objects. Today's robots often grasp objects with e…