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Found 16 papers, 5 papers with code
CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics
For most real-world applications, robots need to adapt and learn continually with limited data in their environments.
Active Class Selection for Few-Shot Class-Incremental Learning
We term this model Few-shot Incremental Active class SeleCtiOn (FIASco).
Using Virtual Reality to Simulate Human-Robot Emergency Evacuation Scenarios
This paper describes our recent effort to use virtual reality to simulate threatening emergency evacuation scenarios in which a robot guides a person to an exit.
Don't Get Yourself into Trouble! Risk-aware Decision-Making for Autonomous Vehicles
Risk is traditionally described as the expected likelihood of an undesirable outcome, such as collisions for autonomous vehicles.
Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection
We propose a safe DRL approach for autonomous vehicle (AV) navigation through crowds of pedestrians while making a left turn at an unsignalized intersection.
Pedestrian Collision Avoidance for Autonomous Vehicles at Unsignalized Intersection Using Deep Q-Network
Prior research has extensively explored Autonomous Vehicle (AV) navigation in the presence of other vehicles, however, navigation among pedestrians, who are the most vulnerable element in urban environments, has been less examined.
F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning
To fill this gap, we present a new dataset termed F-SIOL-310 (Few-Shot Incremental Object Learning) which is specifically captured for testing few-shot incremental object learning capability for robotic vision.
Learning Novel Objects Continually Through Curiosity
Children learn continually by asking questions about the concepts they are most curious about.
Continual Learning of Visual Concepts for Robots through Limited Supervision
For many real-world robotics applications, robots need to continually adapt and learn new concepts.
EEC: Learning to Encode and Regenerate Images for Continual Learning
The two main impediments to continual learning are catastrophic forgetting and memory limitations on the storage of data.
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper
The paper utilizes a recent state-of-the-art approach for incremental learning and adapts it for online learning of scenes (contexts).
Tell me what this is: Few-Shot Incremental Object Learning by a Robot
For many applications, robots will need to be incrementally trained to recognize the specific objects needed for an application.
Storing Encoded Episodes as Concepts for Continual Learning
The two main challenges faced by continual learning approaches are catastrophic forgetting and memory limitations on the storage of data.
The Pedestrian Patterns Dataset
The purpose of the dataset is to capture the patterns of social and pedestrian behavior along the traversed routes at different times and to eventually use this information to make predictions about the risk associated with autonomously traveling along different routes.
Teach Me What You Want to Play: Learning Variants of Connect Four through Human-Robot Interaction
The paper demonstrates a method for teaching a robot the win conditions of the game Connect Four and its variants using a single demonstration and a few trial examples with a question and answer session led by the robot.
Centroid Based Concept Learning for RGB-D Indoor Scene Classification
Inspection of the centroids generated by our approach on RGB-D datasets leads us to propose a method for merging conceptually similar categories, resulting in improved accuracy for all approaches.
