mlsanityData to 7 Oct 2026

Teaching Drones on the Fly: Can Emotional Feedback Serve as Learning Signal for Training Artificial Agents?

arXiv 2202.09634 ↗First posted 19 Feb 2022cs.HCcited by 0

On the map: LLM alignment / RLHF / preference optimisation → Reward modeling and RLHF

Abstract (arXiv)

We investigate whether naturalistic emotional human feedback can be directly exploited as a reward signal for training artificial agents via interactive human-in-the-loop reinforcement learning. To answer this question, we devise an experimental setting inspired by animal training, in which human test subjects interactively teach an emulated drone agent their desired command-action-mapping by providing emotional feedback on the drone's action selections. We present a first empirical proof-of-concept study and analysis confirming that human facial emotion expression can be directly exploited as reward signal in such interactive learning settings. Thereby, we contribute empirical findings towards more naturalistic and intuitive forms of reinforcement learning especially designed for non-expert users.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reward modeling and RLHF, one of 10 directions in LLM alignment / RLHF / preference optimisation. Builds on 8 of its 28 references that are in the map. First posted on arXiv 19 Feb 2022.

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8 of 28 references.

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Same direction, newest first

  1. 2020Learning Rewards from Linguistic Feedback30 Sep

Other references · 7

  1. 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 895
  2. 2013Challenges in Representation Learning: A report on three machine learning contestscited by 301
  3. 2017Interactive Learning from Policy-Dependent Human Feedbackcited by 155
  4. 2019SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wildcited by 62
  5. 2017Real-time Convolutional Neural Networks for Emotion and Gender Classificationcited by 24
  6. 2017AI Challenges in Human-Robot Cognitive Teamingcited by 9
  7. 2020Perception for Autonomous Systems (PAZ)cited by 5

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  1. 2022Improving Multimodal Interactive Agents with Reinforcement Learning from Human Feedback
  2. 2025Emotion-Inspired Learning Signals (EILS): A Homeostatic Framework for Adaptive Autonomous Agents
  3. 2020The EMPATHIC Framework for Task Learning from Implicit Human Feedback
  4. 2024Mapping out the Space of Human Feedback for Reinforcement Learning: A Conceptual Framework
  5. 2025Towards Reinforcement Learning from Neural Feedback: Mapping fNIRS Signals to Agent Performance
  6. 2020Imitating Interactive Intelligence
  7. 2025Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality ↗
  8. 2018Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time