2.3k citations · 3.8k across the 166 of their papers we have counts for
13 papers · 1 filter
Continual adaptation for efficient machine communication
Robert D. Hawkins, Minae Kwon, Dorsa Sadigh +1
To communicate with new partners in new contexts, humans rapidly form new linguistic conventions. Recent neural language models are able to comprehend and produce the existing conv…
Learning from My Partner's Actions: Roles in Decentralized Robot Teams
Dylan P. Losey, Mengxi Li, Jeannette Bohg +1
When teams of robots collaborate to complete a task, communication is often necessary. Like humans, robot teammates should implicitly communicate through their actions: but interpr…
Asking Easy Questions: A User-Friendly Approach to Active Reward Learning
Erdem Bıyık, Malayandi Palan, Nicholas C. Landolfi +2
Robots can learn the right reward function by querying a human expert. Existing approaches attempt to choose questions where the robot is most uncertain about the human's response;…
Robots that Take Advantage of Human Trust
Dylan P. Losey, Dorsa Sadigh
Humans often assume that robots are rational. We believe robots take optimal actions given their objective; hence, when we are uncertain about what the robot's objective is, we int…
Controlling Assistive Robots with Learned Latent Actions
Dylan P. Losey, Krishnan Srinivasan, Ajay Mandlekar +2
Assistive robotic arms enable users with physical disabilities to perform everyday tasks without relying on a caregiver. Unfortunately, the very dexterity that makes these arms use…
Learning How to Dynamically Route Autonomous Vehicles on Shared Roads
Daniel A. Lazar, Erdem Bıyık, Dorsa Sadigh +1
Road congestion induces significant costs across the world, and road network disturbances, such as traffic accidents, can cause highly congested traffic patterns. If a planner had…
Learning Reward Functions by Integrating Human Demonstrations and Preferences
Malayandi Palan, Nicholas C. Landolfi, Gleb Shevchuk +1
Our goal is to accurately and efficiently learn reward functions for autonomous robots. Current approaches to this problem include inverse reinforcement learning (IRL), which uses…
Batch Active Learning Using Determinantal Point Processes
Erdem Bıyık, Kenneth Wang, Nima Anari +1
Data collection and labeling is one of the main challenges in employing machine learning algorithms in a variety of real-world applications with limited data. While active learning…
Deep Local Trajectory Replanning and Control for Robot Navigation
Ashwini Pokle, Roberto Martín-Martín, Patrick Goebel +8
We present a navigation system that combines ideas from hierarchical planning and machine learning. The system uses a traditional global planner to compute optimal paths towards a…
Object Exchangeability in Reinforcement Learning: Extended Abstract
John Mern, Dorsa Sadigh, Mykel Kochenderfer
Although deep reinforcement learning has advanced significantly over the past several years, sample efficiency remains a major challenge. Careful choice of input representations ca…
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Erdem Bıyık, Daniel A. Lazar, Dorsa Sadigh +1
Autonomous vehicles have the potential to increase the capacity of roads via platooning, even when human drivers and autonomous vehicles share roads. However, when users of a road…
Efficient and Safe Exploration in Deterministic Markov Decision Processes with Unknown Transition Models
Erdem Bıyık, Jonathan Margoliash, Shahrouz Ryan Alimo +1
We propose a safe exploration algorithm for deterministic Markov Decision Processes with unknown transition models. Our algorithm guarantees safety by leveraging Lipschitz-continui…
Unsupervised Visuomotor Control through Distributional Planning Networks
Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh +1
While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering o…