Anatomy of Uncertainty: Expressive Descriptors of Robotic Manipulator Motion for Non-verbal Communication in Human-Robot Collaboration

• URL: http://arxiv.org/abs/2607.13696v1
• Date: Wed, 15 Jul 2026 10:48:44 GMT
• Title: Anatomy of Uncertainty: Expressive Descriptors of Robotic Manipulator Motion for Non-verbal Communication in Human-Robot Collaboration
• Abstract summary: This work introduces a mathematical framework for expressing perceptual uncertainty through robotic manipulator motion.<n>Results establish a perceptually grounded basis for encoding robot uncertainty in motion and support future autonomous trajectory generation.
• Score: 0.6576864734526405
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robots operating in human-robot collaboration must communicate not only their intended actions but also uncertainty arising from incomplete or ambiguous perception. This work introduces a mathematical framework for expressing perceptual uncertainty through robotic manipulator motion. Drawing on Laban Movement Analysis, robot behavior is organized in a Commitment-Vigilance state space that maps uncertainty-related states - confidence, curiosity, hesitance, fear, and inactivity - to distinct Laban Effort signatures. Five motion primitives - approach, pause, retreat, exploration, and oscillation - are then parameterized using eleven kinematic and geometric descriptors, including acceleration, pause and retreat characteristics, gaze angles, tilt, and shiver amplitude. A video-based human-subject study evaluated recognition of four expressive trajectories and the influence of individual descriptors on perceived intensity. Participants reliably identified the intended behavioral states, while several descriptors significantly modulated expressiveness. The results establish a perceptually grounded basis for encoding robot uncertainty in motion and support future autonomous trajectory generation using parametric movement representations for collaborative tasks in shared environments. Code, videos, questionnaire and appendices are available at "https://bit.ly/github-aou".

Related papers

• Pose-Anchored Optical Flow for Low-Latency Human Action Anticipation in Human-Robot Teaming [0.4282159812965447]
  PoseOFF is a pose-anchored optical flow representation that captures local motion information around human joints.<n>By conditioning motion feature extraction on human pose, PoseOFF encodes localised motion dynamics at semantically meaningful body locations.<n>Our results show that PoseOFF enables models to achieve comparable or improved performance while observing less of the action sequence.
  arXiv  Detail & Related papers  (2026-08-26T08:07:19Z)
• RGB-D Video Generation for Improving Human-to-Robot Object Handover Prediction [51.76737991200891]
  Human-to-robot (H2R) object handover is a fundamental capability for human-robot collaboration, yet progress is hindered by the scarcity of large-scale, human-centric datasets.<n>We introduce Hand2Bot, an RGB-D video dataset that provides rich contextual information such as body posture and facial expressions.<n>We propose PassGen, a generative pipeline that synthesizes realistic handover sequences while ensuring hand-object consistency.
  arXiv  Detail & Related papers  (2026-08-13T09:55:59Z)
• Do Robots Need Body Language? Comparing Communication Modalities for Legible Motion Intent in Human-Shared Spaces [22.039354879428497]
  High-DoF robots exhibit motion that people read as expressive, intentionally or not, making it important to understand how such cues are perceived.<n>We present an online video study evaluating how different signaling modalities, expressive motion, lights, text, and audio, shape people's ability to understand a quadruped robot's upcoming navigation actions.
  arXiv  Detail & Related papers  (2026-04-03T20:53:05Z)
• Towards Immersive Human-X Interaction: A Real-Time Framework for Physically Plausible Motion Synthesis [51.95817740348585]
  Human-X is a novel framework designed to enable immersive and physically plausible human interactions across diverse entities.<n>Our method jointly predicts actions and reactions in real-time using an auto-regressive reaction diffusion planner.<n>Our framework is validated in real-world applications, including virtual reality interface for human-robot interaction.
  arXiv  Detail & Related papers  (2025-08-04T06:35:48Z)
• 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)
• FABG : End-to-end Imitation Learning for Embodied Affective Human-Robot Interaction [3.8177867835232004]
  This paper proposes FABG (Facial Affective Behavior Generation), an end-to-end imitation learning system for human-robot interaction.<n>We develop an immersive virtual reality (VR) demonstration system that allows operators to perceive stereoscopic environments.<n>We deploy FABG on a real-world 25-degree-of-freedom humanoid robot, validating its effectiveness through four fundamental interaction tasks.
  arXiv  Detail & Related papers  (2025-03-03T09:58:04Z)
• Cyber-Physical Steganography in Robotic Motion Control [9.182884165239996]
  This study seeks to extend the horizons of what constitutes a viable steganographic medium by introducing a steganographic paradigm in robotic motion control.<n>Based on the observation of the robot's inherent sensitivity to changes in its environment, we propose a methodology to encode messages as environmental stimuli influencing the motions of the robotic agent and to decode messages from the resulting motion trajectory.
  arXiv  Detail & Related papers  (2025-01-08T14:44:40Z)
• EMOTION: Expressive Motion Sequence Generation for Humanoid Robots with In-Context Learning [10.266351600604612]
  This paper introduces a framework, called EMOTION, for generating expressive motion sequences in humanoid robots. We conduct online user studies comparing the naturalness and understandability of the motions generated by EMOTION and its human-feedback version, EMOTION++.
  arXiv  Detail & Related papers  (2024-10-30T17:22:45Z)
• ImitationNet: Unsupervised Human-to-Robot Motion Retargeting via Shared Latent Space [9.806227900768926]
  This paper introduces a novel deep-learning approach for human-to-robot motion. Our method does not require paired human-to-robot data, which facilitates its translation to new robots. Our model outperforms existing works regarding human-to-robot similarity in terms of efficiency and precision.
  arXiv  Detail & Related papers  (2023-09-11T08:55:04Z)
• 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)
• From Movement Kinematics to Object Properties: Online Recognition of Human Carefulness [112.28757246103099]
  We show how a robot can infer online, from vision alone, whether or not the human partner is careful when moving an object. We demonstrated that a humanoid robot could perform this inference with high accuracy (up to 81.3%) even with a low-resolution camera. The prompt recognition of movement carefulness from observing the partner's action will allow robots to adapt their actions on the object to show the same degree of care as their human partners.
  arXiv  Detail & Related papers  (2021-09-01T16:03:13Z)
• Careful with That! Observation of Human Movements to Estimate Objects Properties [106.925705883949]
  We focus on the features of human motor actions that communicate insights on the weight of an object. Our final goal is to enable a robot to autonomously infer the degree of care required in object handling.
  arXiv  Detail & Related papers  (2021-03-02T08:14:56Z)

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.