Telemanipulation Assistance Based on Motion Intention Recognition

Abstract

In telemanipulation systems, assistance through variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity [3, 5, 6, 7, 8]. Conventionally, such assistance is based on the sensory data of the environment and without knowing user's motion intention. In this paper, user's motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture is applied. If the task is aligning the endeffector with a target, an attractive force field is produced. Similarly, if the task is avoiding obstacles that block the path, a repulsive force field is generated. In order to successfully recognize user's motion intention, a Hidden Markov Model (HMM)-based algorithm is developed to classify human actions, such as following a path, aligning target and avoiding obstacles.

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

“…Examples include goal prediction of a shared control teleoperation system considering eye gaze behaviors [9], short-term goal prediction using Bayesian reasoning [10], and task types estimation given the 2D mouse inputs [11]. Many methods have been proposed for addressing the goal prediction problem by leveraging the machine learning tools, such as Hidden Markov model [12,13] and Bayesian inference [14]. Recently, Rakita et al developed a shared control-based bimanual manipulation system.…”

A Review of Intent Detection, Arbitration, and Communication Aspects of Shared Control for Physical Human–Robot Interaction

“…Of special interest for our study of arbitration are the unique works on virtual fixtures by Yu et al [52] and Li and Okamura [53]. Yu et al [52] augment the role of the robotic slave to include more autonomy so that the robot is responsible not only for identifying the human's overarching intent but also for defining corresponding virtual fixtures to satisfy that intent. Li and Okamura [53] provide a methodology for the robot to discretely switch the virtual fixture on or off, depending on the human's communicated intent.…”

“…By "machine learning," we here refer to techniques such as HMMs [49,52,53,68], Gaussian mixture models [42], and RBFs [58]. These data-driven approaches typically require a supervised training phase, where the human practices communicating intents with known classifications; after the model is trained, it can be applied to accurately change role arbitrations in real time.…”

“…These data-driven approaches typically require a supervised training phase, where the human practices communicating intents with known classifications; after the model is trained, it can be applied to accurately change role arbitrations in real time. Works by Li and Okamura [53], Yu et al [52], and Aarno et al [49] leverage HMMs in order to change role arbitrations for teleoperation and co-manipulation applications. Using the robot's measurements of the human's intent, as well as precomputed transition and emission matrices, these HMMs probabilistically determine which role arbitration "state" the human is currently attempting to occupy.…”

“…Similarly, in Ref. [52], the states include (a) following the trajectory, (b) avoiding obstacles, and (c) aligning the end effector, while in Ref. [49], the states consist of virtual fixtures along different line segments; just like before, once a state is detected, the role arbitration shifts toward the human or robot as appropriate.…”