Homepage http://ph4m.github.io T.-H. Pham, P. Bailey, D. Posada, J. Enriquez, M. Dolci, P. Twu, IEEE Robotics and Automation Letters (RAL), 2025 - We consider the problem of vision-based 6-DoF object pose estimation in the context of the notional Mars Sample Return campaign, in which a robotic arm would need to localize multiple objects of interest for low-clearance pickup and insertion, under severely constrained hardware. We propose a novel localization algorithm leveraging a... Thu, 25 Sep 2025 00:00:00 -0700 http://ph4m.github.io/2025-09-25-ral/ http://ph4m.github.io/2025-09-25-ral/ T.-H. Pham, W. Seto, S. Daftry, B. Ridge, J. Hansen, T. Thrush, M. Van der Merwe, G. Maggiolino, A. Brinkman, J. Mayo, Y. Cheng, C. Padgett, E. Kulczycki, R. Detry, IEEE Robotics and Automation Letters (RAL), 2021 - We consider the problem of rover relocalization in the context of the notional Mars Sample Return campaign. In this campaign, a rover (R1) needs to be capable of autonomously navigating and localizing itself within an area of approximately 50×50m using reference images collected years earlier by another rover (R0). We... Thu, 18 Mar 2021 00:00:00 -0700 http://ph4m.github.io/2021-03-18-ral/ http://ph4m.github.io/2021-03-18-ral/ S. Daftry, B. Ridge, W. Seto, T.-H. Pham, P. Ilhardt, G. Maggiolino, M. Van der Merwe, A. Brinkman, J. Mayo, E. Kulczyski, R. Detry, IEEE Aerospace Conference, 2021 - A potential Mars Sample Return (MSR) architecture is being jointly studied by NASA and ESA. As currently envisioned, the MSR campaign consists of a series of 3 missions: sample cache, fetch and return to Earth. In this paper, we focus on the fetch part of the MSR, and more specifically... Sat, 06 Mar 2021 00:00:00 -0800 http://ph4m.github.io/2021-03-06-aeroconf/ http://ph4m.github.io/2021-03-06-aeroconf/ T.-H. Pham, W. Seto, S. Daftry, A. Brinkman, J. Mayo, Y. Cheng, C. Padgett, E. Kulczycki, R. Detry, IEEE Aerospace Conference, 2020 - The Mars 2020 rover mission is intended to collect samples which will be stored in metal tubes and left on the surface of Mars, for possible retrieval and return to Earth by a future mission. In the proposed Mars Sample Return (MSR) campaign concept, a follow-up mission would collect the... Sat, 07 Mar 2020 00:00:00 -0800 http://ph4m.github.io/2020-03-07-aeroconf/ http://ph4m.github.io/2020-03-07-aeroconf/ T. Moriyama, G. De Magistris, M. Tatsubori, T.-H. Pham, A. Munawar, R. Tachibana, Asian Simulation Conference, 2018 - Common approaches to control a data-center cooling system rely on approximated system/environment models that are built upon the knowledge of mechanical cooling and electrical and thermal management. These models are difficult to design and often lead to suboptimal or unstable performance. In this paper, we show how deep reinforcement learning... Sat, 27 Oct 2018 00:00:00 -0700 http://ph4m.github.io/2018-10-27-asiasim/ http://ph4m.github.io/2018-10-27-asiasim/ T.-H. Pham, G. De Magistris, D. J. Agravante, S. Chaudhury, A. Munawar, R. Tachibana, arXiv preprint arXiv:1809.08925, 2018 - We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties. The construction and balancing of... Fri, 21 Sep 2018 00:00:00 -0700 http://ph4m.github.io/2018-09-21-ceres/ http://ph4m.github.io/2018-09-21-ceres/ G. De Magistris, A. Munawar, T.-H. Pham, T. Inoue, P. Vinayavekhin, R. Tachibana, 36th Annual Conference of the Robotics Society of Japan (RSJ), 2018 - The accurate modeling of real-world systems and physical interactions is a common challenge towards the resolution of robotics tasks. Machine learning approaches have demonstrated significant results in the modeling of complex systems (e.g., articulated robot structures, cable stretch, fluid dynamics), or to learn robotics tasks (e.g., grasping, reaching) from raw... Tue, 04 Sep 2018 00:00:00 -0700 http://ph4m.github.io/2018-09-04-rsj/ http://ph4m.github.io/2018-09-04-rsj/ T.-H. Pham, G. De Magistris, R. Tachibana, IEEE International Conference on Robotics and Automation (ICRA), 2018 - While deep reinforcement learning techniques have recently produced considerable achievements on many decision-making problems, their use in robotics has largely been limited to simulated worlds or restricted motions, since unconstrained trial-and-error interactions in the real world can have undesirable consequences for the robot or its environment. To overcome such limitations,... Mon, 21 May 2018 00:00:00 -0700 http://ph4m.github.io/2018-05-21-icra-optlayer/ http://ph4m.github.io/2018-05-21-icra-optlayer/ A. Munawar, G. De Magistris, T.-H. Pham, D. Kimura, M. Tatsubori, T. Moriyama, R. Tachibana, G. Booch, IEEE International Conference on Robotics and Automation (ICRA), 2018 - This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms of ontology and rules... Mon, 21 May 2018 00:00:00 -0700 http://ph4m.github.io/2018-05-21-icra-maestrob/ http://ph4m.github.io/2018-05-21-icra-maestrob/ T.-H. Pham, N. Kyriazis, A. A. Argyros, A. Kheddar, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2017 - We consider the problem of estimating realistic contact forces during manipulation, backed with ground-truth measurements, using vision alone. Interaction forces are usually measured by mounting force transducers onto the manipulated objects or the hands. Those are costly, cumbersome, and alter the objects’ physical properties and their perception by the human... Thu, 26 Oct 2017 00:00:00 -0700 http://ph4m.github.io/2017-10-26-pami/ http://ph4m.github.io/2017-10-26-pami/ T.-H. Pham, S. Caron, A. Kheddar, IEEE Transactions on Industrial Informatics (TII), 2017 - We present a novel technique that unobtrusively estimates interaction forces exerted by human participants in multi-contact interaction with rigid environments. Our method uses motion capture only, thus circumventing the need to setup cumbersome force transducers at all potential contacts between the human body and the environment. This problem is particularly... Mon, 23 Oct 2017 00:00:00 -0700 http://ph4m.github.io/2017-10-23-tii/ http://ph4m.github.io/2017-10-23-tii/ T.-H. Pham, A. Bufort, S. Caron, A. Kheddar, "Whole-Body Contact Force Sensing From Motion Capture", in IEEE/SICE International Symposium on System Integration (SII), 2016 - In this paper, we challenge the estimation of contact forces backed with ground-truth sensing in human whole-body interaction with the environment, from motion capture only. Our novel method makes it possible to get rid of cumbersome force sensors in monitoring multi-contact motion together with force data. This problem is very... Tue, 13 Dec 2016 00:00:00 -0800 http://ph4m.github.io/2016-12-13-sii/ http://ph4m.github.io/2016-12-13-sii/ T.-H. Pham, A. Kheddar, A. Qammaz, A. A. Argyros, IEEE ICCV Workshop on Object Understanding for Interaction (OUI), 2015 - Capturing and reproducing hand-objects interactions would open considerable possibilities in computer vision, human-computer interfaces, robotics, animation and rehabilitation. Recently, we witnessed impressive vision-based hand tracking solutions that can potentially be used for such purposes. Yet, a challenging question is: to what extent can vision also capture haptic interactions? These induce... Fri, 11 Dec 2015 00:00:00 -0800 http://ph4m.github.io/2015-12-11-oui/ http://ph4m.github.io/2015-12-11-oui/ T.-H. Pham, A. Kheddar, A. Qammaz, A. A. Argyros, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015 - We present a novel, non-intrusive approach for estimating contact forces during hand-object interactions relying solely on visual input provided by a single RGB-D camera. We consider a manipulated object with known geometrical and physical properties. First, we rely on model-based visual tracking to estimate the object’s pose together with that... Sun, 07 Jun 2015 00:00:00 -0700 http://ph4m.github.io/2015-06-07-cvpr/ http://ph4m.github.io/2015-06-07-cvpr/