2.3k citations · 3.8k across the 166 of their papers we have counts for
16 papers · 1 filter
Incentivizing Routing Choices for Safe and Efficient Transportation in the Face of the COVID-19 Pandemic
Mark Beliaev, Erdem Bıyık, Daniel A. Lazar +3
The COVID-19 pandemic has severely affected many aspects of people's daily lives. While many countries are in a re-opening stage, some effects of the pandemic on people's behaviors…
Learning Latent Representations to Influence Multi-Agent Interaction
Annie Xie, Dylan P. Losey, Ryan Tolsma +2
Seamlessly interacting with humans or robots is hard because these agents are non-stationary. They update their policy in response to the ego agent's behavior, and the ego agent mu…
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference Landscapes
Kejun Li, Maegan Tucker, Erdem Bıyık +6
Characterizing what types of exoskeleton gaits are comfortable for users, and understanding the science of walking more generally, require recovering a user's utility landscape. Le…
Learning Adaptive Language Interfaces through Decomposition
Siddharth Karamcheti, Dorsa Sadigh, Percy Liang
Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings. We introduce a neu…
Multi-Agent Safe Planning with Gaussian Processes
Zheqing Zhu, Erdem Bıyık, Dorsa Sadigh
Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together. In such settings, we need to…
Learning User-Preferred Mappings for Intuitive Robot Control
Mengxi Li, Dylan P. Losey, Jeannette Bohg +1
When humans control drones, cars, and robots, we often have some preconceived notion of how our inputs should make the system behave. Existing approaches to teleoperation typically…
Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving
Zhangjie Cao, Erdem Bıyık, Woodrow Z. Wang +4
Autonomous driving has achieved significant progress in recent years, but autonomous cars are still unable to tackle high-risk situations where a potential accident is likely. In s…
Formalizing and Guaranteeing* Human-Robot Interaction
Hadas Kress-Gazit, Kerstin Eder, Guy Hoffman +12
Robot capabilities are maturing across domains, from self-driving cars, to bipeds and drones. As a result, robots will soon no longer be confined to safety-controlled industrial se…
Active Preference-Based Gaussian Process Regression for Reward Learning
Erdem Bıyık, Nicolas Huynh, Mykel J. Kochenderfer +1
Designing reward functions is a challenging problem in AI and robotics. Humans usually have a difficult time directly specifying all the desirable behaviors that a robot needs to o…
Learning Reward Functions from Diverse Sources of Human Feedback: Optimally Integrating Demonstrations and Preferences
Erdem Bıyık, Dylan P. Losey, Malayandi Palan +3
Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn re…
Shared Autonomy with Learned Latent Actions
Hong Jun Jeon, Dylan P. Losey, Dorsa Sadigh
Assistive robots enable people with disabilities to conduct everyday tasks on their own. However, these tasks can be complex, containing both coarse reaching motions and fine-grain…
Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
Shushman Choudhury, Jayesh K. Gupta, Mykel J. Kochenderfer +2
We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty. Our objective is to minimize the number of…
BLEU Neighbors: A Reference-less Approach to Automatic Evaluation
Kawin Ethayarajh, Dorsa Sadigh
Evaluation is a bottleneck in the development of natural language generation (NLG) models. Automatic metrics such as BLEU rely on references, but for tasks such as open-ended gener…
Exchangeable Input Representations for Reinforcement Learning
John Mern, Dorsa Sadigh, Mykel J. Kochenderfer
Poor sample efficiency is a major limitation of deep reinforcement learning in many domains. This work presents an attention-based method to project neural network inputs into an e…
Fitting a Linear Control Policy to Demonstrations with a Kalman Constraint
Malayandi Palan, Shane Barratt, Alex McCauley +3
We consider the problem of learning a linear control policy for a linear dynamical system, from demonstrations of an expert regulating the system. The standard approach to this pro…
When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans
Minae Kwon, Erdem Biyik, Aditi Talati +3
In order to collaborate safely and efficiently, robots need to anticipate how their human partners will behave. Some of today's robots model humans as if they were also robots, and…