6 papers
POrTAL: Plan-Orchestrated Tree Assembly for Lookahead
Evan Conway, David Porfirio, David Chan +2
When tasking robots in partially observable environments, these robots must efficiently and robustly plan to achieve task goals under uncertainty. Although many probabilistic plann…
Bootstrapping Human-Like Planning via LLMs
David Porfirio, Vincent Hsiao, Morgan Fine-Morris +2
Robot end users increasingly require accessible means of specifying tasks for robots to perform. Two common end-user programming paradigms include drag-and-drop interfaces and natu…
Uncertainty Expression for Human-Robot Task Communication
David Porfirio, Mark Roberts, Laura M. Hiatt
An underlying assumption of many existing approaches to human-robot task communication is that the robot possesses a sufficient amount of environmental domain knowledge, including…
Automating Curriculum Learning for Reinforcement Learning using a Skill-Based Bayesian Network
Vincent Hsiao, Mark Roberts, Laura M. Hiatt +2
A major challenge for reinforcement learning is automatically generating curricula to reduce training time or improve performance in some target task. We introduce SEBNs (Skill-Env…
Towards Online Safety Corrections for Robotic Manipulation Policies
Ariana Spalter, Mark Roberts, Laura M. Hiatt
Recent successes in applying reinforcement learning (RL) for robotics has shown it is a viable approach for constructing robotic controllers. However, RL controllers can produce ma…
Composing Option Sequences by Adaptation: Initial Results
Charles A. Meehan, Paul Rademacher, Mark Roberts +1
Robot manipulation in real-world settings often requires adapting the robot's behavior to the current situation, such as by changing the sequences in which policies execute to achi…