Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts

• URL: http://arxiv.org/abs/2005.12508v1
• Date: Tue, 26 May 2020 03:44:09 GMT
• Title: Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts
• Authors: Joseph Campbell, Katsu Yamane
• Abstract summary: This paper presents a learning-from-demonstration (LfD) framework for teaching human-robot social interactions that involve whole-body haptic contact over the full robot body. The performance of existing LfD frameworks suffers in such interactions due to high dimensionality data sparsity. We show that by leveraging this sparsity, we can reduce the data dimensionality without incurring a significant accuracy penalty, and introduce three strategies for doing so.
• Score: 11.879852629248981
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a learning-from-demonstration (LfD) framework for teaching human-robot social interactions that involve whole-body haptic interaction, i.e. direct human-robot contact over the full robot body. The performance of existing LfD frameworks suffers in such interactions due to the high dimensionality and spatiotemporal sparsity of the demonstration data. We show that by leveraging this sparsity, we can reduce the data dimensionality without incurring a significant accuracy penalty, and introduce three strategies for doing so. By combining these techniques with an LfD framework for learning multimodal human-robot interactions, we can model the spatiotemporal relationship between the tactile and kinesthetic information during whole-body haptic interactions. Using a teleoperated bimanual robot equipped with 61 force sensors, we experimentally demonstrate that a model trained with 121 sample hugs from 4 participants generalizes well to unseen inputs and human partners.

Related papers

• DIRIGENt: End-To-End Robotic Imitation of Human Demonstrations Based on a Diffusion Model [16.26334759935617]
  We introduce DIRIGENt, a novel end-to-end diffusion approach to generate joint values from observing human demonstrations. We create a dataset in which humans imitate a robot and then use this collected data to train a diffusion model that enables a robot to imitate humans.
  arXiv  Detail & Related papers  (2025-01-28T09:05:03Z)
• Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration [9.42179962375058]
  We propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype. The model learns behavior primitives from human demonstrations through adversarial imitation, and complex robot structures are decomposed into functional components. Our framework is validated on five humanoid robots with diverse configurations.
  arXiv  Detail & Related papers  (2024-12-19T18:41:45Z)
• MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations [19.184155232662995]
  We propose a novel approach for learning a shared latent space representation for Human-Robot Interaction (HRI) We train a Variational Autoencoder (VAE) to learn robot motions regularized using an informative latent space prior. We find that our approach of using an informative MDN prior from human observations for a VAE generates more accurate robot motions.
  arXiv  Detail & Related papers  (2024-07-10T13:16:12Z)
• Inter-X: Towards Versatile Human-Human Interaction Analysis [100.254438708001]
  We propose Inter-X, a dataset with accurate body movements and diverse interaction patterns. The dataset includes 11K interaction sequences and more than 8.1M frames. We also equip Inter-X with versatile annotations of more than 34K fine-grained human part-level textual descriptions.
  arXiv  Detail & Related papers  (2023-12-26T13:36:05Z)
• Learning Multimodal Latent Dynamics for Human-Robot Interaction [19.803547418450236]
  This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI) We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents. We find that Users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines.
  arXiv  Detail & Related papers  (2023-11-27T23:56:59Z)
• InterControl: Zero-shot Human Interaction Generation by Controlling Every Joint [67.6297384588837]
  We introduce a novel controllable motion generation method, InterControl, to encourage the synthesized motions maintaining the desired distance between joint pairs. We demonstrate that the distance between joint pairs for human-wise interactions can be generated using an off-the-shelf Large Language Model.
  arXiv  Detail & Related papers  (2023-11-27T14:32:33Z)
• Real-time Addressee Estimation: Deployment of a Deep-Learning Model on the iCub Robot [52.277579221741746]
  Addressee Estimation is a skill essential for social robots to interact smoothly with humans. Inspired by human perceptual skills, a deep-learning model for Addressee Estimation is designed, trained, and deployed on an iCub robot. The study presents the procedure of such implementation and the performance of the model deployed in real-time human-robot interaction.
  arXiv  Detail & Related papers  (2023-11-09T13:01:21Z)
• MILD: Multimodal Interactive Latent Dynamics for Learning Human-Robot Interaction [34.978017200500005]
  We propose Multimodal Interactive Latent Dynamics (MILD) to address the problem of two-party physical Human-Robot Interactions (HRIs) We learn the interaction dynamics from demonstrations, using Hidden Semi-Markov Models (HSMMs) to model the joint distribution of the interacting agents in the latent space of a Variational Autoencoder (VAE) MILD generates more accurate trajectories for the controlled agent (robot) when conditioned on the observed agent's (human) trajectory.
  arXiv  Detail & Related papers  (2022-10-22T11:25:11Z)
• Data-driven emotional body language generation for social robotics [58.88028813371423]
  In social robotics, endowing humanoid robots with the ability to generate bodily expressions of affect can improve human-robot interaction and collaboration. We implement a deep learning data-driven framework that learns from a few hand-designed robotic bodily expressions. The evaluation study found that the anthropomorphism and animacy of the generated expressions are not perceived differently from the hand-designed ones.
  arXiv  Detail & Related papers  (2022-05-02T09:21:39Z)
• Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition [54.749127627191655]
  The ability to recognize human partners is an important social skill to build personalized and long-term human-robot interactions. Deep learning networks have achieved state-of-the-art results and demonstrated to be suitable tools to address such a task. One solution is to make robots learn from their first-hand sensory data with self-supervision.
  arXiv  Detail & Related papers  (2021-03-16T13:50:24Z)
• Learning Predictive Models From Observation and Interaction [137.77887825854768]
  Learning predictive models from interaction with the world allows an agent, such as a robot, to learn about how the world works. However, learning a model that captures the dynamics of complex skills represents a major challenge. We propose a method to augment the training set with observational data of other agents, such as humans.
  arXiv  Detail & Related papers  (2019-12-30T01:10:41Z)

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.