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To Help or Not to Help: LLM-based Attentive Support for Human-Robot Group Interactions
In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain silent to not disturb the group.
CoPAL: Corrective Planning of Robot Actions with Large Language Models
In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge.
Learning Type-Generalized Actions for Symbolic Planning
Symbolic planning is a powerful technique to solve complex tasks that require long sequences of actions and can equip an intelligent agent with complex behavior.
Intention estimation from gaze and motion features for human-robot shared-control object manipulation
Here, an intention estimation framework is presented, which uses natural gaze and motion features to predict the current action and the target object.
Evolutionary Training and Abstraction Yields Algorithmic Generalization of Neural Computers
A key feature of intelligent behaviour is the ability to learn abstract strategies that scale and transfer to unfamiliar problems.
SKID RAW: Skill Discovery from Raw Trajectories
Integrating robots in complex everyday environments requires a multitude of problems to be solved.
Model-Based Quality-Diversity Search for Efficient Robot Learning
One approach that was recently used to autonomously generate a repertoire of diverse skills is a novelty based Quality-Diversity~(QD) algorithm.
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture
A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems.
Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer
A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems.
Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks
By using learning signals which mimic the intrinsic motivation signalcognitive dissonance in addition with a mental replay strategy to intensify experiences, the stochastic recurrent network can learn from few physical interactions and adapts to novel environments in seconds.
