000			03130nam a22003377a 4500
003			OSt
005			20250718165629.0
008			250718b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH TK 870 _bB86 2025
100			_aBumagat, Kyle Mhiron P. _eauthor
245			_aMulti-simulated deep reinforcement learning based conversational robot arm/ _cKyle Mhiron P. Bumagat, Arianne Joy D. Evangelista, John Louis D. Lagramada, Jhon Mark G. Remollo, Nicamae G. Tamundong, and Althea R. Villanueva.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_ax, 90pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege Of Engineering.-- _bBachelor of science in electronics engineering: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aAdaptive control that dynamically responds to perturbations is a difficult problem to solve using classical techniques in robot manipulation. This raises a critical issue in creating a fully collaborative robotic system. In this study, deep reinforcement learning was used to train a robot arm in reaching a specific 3D point in space using 4096 parallel environments. The rewards were shaped to minimize action rate and velocity under a curriculum with domain randomization. This study simulated, gain-tuned, and trained the agents using the Isaac Lab framework. Within 6.24 minutes, a performant model arrived, essentially compressing months of training to minutes. Proxima policy optimization (PPO) was used with a multi-layer perceptron (MLP) neural network backbone. A modular ROS 2 package was also presented for bridging natural language understanding to physical systems. The package employs both cloud and edge computing of state-of-the-art speech- to-text, text-to-speech, and large language models. The developed system was evaluated for its educational impact using a structured learning module. Results showed significant improvements between the pre-test and post-test, with a t-value of -19.329 and p < .001. The mean scores for the evaluated factors: Attention, Relevance, Confidence, and Satisfaction ranged from approximately 3.9 to 4.3, with Cronbach’s alpha values indicating high reliability. The Structural Equation Model (SEM) indicated a good fit, SRMR = 0.071. Future enhancements should focus on increasing sample size, adding beginner-friendly resources, and expanding advanced ROS 2 topics. Further development of the conversational robot arm with interactive features can also improve student engagement and learning.
650			_aRobot manipulation
650			_aAdaptive control
650			_aDeep learning
700			_aEvangelista, Arianne Joy D. _eauthor
700			_aLagramada, John Louis D. _eauthor
700			_aRemollo, Jhon Mark G. _eauthor
700			_aTamundong, Nicamae G. _eauthor
700			_aVillanueva, Althea R. _eauthor
942			_2lcc _cBTH COE _n0
999			_c30475 _d30475