Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction

• URL: http://arxiv.org/abs/2310.03314v1
• Date: Thu, 5 Oct 2023 05:12:14 GMT
• Title: Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction
• Authors: Aadi Kothari, Tony Tohme, Xiaotong Zhang, and Kamal Youcef-Toumi
• Abstract summary: We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints. It is tested on a human arm kinematic model and implemented on a human-robot collaborative setup with a UR5 robot arm.
• Score: 5.501477817904299
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Human motion prediction is an essential step for efficient and safe human-robot collaboration. Current methods either purely rely on representing the human joints in some form of neural network-based architecture or use regression models offline to fit hyper-parameters in the hope of capturing a model encompassing human motion. While these methods provide good initial results, they are missing out on leveraging well-studied human body kinematic models as well as body and scene constraints which can help boost the efficacy of these prediction frameworks while also explicitly avoiding implausible human joint configurations. We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints in a Gaussian Process Regression (GPR) model to predict human motion over a set time horizon. This formulation is combined with an online context-aware constraints model to leverage task-dependent motions. It is tested on a human arm kinematic model and implemented on a human-robot collaborative setup with a UR5 robot arm to demonstrate the real-time capability of our approach. Simulations were also performed on datasets like HA4M and ANDY. The simulation and experimental results demonstrate considerable improvements in a Gaussian Process framework when these constraints are explicitly considered.

Related papers

• Human motion trajectory prediction using the Social Force Model for real-time and low computational cost applications [3.5970055082749655]
  We propose a novel trajectory prediction model, Social Force Generative Adversarial Network (SoFGAN) SoFGAN uses a Generative Adversarial Network (GAN) and Social Force Model (SFM) to generate different plausible people trajectories reducing collisions in a scene. We show that our method is more accurate in making predictions in UCY or BIWI datasets than most of the current state-of-the-art models and also reduces collisions in comparison to other approaches.
  arXiv  Detail & Related papers  (2023-11-17T15:32:21Z)
• 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)
• Open-VICO: An Open-Source Gazebo Toolkit for Multi-Camera-based Skeleton Tracking in Human-Robot Collaboration [0.0]
  This work presents Open-VICO, an open-source toolkit to integrate virtual human models in Gazebo. In particular, Open-VICO allows to combine in the same simulation environment realistic human kinematic models, multi-camera vision setups, and human-tracking techniques.
  arXiv  Detail & Related papers  (2022-03-28T13:21:32Z)
• Learning Human Motion Prediction via Stochastic Differential Equations [19.30774202476477]
  We propose a novel approach in modeling the motion prediction problem based on differential equations and path integrals. It achieves a 12.48% accuracy improvement over current state-of-the-art methods in average.
  arXiv  Detail & Related papers  (2021-12-21T11:55:13Z)
• LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies [78.17425779503047]
  We propose a novel neural implicit representation for the human body. It is fully differentiable and optimizable with disentangled shape and pose latent spaces. Our model can be trained and fine-tuned directly on non-watertight raw data with well-designed losses.
  arXiv  Detail & Related papers  (2021-11-30T04:10:57Z)
• Nonprehensile Riemannian Motion Predictive Control [57.295751294224765]
  We introduce a novel Real-to-Sim reward analysis technique to reliably imagine and predict the outcome of taking possible actions for a real robotic platform. We produce a closed-loop controller to reactively push objects in a continuous action space. We observe that RMPC is robust in cluttered as well as occluded environments and outperforms the baselines.
  arXiv  Detail & Related papers  (2021-11-15T18:50:04Z)
• Probabilistic Human Motion Prediction via A Bayesian Neural Network [71.16277790708529]
  We propose a probabilistic model for human motion prediction in this paper. Our model could generate several future motions when given an observed motion sequence. We extensively validate our approach on a large scale benchmark dataset Human3.6m.
  arXiv  Detail & Related papers  (2021-07-14T09:05:33Z)
• Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
  The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator. A cost-sensitive active learning approach is used to select optimal joint configurations. The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
  arXiv  Detail & Related papers  (2021-01-26T16:01:02Z)
• Leveraging Neural Network Gradients within Trajectory Optimization for Proactive Human-Robot Interactions [32.57882479132015]
  We present a framework that fuses together the interpretability and flexibility of trajectory optimization (TO) with the predictive power of state-of-the-art human trajectory prediction models. We demonstrate the efficacy of our approach in a multi-agent scenario whereby a robot is required to safely and efficiently navigate through a crowd of up to ten pedestrians.
  arXiv  Detail & Related papers  (2020-12-02T08:43:36Z)
• Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach [34.70843462687529]
  We provide a self-contained tutorial on a conditional variational autoencoder approach to human behavior prediction. The goals of this tutorial paper are to review and build a taxonomy of state-of-the-art methods in human behavior prediction.
  arXiv  Detail & Related papers  (2020-08-10T03:18:27Z)

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.