Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction

• URL: http://arxiv.org/abs/2010.04602v1
• Date: Fri, 9 Oct 2020 14:28:48 GMT
• Title: Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction
• Authors: Tom Weber, Stefan Wermter
• Abstract summary: Explainable artificial intelligence (XAI) can help foster trust in and acceptance of intelligent and autonomous systems. NICO, an open-source humanoid robot platform, is introduced and how the interaction of intrinsic explanations by the robot itself and extrinsic explanations provided by the environment enable efficient robotic behavior.
• Score: 19.844084722919764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Explainable artificial intelligence (XAI) can help foster trust in and acceptance of intelligent and autonomous systems. Moreover, understanding the motivation for an agent's behavior results in better and more successful collaborations between robots and humans. However, not only can humans benefit from a robot's explanation but the robot itself can also benefit from explanations given to him. Currently, most attention is paid to explaining deep neural networks and black-box models. However, a lot of these approaches are not applicable to humanoid robots. Therefore, in this position paper, current problems with adapting XAI methods to explainable neurorobotics are described. Furthermore, NICO, an open-source humanoid robot platform, is introduced and how the interaction of intrinsic explanations by the robot itself and extrinsic explanations provided by the environment enable efficient robotic behavior.

Related papers

• Explaining Explaining [0.882727051273924]
  Explanation is key to people having confidence in high-stakes AI systems. Machine-learning-based systems can't explain because they are usually black boxes. We describe a hybrid approach to developing cognitive agents.
  arXiv  Detail & Related papers  (2024-09-26T16:55:44Z)
• SACSoN: Scalable Autonomous Control for Social Navigation [62.59274275261392]
  We develop methods for training policies for socially unobtrusive navigation. By minimizing this counterfactual perturbation, we can induce robots to behave in ways that do not alter the natural behavior of humans in the shared space. We collect a large dataset where an indoor mobile robot interacts with human bystanders.
  arXiv  Detail & Related papers  (2023-06-02T19:07:52Z)
• Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation [104.5440430194206]
  This work investigates the perception of object manipulations performed with a communicative intent by two robots. We designed the robots' movements to communicate carefulness or not during the transportation of objects.
  arXiv  Detail & Related papers  (2022-08-03T13:26:52Z)
• Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios [1.671353192305391]
  We make use of human-like explanations built from the probability of success to complete the goal that an autonomous robot shows after performing an action. These explanations are intended to be understood by people who have no or very little experience with artificial intelligence methods.
  arXiv  Detail & Related papers  (2022-07-07T10:40:24Z)
• Explain yourself! Effects of Explanations in Human-Robot Interaction [10.389325878657697]
  Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. The effects on human perceptions of robots that explain their decisions have not been studied thoroughly. This study demonstrates the need for and potential of explainable human-robot interaction.
  arXiv  Detail & Related papers  (2022-04-09T15:54:27Z)
• Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks [59.098560311521034]
  We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
  arXiv  Detail & Related papers  (2022-03-29T15:03:05Z)
• Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together [68.44697646919515]
  This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases. The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
  arXiv  Detail & Related papers  (2022-02-03T10:04:26Z)
• Neuroscience-inspired perception-action in robotics: applying active inference for state estimation, control and self-perception [2.1067139116005595]
  We discuss how neuroscience findings open up opportunities to improve current estimation and control algorithms in robotics. This paper summarizes some experiments and lessons learned from developing such a computational model on real embodied platforms.
  arXiv  Detail & Related papers  (2021-05-10T10:59:38Z)
• Joint Mind Modeling for Explanation Generation in Complex Human-Robot Collaborative Tasks [83.37025218216888]
  We propose a novel explainable AI (XAI) framework for achieving human-like communication in human-robot collaborations. The robot builds a hierarchical mind model of the human user and generates explanations of its own mind as a form of communications. Results show that the generated explanations of our approach significantly improves the collaboration performance and user perception of the robot.
  arXiv  Detail & Related papers  (2020-07-24T23:35:03Z)
• Machine Common Sense [77.34726150561087]
  Machine common sense remains a broad, potentially unbounded problem in artificial intelligence (AI) This article deals with the aspects of modeling commonsense reasoning focusing on such domain as interpersonal interactions.
  arXiv  Detail & Related papers  (2020-06-15T13:59:47Z)
• Explainable Goal-Driven Agents and Robots -- A Comprehensive Review [13.94373363822037]
  The paper reviews approaches on explainable goal-driven intelligent agents and robots. It focuses on techniques for explaining and communicating agents perceptual functions and cognitive reasoning. It suggests a roadmap for the possible realization of effective goal-driven explainable agents and robots.
  arXiv  Detail & Related papers  (2020-04-21T01:41:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.