Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction
- URL: http://arxiv.org/abs/2402.04768v2
- Date: Mon, 8 Apr 2024 15:43:14 GMT
- Title: Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction
- Authors: Esteve Valls Mascaro, Yashuai Yan, Dongheui Lee,
- Abstract summary: We propose to model social motion forecasting in a shared human-robot representation space.
ECHO operates in the aforementioned shared space to predict the future motions of the agents encountered in social scenarios.
We evaluate our model in multi-person and human-robot motion forecasting tasks and obtain state-of-the-art performance by a large margin.
- Score: 9.806227900768926
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Integrating robots into populated environments is a complex challenge that requires an understanding of human social dynamics. In this work, we propose to model social motion forecasting in a shared human-robot representation space, which facilitates us to synthesize robot motions that interact with humans in social scenarios despite not observing any robot in the motion training. We develop a transformer-based architecture called ECHO, which operates in the aforementioned shared space to predict the future motions of the agents encountered in social scenarios. Contrary to prior works, we reformulate the social motion problem as the refinement of the predicted individual motions based on the surrounding agents, which facilitates the training while allowing for single-motion forecasting when only one human is in the scene. We evaluate our model in multi-person and human-robot motion forecasting tasks and obtain state-of-the-art performance by a large margin while being efficient and performing in real-time. Additionally, our qualitative results showcase the effectiveness of our approach in generating human-robot interaction behaviors that can be controlled via text commands. Webpage: https://evm7.github.io/ECHO/
Related papers
- Real-Time Dynamic Robot-Assisted Hand-Object Interaction via Motion Primitives [45.256762954338704]
We propose an approach to enhancing physical HRI with a focus on dynamic robot-assisted hand-object interaction.
We employ a transformer-based algorithm to perform real-time 3D modeling of human hands from single RGB images.
The robot's action implementation is dynamically fine-tuned using the continuously updated 3D hand models.
arXiv Detail & Related papers (2024-05-29T21:20:16Z) - 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) - Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments.
It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion.
Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
arXiv Detail & Related papers (2023-10-19T17:29:17Z) - 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) - Learning Human-to-Robot Handovers from Point Clouds [63.18127198174958]
We propose the first framework to learn control policies for vision-based human-to-robot handovers.
We show significant performance gains over baselines on a simulation benchmark, sim-to-sim transfer and sim-to-real transfer.
arXiv Detail & Related papers (2023-03-30T17:58:36Z) - Model Predictive Control for Fluid Human-to-Robot Handovers [50.72520769938633]
Planning motions that take human comfort into account is not a part of the human-robot handover process.
We propose to generate smooth motions via an efficient model-predictive control framework.
We conduct human-to-robot handover experiments on a diverse set of objects with several users.
arXiv Detail & Related papers (2022-03-31T23:08:20Z) - 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) - A MultiModal Social Robot Toward Personalized Emotion Interaction [1.2183405753834562]
This study demonstrates a multimodal human-robot interaction (HRI) framework with reinforcement learning to enhance the robotic interaction policy.
The goal is to apply this framework in social scenarios that can let the robots generate a more natural and engaging HRI framework.
arXiv Detail & Related papers (2021-10-08T00:35:44Z) - Show Me What You Can Do: Capability Calibration on Reachable Workspace
for Human-Robot Collaboration [83.4081612443128]
We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth.
We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
arXiv Detail & Related papers (2021-03-06T09:14:30Z) - PHASE: PHysically-grounded Abstract Social Events for Machine Social
Perception [50.551003004553806]
We create a dataset of physically-grounded abstract social events, PHASE, that resemble a wide range of real-life social interactions.
Phase is validated with human experiments demonstrating that humans perceive rich interactions in the social events.
As a baseline model, we introduce a Bayesian inverse planning approach, SIMPLE, which outperforms state-of-the-art feed-forward neural networks.
arXiv Detail & Related papers (2021-03-02T18:44:57Z) - Affect-Driven Modelling of Robot Personality for Collaborative
Human-Robot Interactions [16.40684407420441]
Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour.
We propose a novel framework for personality-driven behaviour generation in social robots.
arXiv Detail & Related papers (2020-10-14T16:34:14Z)
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.