35 citations · 36 across the 9 of their papers we have counts for
9 papers
Can Causal Models Enhance Robot Navigation? Online Causal Adaptation for Real-Robot Navigation
Zhitao Liang, Alex Mitrevski, Emmanuel Dean +1
Causality in robotics aims to produce more interpretable and flexible robot behaviours by enabling robots to predict the consequences of their actions; however, deploying causal mo…
A Causal Approach to Predicting and Improving Human Perceptions of Social Navigation Robots
Maximilian Diehl, Nathan Tsoi, Gustavo Chavez +2
As mobile robots are increasingly deployed in human environments, enabling them to predict how people perceive them is critical for socially adaptable navigation. Predicting percep…
Enabling Robots to Identify Missing Steps in Robot Tasks for Guided Learning from Demonstration
Maximilian Diehl, Tathagata Chakraborti, Karinne Ramirez-Amaro
Learning from Demonstration (LfD) systems are commonly used to teach robots new tasks by generating a set of skills from user-provided demonstrations. These skills can then be sequ…
Hierarchical Reinforcement Learning Based on Planning Operators
Jing Zhang, Emmanuel Dean, Karinne Ramirez-Amaro
Long-horizon manipulation tasks such as stacking represent a longstanding challenge in the field of robotic manipulation, particularly when using reinforcement learning (RL) method…
A Causal-based Approach to Explain, Predict and Prevent Failures in Robotic Tasks
Maximilian Diehl, Karinne Ramirez-Amaro
Robots working in real environments need to adapt to unexpected changes to avoid failures. This is an open and complex challenge that requires robots to timely predict and identify…
Why did I fail? A Causal-based Method to Find Explanations for Robot Failures
Maximilian Diehl, Karinne Ramirez-Amaro
Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust a…
Optimizing robot planning domains to reduce search time for long-horizon planning
Maximilian Diehl, Chris Paxton, Karinne Ramirez-Amaro
We have recently introduced a system that automatically generates robotic planning operators from human demonstrations. One feature of our system is the operator count, which keeps…
Work in Progress -- Automated Generation of Robotic Planning Domains from Observations
Maximilian Diehl, Karinne Ramirez-Amaro
In this paper, we report the results of our latest work on the automated generation of planning operators from human demonstrations, and we present some of our future research idea…
Automated Generation of Robotic Planning Domains from Observations
Maximilian Diehl, Chris Paxton, Karinne Ramirez-Amaro
Automated planning enables robots to find plans to achieve complex, long-horizon tasks, given a planning domain. This planning domain consists of a list of actions, with their asso…