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30 results on '"Liarokapis, Minas"'

1. Benchmarking Reinforcement Learning Methods for Dexterous Robotic Manipulation with a Three-Fingered Gripper

Author

Cutler, Elizabeth, Xing, Yuning, Cui, Tony, Zhou, Brendan, van Rijnsoever, Koen, Hart, Ben, Valencia, David, Ong, Lee Violet C., Gee, Trevor, Liarokapis, Minas, and Williams, Henry

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Reinforcement Learning (RL) training is predominantly conducted in cost-effective and controlled simulation environments. However, the transfer of these trained models to real-world tasks often presents unavoidable challenges. This research explores the direct training of RL algorithms in controlled yet realistic real-world settings for the execution of dexterous manipulation. The benchmarking results of three RL algorithms trained on intricate in-hand manipulation tasks within practical real-world contexts are presented. Our study not only demonstrates the practicality of RL training in authentic real-world scenarios, facilitating direct real-world applications, but also provides insights into the associated challenges and considerations. Additionally, our experiences with the employed experimental methods are shared, with the aim of empowering and engaging fellow researchers and practitioners in this dynamic field of robotics.

Published
2024

2. Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks

Author

Valencia, David, Williams, Henry, Xing, Yuning, Gee, Trevor, Liarokapis, Minas, and MacDonald, Bruce A.

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Reinforcement Learning (RL) has been widely used to solve tasks where the environment consistently provides a dense reward value. However, in real-world scenarios, rewards can often be poorly defined or sparse. Auxiliary signals are indispensable for discovering efficient exploration strategies and aiding the learning process. In this work, inspired by intrinsic motivation theory, we postulate that the intrinsic stimuli of novelty and surprise can assist in improving exploration in complex, sparsely rewarded environments. We introduce a novel sample-efficient method able to learn directly from pixels, an image-based extension of TD3 with an autoencoder called \textit{NaSA-TD3}. The experiments demonstrate that NaSA-TD3 is easy to train and an efficient method for tackling complex continuous-control robotic tasks, both in simulated environments and real-world settings. NaSA-TD3 outperforms existing state-of-the-art RL image-based methods in terms of final performance without requiring pre-trained models or human demonstrations.

Published
2024

3. CTD4 -- A Deep Continuous Distributional Actor-Critic Agent with a Kalman Fusion of Multiple Critics

Author

Valencia, David, Williams, Henry, Gee, Trevor, MacDonald, Bruce A, and Liarokapis, Minas

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Categorical Distributional Reinforcement Learning (CDRL) has demonstrated superior sample efficiency in learning complex tasks compared to conventional Reinforcement Learning (RL) approaches. However, the practical application of CDRL is encumbered by challenging projection steps, detailed parameter tuning, and domain knowledge. This paper addresses these challenges by introducing a pioneering Continuous Distributional Model-Free RL algorithm tailored for continuous action spaces. The proposed algorithm simplifies the implementation of distributional RL, adopting an actor-critic architecture wherein the critic outputs a continuous probability distribution. Additionally, we propose an ensemble of multiple critics fused through a Kalman fusion mechanism to mitigate overestimation bias. Through a series of experiments, we validate that our proposed method is easy to train and serves as a sample-efficient solution for executing complex continuous-control tasks.

Published
2024

13. On Differential Mechanisms for Underactuated, Lightweight, Adaptive Prosthetic Hands

Author

Gao, Geng, Shahmohammadi, Mojtaba, Gerez, Lucas, Kontoudis, George P., Liarokapis, Minas, Gao, Geng, Shahmohammadi, Mojtaba, Gerez, Lucas, Kontoudis, George P., and Liarokapis, Minas

Abstract

Over the last decade underactuated, adaptive robot grippers and hands have received an increased interest from the robotics research community. This class of robotic end-effectors can be used in many different fields and scenarios with a very promising application being the development of prosthetic devices. Their suitability for the development of such devices is attributed to the utilization of underactuation that provides increased functionality and dexterity with reduced weight, cost, and control complexity. The most critical components of underactuated, adaptive hands that allow them to perform a broad set of grasp poses are appropriate differential mechanisms that facilitate the actuation of multiple degrees of freedom using a single motor. In this work, we focus on the design, analysis, and experimental validation of a four output geared differential, a series elastic differential, and a whiffletree differential that can incorporate a series of manual and automated locking mechanisms. The locking mechanisms have been developed so as to enhance the control of the differential outputs, allowing for efficient grasp selection with a minimal set of actuators. The differential mechanisms are applied to prosthetic hands, comparing them and describing the benefits and the disadvantages of each.

Published
2021

18. 19 - Practice Problems

Author

Popovic, Marko B., Lamkin-Kennard, Kathleen A., Tashiro, Hiroyuki, Beckerle, Philipp, Willwacher, Steffen, Liarokapis, Minas, Johnson, Michelle J., Goodworth, Adam D., Boyraz, Pinar, and Dobrev, Ivo

Published
2019
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24. An Adaptive Actuation Mechanism for Anthropomorphic Robot Hands

Author

Kontoudis, George P., Liarokapis, Minas, Vamvoudakis, Kyriakos G., Furukawa, Tomonari, Kontoudis, George P., Liarokapis, Minas, Vamvoudakis, Kyriakos G., and Furukawa, Tomonari

Abstract

This paper presents an adaptive actuation mechanism that can be employed for the development of anthropomorphic, dexterous robot hands. The tendon-driven actuation mechanism achieves both flexion/extension and adduction/abduction on the finger's metacarpophalangeal joint using two actuators. Moment arm pulleys are employed to drive the tendon laterally and achieve a simultaneous execution of abduction and flexion motion. Particular emphasis has been given to the modeling and analysis of the actuation mechanism. More specifically, the analysis determines specific values for the design parameters for desired abduction angles. Also, a model for spatial motion is provided that relates the actuation modes with the finger motions. A static balance analysis is performed for the computation of the tendon force at each joint. A model is employed for the computation of the stiffness of the rotational flexure joints. The proposed mechanism has been designed and fabricated with the hybrid deposition manufacturing technique. The efficiency of the mechanism has been validated with experiments that include the assessment of the role of friction, the computation of the reachable workspace, the assessment of the force exertion capabilities, the demonstration of the feasible motions, and the evaluation of the grasping and manipulation capabilities. An anthropomorphic robot hand equipped with the proposed actuation mechanism was also fabricated to evaluate its performance. The proposed mechanism facilitates the collaboration of actuators to increase the exerted forces, improving hand dexterity and allowing the execution of dexterous manipulation tasks.

Published
2019
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25. An Adaptive Actuation Mechanism for Anthropomorphic Robot Hands

Author

Mechanical Engineering, Kontoudis, George P., Liarokapis, Minas, Vamvoudakis, Kyriakos G., Furukawa, Tomonari, Mechanical Engineering, Kontoudis, George P., Liarokapis, Minas, Vamvoudakis, Kyriakos G., and Furukawa, Tomonari

Abstract

This paper presents an adaptive actuation mechanism that can be employed for the development of anthropomorphic, dexterous robot hands. The tendon-driven actuation mechanism achieves both flexion/extension and adduction/abduction on the finger's metacarpophalangeal joint using two actuators. Moment arm pulleys are employed to drive the tendon laterally and achieve a simultaneous execution of abduction and flexion motion. Particular emphasis has been given to the modeling and analysis of the actuation mechanism. More specifically, the analysis determines specific values for the design parameters for desired abduction angles. Also, a model for spatial motion is provided that relates the actuation modes with the finger motions. A static balance analysis is performed for the computation of the tendon force at each joint. A model is employed for the computation of the stiffness of the rotational flexure joints. The proposed mechanism has been designed and fabricated with the hybrid deposition manufacturing technique. The efficiency of the mechanism has been validated with experiments that include the assessment of the role of friction, the computation of the reachable workspace, the assessment of the force exertion capabilities, the demonstration of the feasible motions, and the evaluation of the grasping and manipulation capabilities. An anthropomorphic robot hand equipped with the proposed actuation mechanism was also fabricated to evaluate its performance. The proposed mechanism facilitates the collaboration of actuators to increase the exerted forces, improving hand dexterity and allowing the execution of dexterous manipulation tasks.

Published
2019

29. Switching in Feedforward Control of Grip Force During Tool-Mediated Interaction With Elastic Force Fields.

Author

White, Olivier, Karniel, Amir, Papaxanthis, Charalambos, Barbiero, Marie, Nisky, Ilana, Bianchi, Matteo, Garate, Virginia Ruiz, Qiushi Fu, Liarokapis, Minas, and Moscatelli, Alessandro

Subjects
PHASE transitions, FEEDFORWARD control systems, OBJECT manipulation
Abstract

Switched systems are common in artificial control systems. Here, we suggest that the brain adopts a switched feedforward control of grip forces during manipulation of objects. We measured how participants modulated grip force when interacting with soft and rigid virtual objects when stiffness varied continuously between trials. We identified a sudden phase transition between two forms of feedforward control that differed in the timing of the synchronization between the anticipated load force and the applied grip force. The switch occurred several trials after a threshold stiffness level in the range 100--200 N/m. These results suggest that in the control of grip force, the brain acts as a switching control system. This opens new research questions as to the nature of the discrete state variables that drive the switching. [ABSTRACT FROM AUTHOR]

Published
2018
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30. Contributors

Author

Arslan, Yunus Ziya, Beckerle, Philipp, Bowers, Matthew P., Boyraz, Pinar, Djuric, Ana, Dobrev, Ivo, Fischer, Gregory, Goodworth, Adam D., Iramina, Keiji, Ji, Songbai, Johnson, Michelle J., Jovanovic, Vukica, Kaipa, Krishnanand N., Karabulut, Derya, Lamkin-Kennard, Kathleen A., Liarokapis, Minas, Luo, Ming, Ohta, Jun, Onal, Cagdas, Ortes, Faruk, Padir, Taskin, Popovic, Marko B., Sinyukov, Dmitry A., Tashiro, Hiroyuki, Terasawa, Yasuo, Tetreault, Kimberly, Troy, Karen L., and Willwacher, Steffen

Published
2019
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