121 citations · 654 across the 70 of their papers we have counts for
10 papers · 1 filter
PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection
Shivin Dass, Karl Pertsch, Hejia Zhang +3
Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research. However, collecting l…
A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning
Hejia Zhang, Shao-Hung Chan, Jie Zhong +3
We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple rob…
Training Diverse High-Dimensional Controllers by Scaling Covariance Matrix Adaptation MAP-Annealing
Bryon Tjanaka, Matthew C. Fontaine, David H. Lee +2
Pre-training a diverse set of neural network controllers in simulation has enabled robots to adapt online to damage in robot locomotion tasks. However, finding diverse, high-perfor…
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment
Yilei Zeng, Jiali Duan, Yang Li +4
Human-centered AI considers human experiences with AI performance. While abundant research has been helping AI achieve superhuman performance either by fully automatic or weak supe…
Generating Diverse Indoor Furniture Arrangements
Ya-Chuan Hsu, Matthew C. Fontaine, Sam Earle +3
We present a method for generating arrangements of indoor furniture from human-designed furniture layout data. Our method creates arrangements that target specified diversity, such…
Deep Surrogate Assisted Generation of Environments
Varun Bhatt, Bryon Tjanaka, Matthew C. Fontaine +1
Recent progress in reinforcement learning (RL) has started producing generally capable agents that can solve a distribution of complex environments. These agents are typically test…
Covariance Matrix Adaptation MAP-Annealing
Matthew C. Fontaine, Stefanos Nikolaidis
Single-objective optimization algorithms search for the single highest-quality solution with respect to an objective. Quality diversity (QD) optimization algorithms, such as Covari…
Learning Performance Graphs from Demonstrations via Task-Based Evaluations
Aniruddh G. Puranic, Jyotirmoy V. Deshmukh, Stefanos Nikolaidis
In the learning from demonstration (LfD) paradigm, understanding and evaluating the demonstrated behaviors plays a critical role in extracting control policies for robots. Without…
Approximating Gradients for Differentiable Quality Diversity in Reinforcement Learning
Bryon Tjanaka, Matthew C. Fontaine, Julian Togelius +1
Consider the problem of training robustly capable agents. One approach is to generate a diverse collection of agent polices. Training can then be viewed as a quality diversity (QD)…
Using Design Metaphors to Understand User Expectations of Socially Interactive Robot Embodiments
Nathaniel Dennler, Changxiao Ruan, Jessica Hadiwijoyo +3
The physical design of a robot suggests expectations of that robot's functionality for human users and collaborators. When those expectations align with the true capabilities of th…