93 citations · 290 across the 33 of their papers we have counts for
42 papers
In View or Out of View? A Vision-Aware Filter that Infers and Reacts to the Human's Field of View
Mengxue Fu, Ya-Chuan Hsu, Yinlong Dai +2
During human-robot interaction, sight plays an important role. When the human sees a robot, they can observe the robot's actions and better predict the robot's future behaviors. Co…
CASTER: Formalizing Zero-Shot Manipulation from Synthetic Videos as Constrained Optimization
Yinlong Dai, Homanga Bharadhwaj, Dylan P. Losey
Prior research on learning from humans often requires real human demonstrations. But advances in video generation models offer a new paradigm: we can now prompt these generators to…
Assisting for Open-Ended Tasks: Goal-Oriented Shared Autonomy as a Particle Filter
Mengxue Fu, Ethan Xu, Sam Iyer-Singh +3
A common approach for shared autonomy blends human inputs with autonomous assistance based on the human's likely goal. However, most existing approaches assume that a static set of…
Stable-BC: Controlling Covariate Shift with Stable Behavior Cloning
Shaunak A. Mehta, Yusuf Umut Ciftci, Balamurugan Ramachandran +2
Behavior cloning is a common imitation learning paradigm. Under behavior cloning the robot collects expert demonstrations, and then trains a policy to match the actions taken by th…
Waypoint-Based Reinforcement Learning for Robot Manipulation Tasks
Shaunak A. Mehta, Soheil Habibian, Dylan P. Losey
Robot arms should be able to learn new tasks. One framework here is reinforcement learning, where the robot is given a reward function that encodes the task, and the robot autonomo…
A Review of Communicating Robot Learning during Human-Robot Interaction
Soheil Habibian, Antonio Alvarez Valdivia, Laura H. Blumenschein +1
For robots to seamlessly interact with humans, we first need to make sure that humans and robots understand one another. Diverse algorithms have been developed to enable robots to…
StROL: Stabilized and Robust Online Learning from Humans
Shaunak A. Mehta, Forrest Meng, Andrea Bajcsy +1
Robots often need to learn the human's reward function online, during the current interaction. This real-time learning requires fast but approximate learning rules: when the human'…
Reward Learning with Intractable Normalizing Functions
Joshua Hoegerman, Dylan P. Losey
Robots can learn to imitate humans by inferring what the human is optimizing for. One common framework for this is Bayesian reward learning, where the robot treats the human's demo…
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…
LIMIT: Learning Interfaces to Maximize Information Transfer
Benjamin A. Christie, Dylan P. Losey
Robots can use auditory, visual, or haptic interfaces to convey information to human users. The way these interfaces select signals is typically pre-defined by the designer: for in…
Coordinated Multi-Robot Shared Autonomy Based on Scheduling and Demonstrations
Michael Hagenow, Emmanuel Senft, Nitzan Orr +5
Shared autonomy methods, where a human operator and a robot arm work together, have enabled robots to complete a range of complex and highly variable tasks. Existing work primarily…
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…
RISO: Combining Rigid Grippers with Soft Switchable Adhesives
Shaunak A. Mehta, Yeunhee Kim, Joshua Hoegerman +2
Robot arms that assist humans should be able to pick up, move, and release everyday objects. Today's assistive robot arms use rigid grippers to pinch items between fingers; while t…
Towards Robots that Influence Humans over Long-Term Interaction
Shahabedin Sagheb, Ye-Ji Mun, Neema Ahmadian +4
When humans interact with robots influence is inevitable. Consider an autonomous car driving near a human: the speed and steering of the autonomous car will affect how the human dr…
Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interaction
Shaunak A. Mehta, Dylan P. Losey
Humans can leverage physical interaction to teach robot arms. This physical interaction takes multiple forms depending on the task, the user, and what the robot has learned so far.…
Wrapping Haptic Displays Around Robot Arms to Communicate Learning
Antonio Alvarez Valdivia, Soheil Habibian, Carly A. Mendenhall +4
Humans can leverage physical interaction to teach robot arms. As the human kinesthetically guides the robot through demonstrations, the robot learns the desired task. While prior w…
Encouraging Human Interaction with Robot Teams: Legible and Fair Subtask Allocations
Soheil Habibian, Dylan P Losey
Recent works explore collaboration between humans and teams of robots. These approaches make sense if the human is already working with the robot team; but how should robots encour…
SARI: Shared Autonomy across Repeated Interaction
Ananth Jonnavittula, Shaunak A. Mehta, Dylan P. Losey
Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot…
Communicating Robot Conventions through Shared Autonomy
Ananth Jonnavittula, Dylan P. Losey
When humans control robot arms these robots often need to infer the human's desired task. Prior research on assistive teleoperation and shared autonomy explores how robots can dete…
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…
Wrapped Haptic Display for Communicating Physical Robot Learning
Antonio Alvarez Valdivia, Ritish Shailly, Naman Seth +3
Physical interaction between humans and robots can help robots learn to perform complex tasks. The robot arm gains information by observing how the human kinesthetically guides it…
Communicating Inferred Goals with Passive Augmented Reality and Active Haptic Feedback
James F. Mullen, Josh Mosier, Sounak Chakrabarti +3
Robots learn as they interact with humans. Consider a human teleoperating an assistive robot arm: as the human guides and corrects the arm's motion, the robot gathers information a…
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…
Learning Latent Actions to Control Assistive Robots
Dylan P. Losey, Hong Jun Jeon, Mengxi Li +5
Assistive robot arms enable people with disabilities to conduct everyday tasks on their own. These arms are dexterous and high-dimensional; however, the interfaces people must use…