Search Results for author: Dylan P. Losey

Found 10 papers, 2 papers with code

RILI: Robustly Influencing Latent Intent

no code implementations23 Mar 2022 Sagar Parekh, Soheil Habibian, Dylan P. Losey

When robots interact with human partners, often these partners change their behavior in response to the robot.

Physical Interaction as Communication: Learning Robot Objectives Online from Human Corrections

no code implementations6 Jul 2021 Dylan P. Losey, Andrea Bajcsy, Marcia K. O'Malley, Anca D. Dragan

We recognize that physical human-robot interaction (pHRI) is often intentional -- the human intervenes on purpose because the robot is not doing the task correctly.

Learning Visually Guided Latent Actions for Assistive Teleoperation

1 code implementation2 May 2021 Siddharth Karamcheti, Albert J. Zhai, Dylan P. Losey, Dorsa Sadigh

In this work, we develop assistive robots that condition their latent embeddings on visual inputs.

Learning Latent Representations to Influence Multi-Agent Interaction

no code implementations12 Nov 2020 Annie Xie, Dylan P. Losey, Ryan Tolsma, Chelsea Finn, Dorsa Sadigh

We propose a reinforcement learning-based framework for learning latent representations of an agent's policy, where the ego agent identifies the relationship between its behavior and the other agent's future strategy.

reinforcement-learning

Learning User-Preferred Mappings for Intuitive Robot Control

no code implementations22 Jul 2020 Mengxi Li, Dylan P. Losey, Jeannette Bohg, Dorsa Sadigh

Existing approaches to teleoperation typically assume a one-size-fits-all approach, where the designers pre-define a mapping between human inputs and robot actions, and every user must adapt to this mapping over repeated interactions.

Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences

no code implementations24 Jun 2020 Erdem Biyik, Dylan P. Losey, Malayandi Palan, Nicholas C. Landolfi, Gleb Shevchuk, Dorsa Sadigh

As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers.

When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans

no code implementations13 Jan 2020 Minae Kwon, Erdem Biyik, Aditi Talati, Karan Bhasin, Dylan P. Losey, Dorsa Sadigh

Overall, we extend existing rational human models so that collaborative robots can anticipate and plan around suboptimal human behavior during HRI.

Learning from My Partner's Actions: Roles in Decentralized Robot Teams

no code implementations16 Oct 2019 Dylan P. Losey, Mengxi Li, Jeannette Bohg, Dorsa Sadigh

When teams of robots collaborate to complete a task, communication is often necessary.

Controlling Assistive Robots with Learned Latent Actions

no code implementations20 Sep 2019 Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar, Animesh Garg, Dorsa Sadigh

Our insight is that we can make assistive robots easier for humans to control by leveraging latent actions.

Robotics

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