For successful peg-in-hole assembly, predefined sub-tasks should be executed sequentially according to the current contact state. Therefore, recognizing contact state transitions is essential in order to determine whether to continue the current task or proceed to the next. In that context, learning-based solutions have shown outstanding results. However, these methods heavily rely on balanced datasets, which are challenging to obtain due to the short duration of certain contact states and rare failure cases. To address this issue, this paper proposes a framework for estimating contact state transitions using anomaly detection through input data reconstruction. The proposed framework operates in a semi-supervised manner, eliminating the need for balanced datasets during training. For input data reconstruction, a convolutional neural network is combined with a variational autoencoder to process various sensor measurements as a multivariate time series. Unlike traditional binary anomaly detection, the proposed anomaly detector scores reconstruction errors and leverages domain knowledge to identify various contact state transitions in the peg-in-hole assembly. The effectiveness of the proposed framework is validated through experiments using a torque-controlled dual manipulator system.

The avatar robot is a robot capable of realistic remote operation. In remote operation, the controllability of the glove is crucial. This glove can manipulate the hand interacting directly with the environment at the remote site. The glove must be able to accurately estimate the hand posture and provide haptic feedback to convey information about the remote environment and enhance operability. Throughout the process, user discomfort should be minimized. To achieve this goal, the research proposes providing force feedback to the fingers using Trigonometric Series Elastic Actuators. Haptic gloves are attached to the Middle Phalanx to facilitate the easy installation of additional add-ons, ensuring users feel securely fixed when attached. Additionally, by proposing an algorithm to estimate the fingertip position without directly attaching it to the fingertip, the haptic glove estimates hand posture and delivers appropriate force as needed. Finally, the system, including the haptic glove, participated in the ANA Avatar XPRIZE competition. The avatar system performed eight missions, which included not only remote manipulation of objects but also social interactions, demonstrating its effectiveness.

Stable contact and safe responses to the collision have been studied to develop interactive robots such as service and collaborative robots. Stable and safe interactions are usually achieved through the inherent compliance of a motion controller with external torque estimation. However, a fixed control gain would sacrifice either compliance or position tracking performance. Additionally, external torque estimation is susceptible to model errors. In this study, a novel variable stiffness control approach is proposed to achieve a high position tracking performance in free motion and compliant behavior in the contact state. For this purpose, a precise estimation of the external torque and control gains that change based on the external torque are required. To estimate the external torque precisely, a collision detecting learning algorithm that uses long short-term memory (LSTM) is adopted. Although this method uses only proprioceptive sensors, its torque estimation capability is comparable to that of methods that use additional sensors. Then, the stiffness of a motion controller is adjusted based on the external torque in the stable region. Moreover, by adopting the Operational Space Formulation considering joint elasticity for a motion controller, high position tracking performance can be achieved with only proprioceptive sensors. The performance of the proposed method was validated through comparative experiments with two degrees of freedom (DoF) manipulator.

In this study, a mechanism that realizes a novel structural form of the harmonic reducer is introduced. Conventional robots often use various mechanical reducers owing to low torque and high-speed characteristics of electric motors. Among them, harmonic reducers are frequently used because of their compact size and backlash-free precision. The plate harmonic reducer which uses the same topological geometry and reducing mechanism as the conventional harmonic reducer is a novel type of strain gear that changes its shape to a plate form for axial deformation. It has unique differences in terms of axial thickness, torsional stiffness, and efficiency due to its morphological characteristics. This study introduces and analyzes the reducing principle of the plate harmonic reducer and describes the methodological solutions for realization. Finally, the theoretical performance improvement and operating feasibility of the plate harmonic reducer are analyzed using finite element method and a 3D-printed prototype model.