Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation

• URL: http://arxiv.org/abs/2608.06221v1
• Date: Thu, 06 Aug 2026 16:12:18 GMT
• Title: Robot Learning from Human Demonstrations: Handwritten Alphabet Trajectories and Human-Likeness Evaluation
• Abstract summary: This paper presents a framework for learning human-like robot motion from demonstration.<n>A dataset of 3,142 handwriting demonstrations was collected from 22 participants.<n>A user study with 21 participants evaluated the perceived human-likeness of the generated trajectories.
• Score: 0.26763498831034044
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill to a robot. The resulting human-like robot motion is recognised as a key factor in building trust and enabling natural collaboration in human-robot interaction. This paper presents a framework for learning human-like robot motion from demonstration, including data collection, probabilistic trajectory learning, and perceptual user evaluation. A dataset of 3,142 handwriting demonstrations was collected from 22 participants across all 52 Latin alphabet character-case combinations via a touchscreen teleoperation interface, capturing planar position, contact force, and timing. Building on the widely used Gaussian Mixture Model and Gaussian Mixture Regression approach for learning from demonstration, the framework is extended in this work by incorporating force and normalised time dimensions to enable richer representation of human dynamics, and adapting it to handle non-continuous, multi-segment trajectories, enabling generalisation across demonstrations. A user study with 21 participants evaluated the perceived human-likeness of the generated trajectories using a continuous scale anchored between robotic and human-like motion, normalised to 0-100 where 50 represents the neutral midpoint. The generated trajectories achieved an overall human-likeness score of 71.50 (SD=22.56), indicating that the majority of trajectories were perceived as more human-like. Participants identified geometric positioning and trajectory sequence as the most influential perceptual factors, and reported positive attitudes toward human-like robot behaviour. The datasets are released as open-source, providing a reproducible benchmark for developing and evaluating human-like robot motion methods.

Related papers

• Towards Motion Turing Test: Evaluating Human-Likeness in Humanoid Robots [54.54929302887405]
  We present the Human-Humanoid Motion dataset, which consists of 1,000 motion sequences spanning 15 action categories.<n>All motion sequences are converted into SMPL-X representations to eliminate the influence of visual appearance.<n>Analysis of the collected data reveals that humanoid motions still exhibit noticeable deviations from human movements.
  arXiv  Detail & Related papers  (2026-03-06T11:40:56Z)
• Recognizing Actions from Robotic View for Natural Human-Robot Interaction [52.00935005918032]
  Natural Human-Robot Interaction (N-HRI) requires robots to recognize human actions at varying distances and states, regardless of whether the robot itself is in motion or stationary.<n>Existing benchmarks for N-HRI fail to address the unique complexities in N-HRI due to limited data, modalities, task categories, and diversity of subjects and environments.<n>We introduce (Action from Robotic View) a large-scale dataset for perception-centric robotic views prevalent in mobile service robots.
  arXiv  Detail & Related papers  (2025-07-30T09:48:34Z)
• Toward Aligning Human and Robot Actions via Multi-Modal Demonstration Learning [3.9738951919572827]
  We propose a framework that explicitly models human demonstrations from RGB video with robot demonstrations in voxelized RGB-D space.<n>Our approach combines ResNet-based visual encoding for human intention modeling and a Perceiver Transformer for voxel-based robot action prediction.
  arXiv  Detail & Related papers  (2025-04-14T21:14:51Z)
• AnyDexGrasp: General Dexterous Grasping for Different Hands with Human-level Learning Efficiency [49.868970174484204]
  We introduce an efficient approach for learning dexterous grasping with minimal data.<n>Our method achieves high performance with human-level learning efficiency: only hundreds of grasp attempts on 40 training objects.<n>This method demonstrates promising applications for humanoid robots, prosthetics, and other domains requiring robust, versatile robotic manipulation.
  arXiv  Detail & Related papers  (2025-02-23T03:26:06Z)
• 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.<n>The model learns behavior primitives from human demonstrations through adversarial imitation, and complex robot structures are decomposed into functional components.<n>Our framework is validated on five humanoid robots with diverse configurations.
  arXiv  Detail & Related papers  (2024-12-19T18:41:45Z)
• Learning Multimodal Latent Dynamics for Human-Robot Interaction [18.68936554172693]
  This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI)<n>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.<n>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)
• 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)
• HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration [57.045140028275036]
  We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning. We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
  arXiv  Detail & Related papers  (2022-12-08T15:56:13Z)
• 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)
• Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts [11.879852629248981]
  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.
  arXiv  Detail & Related papers  (2020-05-26T03:44:09Z)

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.