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Found 9 papers, 2 papers with code
NatSGD: A Dataset with Speech, Gestures, and Demonstrations for Robot Learning in Natural Human-Robot Interaction
NatSGD serves as a foundational resource at the intersection of machine learning and HRI research, and we demonstrate its effectiveness in training robots to understand tasks through multimodal human commands, emphasizing the significance of jointly considering speech and gestures.
"Task Success" is not Enough: Investigating the Use of Video-Language Models as Behavior Critics for Catching Undesirable Agent Behaviors
Large-scale generative models are shown to be useful for sampling meaningful candidate solutions, yet they often overlook task constraints and user preferences.
Learning from Ambiguous Demonstrations with Self-Explanation Guided Reinforcement Learning
Our main contribution is to propose the Self-Explanation for RL from Demonstrations (SERLfD) framework, which can overcome the limitations of traditional RLfD works.
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems
The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities.
Contrastively Learning Visual Attention as Affordance Cues from Demonstrations for Robotic Grasping
In this work, our goal is instead to fill the gap between affordance discovery and affordance-based policy learning by integrating the two objectives in an end-to-end imitation learning framework based on deep neural networks.
Plan-Recognition-Driven Attention Modeling for Visual Recognition
We build an event recognition system, ER-PRN, which takes Pixel Dynamics Network as a subroutine, to recognize events based on observations augmented by plan-recognition-driven attention.
Discovering Underlying Plans Based on Shallow Models
Specifically, we propose two approaches, DUP and RNNPlanner, to discover target plans based on vector representations of actions.
Recognizing Plans by Learning Embeddings from Observed Action Distributions
The first involves resampling the distribution sequences to single action sequences, from which we could learn an action affinity model based on learned action (word) embeddings for plan recognition.
Explicablility as Minimizing Distance from Expected Behavior
In order to have effective human-AI collaboration, it is necessary to address how the AI agent's behavior is being perceived by the humans-in-the-loop.
