Motion Mappings for Continuous Bilateral Teleoperation

Abstract

Mapping operator motions to a robot is a key problem in teleoperation. Due to differences between workspaces, such as object locations, it is particularly challenging to derive smooth motion mappings that fulfill different goals (e.g. picking objects with different poses on the two sides or passing through key points). Indeed, most state-of-the-art methods rely on mode switches, leading to a discontinuous, lowtransparency experience. In this paper, we propose a unified formulation for position, orientation and velocity mappings based on the poses of objects of interest in the operator and robot workspaces. We apply it in the context of bilateral teleoperation. Two possible implementations to achieve the proposed mappings are studied: an iterative approach based on locallyweighted translations and rotations, and a neural network approach. Evaluations are conducted both in simulation and using two torque-controlled Franka Emika Panda robots. Our results show that, despite longer training times, the neural network approach provides faster mapping evaluations and lower interaction forces for the operator, which are crucial for continuous, real-time teleoperation.

Editorial: Artificial Intelligence and Human Movement in Industries and Creation

“…Most of the researchers usually focus on the use of a single modality sensor - due to the simplicity and low cost of the final system - and the design of either conventional machine learning algorithms or complex deep learning network architectures for analyzing human motion data ( Konstantinidis et al, 2018 ; Konstantinidis et al, 2020 ). Such cost-effective approaches have been applied to a wide range of application domains, including entertainment ( Kaza et al, 2016 ; Baker, 2020 ), health ( Dias et al ; Konstantinidis et al, 2021 ), education ( Psaltis et al, 2017 ; Stefanidis et al, 2019 ), sports ( Tisserand et al, 2017 ), robotics ( Jaquier et al, 2020 ; Gao et al, 2021 ), art and cultural heritage ( Dimitropoulos et al, 2018 ), showing the great potential of AI technology.…”

Bilateral teleoperation with object-adaptive mapping

“…3 ). The diffeomorphic approach [ 17 , 18 ], contrarily, can build functions with higher orders of continuity and only distort the neighbouring space of the points. However, this comes at the cost of high training and evaluation times, which are often prohibitive in teleoperation, especially in the bilateral case.…”

“…Finally, it is worth mentioning that our approach shows more promise for dynamic scenarios, where objects are moving in either one of the workspaces. This is because it does not require learning time (see Table 1 ), hence the mapping can be quickly updated as opposed to other alternatives like [ 17 ] and [ 18 ], where recomputation takes a considerable amount of learning time.…”

“…For complex scenarios where the decision-making of commands is very difficult or changed rapidly, we may have to rely on ourselves to send realtime commands. In our previous work [ 17 ], two motion mapping methods were proposed for continuously bilateral teleoperation. However, both methods cannot adapt to moving objects online, as they take long time for re-training.…”

6-DOF Bilateral Teleoperation Hybrid Control System for Power Distribution Live-Line Operation Robot

“…However, when the size of the master hand is much smaller than that of the slave manipulator, the displacement and speed on the end of the master are amplified for the slave, which makes the fine operation quite difficult. There are many mapping methods for heterogeneous teleoperation, such as joint-joint mapping, point-point mapping in the workspace, rate mapping, and hybrid mapping [13][14][15][16][17]. The main problems are the difference between the master and slave motion tracks and the difference between the master and slave workspaces [18].…”