A Framework for Recognition and Prediction of Human Motions in Human-Robot Collaboration Using Probabilistic Motion Models

Abstract

This paper presents a framework for recognition and prediction of ongoing human motions. The predictions generated by this framework could be used in a controller for a robotic device, enabling the emergence of intuitive and predictable interactions between humans and a robotic collaborator. The framework includes motion onset detection, phase speed estimation, intent estimation and conditioning. For recognition and prediction of a motion, the framework makes use of a motion model database. This database contains several motion models learned using the probabilistic Principal Component Analysis (PPCA) method. The proposed framework is evaluated with joint angle trajectories of eight subjects performing squatting, stooping and lifting tasks. The motion onset and phase speed estimation modules are first evaluated separately. Next, an evaluation of the full framework provides more insight in the current challenges regarding motion prediction. A brief comparison between PPCA and the Probabilistic Movement Primitives (ProMP) method for learning motion models is made based on the influence of both methodologies on the performance of the framework. Both PPCA and ProMP motion models are able to predict motions over a short time horizon but struggle to predict motions over a longer horizon.

Improving Haptic Response for Contextual Human Robot Interaction

“…This section explores the prediction and intention detection in the literature. Mostly, machine learning techniques, such as neural networks [30][31][32], Bayesian methods [32,33], principal component analysis [34], dynamic movement primitive [35], and hidden Markov models [36], have been used. A probabilistic principal component analysis was used for the recognition and prediction of human motion through motion onset detection by relying on a motion detection database of various motion models and an estimation of the execution speed of a motion [34].…”

“…Mostly, machine learning techniques, such as neural networks [30][31][32], Bayesian methods [32,33], principal component analysis [34], dynamic movement primitive [35], and hidden Markov models [36], have been used. A probabilistic principal component analysis was used for the recognition and prediction of human motion through motion onset detection by relying on a motion detection database of various motion models and an estimation of the execution speed of a motion [34]. Li et al used a Bayesian predictor for the motion trajectory of the human arm in a reaching task by combining early partial trajectory classification and human motion regression in addition to neural networks used to model the non-linearity and uncertainty of human hand motion [32].…”

Intention Recognition with Recurrent Neural Networks for Dynamic Human-Robot Collaboration

“…In order to recognize whether a handover should take place, authors in [14] have employed support vector machines to distinguish between handover and non-handover motions based on the giver's kinematic behaviors. Another framework learns motion models using probabilistic principal component analysis for motion onset detection and intent estimation [15].…”

Real-time multitask multihuman–robot interaction based on context awareness

“…However, these approaches will decrease the efficiency of collaborative assembly, as the robots will frequently move away and stop during the assembly process. To solve the problem of the safe shutdown, researches on human behavior prediction [16,17] have been conducted based on context awareness. That is, the robot perceives the operator to avoid collision in advance by predicting the joint position of the operator in a certain period in the future.…”