-
Learning from failures in robot-assisted feeding: Using online learning to develop manipulation strategies for bite acquisition
Successful robot-assisted feeding requires bite acquisition of a wide va...
read it
-
Leveraging Post Hoc Context for Faster Learning in Bandit Settings with Applications in Robot-Assisted Feeding
Autonomous robot-assisted feeding requires the ability to acquire a wide...
read it
-
Robot-Assisted Feeding: Generalizing Skewering Strategies across Food Items on a Realistic Plate
A robot-assisted feeding system must successfully acquire many different...
read it
-
Leveraging Multimodal Haptic Sensory Data for Robust Cutting
Cutting is a common form of manipulation when working with divisible obj...
read it
-
Food manipulation: A cadence of haptic signals
Autonomous assistive feeding is challenging because it requires manipula...
read it
-
Playing with Food: Learning Food Item Representations through Interactive Exploration
A key challenge in robotic food manipulation is modeling the material pr...
read it
-
Online Spatial Concept and Lexical Acquisition with Simultaneous Localization and Mapping
In this paper, we propose an online learning algorithm based on a Rao-Bl...
read it
Adaptive Robot-Assisted Feeding: An Online Learning Framework for Acquiring Previously-Unseen Food Items
Successful robot-assisted feeding requires bite acquisition of a wide variety of food items. Different food items may require different manipulation actions for successful bite acquisition. Therefore, a key challenge is to handle previously-unseen food items with very different action distributions. By leveraging contexts from previous bite acquisition attempts, a robot should be able to learn online how to acquire those previously-unseen food items. We construct an online learning framework for this problem setting and use the ϵ-greedy and LinUCB contextual bandit algorithms to minimize cumulative regret within that setting. Finally, we demonstrate empirically on a robot-assisted feeding system that this solution can adapt quickly to a food item with an action success rate distribution that differs greatly from previously-seen food items.
READ FULL TEXT
Comments
There are no comments yet.