Paper in IROS 2012: “Linguistic Transfer of Human Assembly Tasks to Robots”

October 7th, 2012 Irfan Essa Posted in 0205507, Activity Recognition, IROS/ICRA, Mike Stilman, Robotics No Comments »

Linguistic Transfer of Human Assembly Tasks to Robots

  • N. Dantam, I. Essa, and M. Stilman (2012), “Linguistic Transfer of Human Assembly Tasks to Robots,” in Proceedings of Intelligent Robots and Systems (IROS), 2012. [PDF] [DOI] [BIBTEX]
    @InProceedings{    2012-Dantam-LTHATR,
      author  = {N. Dantam and I. Essa and M. Stilman},
      booktitle  = {Proceedings of Intelligent Robots and Systems
      doi    = {10.1109/IROS.2012.6385749},
      pdf    = {},
      title    = {Linguistic Transfer of Human Assembly Tasks to
      year    = {2012}


We demonstrate the automatic transfer of an assembly task from human to robot. This work extends efforts showing the utility of linguistic models in verifiable robot control policies by now performing real visual analysis of human demonstrations to automatically extract a policy for the task. This method tokenizes each human demonstration into a sequence of object connection symbols, then transforms the set of sequences from all demonstrations into an automaton, which represents the task-language for assembling a desired object. Finally, we combine this assembly automaton with a kinematic model of a robot arm to reproduce the demonstrated task.

Presented at: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2012), October 7-12, 2012 Vilamoura, Algarve, Portugal.


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Funding (2011) NSF (1146352) “EAGER: Linguistic Task Transfer for Humans and Cyber Systems”

September 1st, 2011 Irfan Essa Posted in Activity Recognition, Mike Stilman, NSF, Robotics No Comments »

EAGER: Linguistic Task Transfer for Humans and Cyber Systems (Mike Stillman, Irfan Essa) NSF/RI

This project, investigating formal languages as a general methodology for task transfer between distinct cyber-physical systems such as humans and robots, aims to expand the science of cyber physical systems by developing Motion Grammars that will enable task transfer between distinct systems.

Formal languages are tools for encoding, describing and transferring structured knowledge. In natural language, the latter process is called communication. Similarly, we will develop a formal language through which arbitrary cyber-physical systems communicate tasks via structured actions. This investigation of Motion Grammars will contribute to the science of human cognition and the engineering of cyber-physical algorithms. By observing human activities during manipulation we will develop a novel class of hybrid control algorithms based on linguistic representations of task execution. These algorithms will broaden the capabilities of man-made systems and provide the infrastructure for motion transfer between humans, robots and broader systems in a generic context. Furthermore, the representation in a rigorous grammatical context will enable formal verification and validation in future work.
Broader Impacts: The proposed research has direct applications to new solutions for manufacturing, medical treatments such as surgery, logistics and food processing. In turn, each of these areas has a significant impact on the efficiency and convenience of our daily lives. The PIs serve as coordinators of graduate/undergraduate programs and mentors to community schools. In order to guarantee that women and minorities have a significant role in the research, the PIs will annually invite K-12 students from Atlanta schools with primarily African American populations to the laboratories. One-day robot classes will be conducted that engage students in the excitement of hands-on science by interactively using lab equipment to transfer their manipulation skills to a robot arm.

Via Award#1146352 – EAGER: Linguistic Task Transfer for Humans and Cyber Systems.

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Funding (2011): NSF (1059362): “II-New: Motion Grammar Laboratory”

March 1st, 2011 Irfan Essa Posted in Henrik Christensen, Mike Stilman, NSF No Comments »

II-New: Motion Grammar Laboratory (Stillman, Essa, Egerstadt, Christensen, Ueda) Division of Computer and Network Systems Instrumentation Grant.

An anthropomorphic robot arm and a human capture system enable the autonomous performance of assembly tasks with significant uncertainty in problem specifications and environments. This line of work is investigated through sequences of manipulation actions where the guarantee of the completion of task-level objectives is rooted in the discovery of the semantic structure of human manipulation. New research directions in anthropomorphic robotics are explored including programming by demonstration, activity recognition, control and estimation and planning.

The motion grammar laboratory infrastructure allows a great opportunity for research and education. New classroom experiences for undergraduates and graduates provide practical experience in robot human interaction and activity process sharing. This opens possibilities for human training and rehabilitation, as well as assistive personal robotic, and opens the door to a host of technological innovations.

via Award#1059362 – II-New: Motion Grammar Laboratory.

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Paper (2009): ICASSP “Learning Basic Units in American Sign Language using Discriminative Segmental Feature Selection”

February 4th, 2009 Irfan Essa Posted in 0205507, Face and Gesture, ICASSP, James Rehg, Machine Learning, Pei Yin, Thad Starner No Comments »

Pei Yin, Thad Starner, Harley Hamilton, Irfan Essa, James M. Rehg (2009), “Learning Basic Units in American Sign Language using Discriminative Segmental Feature Selection” in IEEE Conference on Acoustics, Speech, and Signal Processing 2009 (ICASSP 2009). Session: Spoken Language Understanding I, Tuesday, April 21, 11:00 – 13:00, Taipei, Taiwan.


The natural language for most deaf signers in the United States is American Sign Language (ASL). ASL has internal structure like spoken languages, and ASL linguists have introduced several phonemic models. The study of ASL phonemes is not only interesting to linguists, but also useful for scalability in recognition by machines. Since machine perception is different than human perception, this paper learns the basic units for ASL directly from data. Comparing with previous studies, our approach computes a set of data-driven units (fenemes) discriminatively from the results of segmental feature selection. The learning iterates the following two steps: first apply discriminative feature selection segmentally to the signs, and then tie the most similar temporal segments to re-train. Intuitively, the sign parts indistinguishable to machines are merged to form basic units, which we call ASL fenemes. Experiments on publicly available ASL recognition data show that the extracted data-driven fenemes are meaningful, and recognition using those fenemes achieves improved accuracy at reduced model complexity

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Paper: ICASSP (2008) “Discriminative Feature Selection for Hidden Markov Models using Segmental Boosting”

April 3rd, 2008 Irfan Essa Posted in 0205507, Face and Gesture, Funding, James Rehg, Machine Learning, PAMI/ICCV/CVPR/ECCV, Papers, Pei Yin, Thad Starner No Comments »

Pei Yin, Irfan Essa, James Rehg, Thad Starner (2008) “Discriminative Feature Selection for Hidden Markov Models using Segmental Boosting”, ICASSP 2008 – March 30 – April 4, 2008 – Las Vegas, Nevada, U.S.A. (Paper: MLSP-P3.D8, Session: Pattern Recognition and Classification II, Time: Thursday, April 3, 15:30 – 17:30, Topic: Machine Learning for Signal Processing: Learning Theory and Modeling) (PDF|Project Site)


icassp08We address the feature selection problem for hidden Markov models (HMMs) in sequence classification. Temporal correlation in sequences often causes difficulty in applying feature selection techniques. Inspired by segmental k-means segmentation (SKS), we propose Segmentally Boosted HMMs (SBHMMs), where the state-optimized features are constructed in a segmental and discriminative manner. The contributions are twofold. First, we introduce a novel feature selection algorithm, where the temporal dynamics are decoupled from the static learning procedure by assuming that the sequential data are piecewise independent and identically distributed. Second, we show that the SBHMM consistently improves traditional HMM recognition in various domains. The reduction of error compared to traditional HMMs ranges from 17% to 70% in American Sign Language recognition, human gait identification, lip reading, and speech recognition.

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