Intent inference in shared-control teleoperation system in consideration of user behavior

Abstract

In shared-control teleoperation, rather than directly executing a user’s input, a robot system assists the user via part of autonomy to reduce user’s workload and improve efficiency. Effective assistance is challenging task as it requires correctly inferring the user intent, including predicting the user goal from all possible candidates as well as inferring the user preferred movement in the next step. In this paper, we present a probabilistic formulation for inferring the user intent by taking consideration of user behavior. In our approach, the user behavior is learned from demonstrations, which is then incorporated in goal prediction and path planning. Using maximum entropy principle, two goal prediction methods are tailored according to the similarity metrics between user’s short-term movements and the learned user behavior. We have validated the proposed approaches with a user study—examining the performance of our goal prediction methods in approaching tasks in multiple goals scenario. The results show that our approaches perform well in user goal prediction and are able to respond quickly to dynamic changing of the user’s goals. Comparison analysis shows that the proposed approaches outperform the existing methods especially in scenarios with goal ambiguity.

Efficient and Collision-Free Human–Robot Collaboration Based on Intention and Trajectory Prediction

“…In [22], Landi et al combined the minimum jerk model with an adaptive neural network to predict whether the human will react to the robot end-effector. The similarity between observed short-term movements and the learned user behavior was used to predict human reaching goal in a teleoperation task [23].…”

A Rental Platform Service Supply Chain Network Equilibrium Model Considering Digital Detection Technology Investment and Big Data Marketing

“…At this time, a big data marketing strategy must be adopted. Based on these accumulated data, we must portray the demanding market search behaviors [25], correctly infer consumers' intentions and preferred actions [26] while accurately gaining insight into consumers' changes in a timely manner and using big data to help the business make better decisions [9,10], enhance consumers' experience [5,27], use digging and analyzing data to understand consumers' demand preferences more clearly, enhance the conversion for the entire supply chain network management [28] and, finally, transform the big data marketing strategy into economic benefits. In addition, the service supply chain indicates, forecasts, analyzes and guides consumer behaviors based on datadriven analysis [29].…”

A Systematic Comparison of Simulation Software for Robotic Arm Manipulation using ROS2

“…However, with the advent of VR technologies, researchers have investigated whether these technologies are appropriate for direct teleoperation. For example, they have proposed using a VR controller such as [21], [5] or a phone [22]. While others have investigated the use of motion mapping of the user's body [10], [23] or only gaze control [12].…”

“…Similarly, we evaluate our framework on 2 different robots, a Rethink Robotics Baxter and a Universal Robotic 3, and ask 37 non-expert participants to carry out a simple pick-and-place task. Additionally, by using two different control modalities, our goal is to bridge the gap between VR control methods [21], [5] and complete body mapping [10], [11] by investigating the performance of hand and finger tracking through a SenseGlove.…”