SAM: a perceptive spoken language-understanding robot

Speech activated manipulator (SAM), a reasoning robotic system with sensory capabilities that interacts with a human partner using natural language, is described. The robot understands, in real time, about 1041 semantically meaningful naturally spoken English language sentences using a vocabulary of about 200 words. SAM includes developments in mechanical control, real-time operating systems, multiprocessor communication and synchronization, kinematics, sensors and perception techniques, speech recognition and natural language understanding, robotic reasoning with gripper and arm motion planning. Speech recognition is augmented with semantic analysis and evaluation to form a complete speech understanding system. Used in conjunction with error recovery rules in the robot expert and frame-based knowledge system, SAM is robust and resistant to user errors. The most interesting aspects of the SAM system are described. Observations and experiences are discussed along with some advice for those interested in building similar systems. >

[1]  Michael K. Brown,et al.  On ultrasonic detection of surface features , 1986, Proceedings. 1986 IEEE International Conference on Robotics and Automation.

[2]  Biing-Hwang Juang,et al.  A model-based connected-digit recognition system using either hidden Markov models or templates , 1986 .

[3]  Ingemar J. Cox,et al.  Real-time software for robotics , 1988, AT&T Technical Journal.

[4]  Michael K. Brown.  Locating object surfaces with an ultrasonic range sensor , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[5]  Lawrence R. Rabiner,et al.  A segmental k-means training procedure for connected word recognition , 1986, AT&T Technical Journal.

[6]  Robert A. Boie,et al.  Active damping of ultrasonic transducers for robotic applications , 1984, ICRA.

[7]  Jay G. Wilpon,et al.  A grammar compiler for connected speech recognition , 1991, IEEE Trans. Signal Process..

[8]  Guy L. Steele,et al.  Common Lisp the Language , 1984 .

[9]  A. L. Gorin,et al.  Parallel level-building on a tree machine (speech recognition) , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[10]  A. L. Gorin,et al.  The ASPEN parallel computer, speech recognition and parallel dynamic programming , 1987, ICASSP '87. IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  Patti Price,et al.  The DARPA 1000-word resource management database for continuous speech recognition , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[12]  J. Olive,et al.  Text to speech—An overview , 1985 .

[13]  M. K. Brown,et al.  A controlled impedance robot gripper , 1985, AT&T Technical Journal.

[14]  Pierre Jules Louis Edmond Vicens,et al.  Aspects of speech recognition by computer , 1969 .

[15]  Richard A. Bolt,et al.  “Put-that-there”: Voice and gesture at the graphics interface , 1980, SIGGRAPH '80.

[16]  Michael K. Brown,et al.  Feature extraction techniques for recognizing solid objects with an ultrasonic range sensor , 1985, IEEE J. Robotics Autom..

[17]  Lawrence R. Rabiner,et al.  Training set design for connected speech recognition , 1991, IEEE Trans. Signal Process..

[18]  W. Eric L. Grimson,et al.  Handey: A robot system that recognizes, plans, and manipulates , 1987, Proceedings. 1987 IEEE International Conference on Robotics and Automation.

[19]  David C. Brown,et al.  NLG - Natural Language Graphics , 1977, IJCAI.

[20]  Robert A. Boie.  Capacitive impedance readout tactile image sensor , 1984, ICRA.

[21]  Behzad Shahraray,et al.  Robust Depth Estimation From Optical Flow , 1988, [1988 Proceedings] Second International Conference on Computer Vision.

[22]  A BoltRichard,et al.  Put-that-there , 1980 .

[23]  M. K. Brown.  Computer simulation of controlled impedance robot hand , 1984, ICRA.

[24]  M K Brown,et al.  The Extraction of Curved Surface Features with Generic Range Sensors , 1986 .