Overview
The Interaction Lab contributes to the MARS-2020 project as a subcontractor to NASA. Our work focuses on the generation and use of primitive motions for humanoid motion control. In the scope of the MARS-2020 project, our efforts are directed to the
Robonaut humanoid robot.
Team & Contact Info
- Principal Investigator (PI):
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- Postdocs:
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- Graduate Students:
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- Other Participants:
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Mail either the PI or Team Leader.
Projects
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Real Time Human Motion Tracker
The image shows a 3DOF rotational sensor being develloped at the lab as a
solution to (wireless) low cost motion capture device capable of logging
high DOF human motion in an unstructured environment. Sensors are based on
an Atmel 8 bit microcontroller w/10 bit ADC, 8 Mhz, including filtered 300
deg/sec Gyroscopes and 2-G Accelerometers.
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Humanoid Motion Planning
The image shows the type of roadmap graph we are using for applications
such as motion planning or collision-free IK. We are currently
investigating the use of demonstrated data motion in order to build
efficient roadmaps. Nodes and edges of this 17-dimensional roadmap are
graphically represented in the image with two colors, showing the position
of the right wrist (red) and left wrist (green).
- Example videos of obtained collision-free two arm motions with Robonaut:
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bothup (4.1MB), updown (5.8MB), and
around (3.2MB).
- Also Learning Reaching Primitives from Demonstrated Motion
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Hierarchical Model for Learning by Imitation
The goals of this project are to develop models of motor learning for
articulated agents through imitation. We are interested on methods suited
to real time interaction, and able to perform learning online.
Example videos:
- Parametric primitives: Demonstration [left] and imitation [right] of "figure-8" movement [mpg, 982KB].
- Sequence learning: Demonstration [left] and imitation [right] of a movement sequence [mpg, 3MB].
See also, Metric for the Evaluation of Imitation
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Parametric Motor Primitives and Bayesian Motion Classification
We have developed a movement classifier that aims to improve communication
between humans and humanoids via motion. The classifier uses a Bayesian
classifier to categorize joint-angle data into a motor primitive.
Motor primitives are parametric and kinematic models of motion.
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Automated Derivation of Motion Primitives
We employ spatio-temporal dimension reduction to analyze motion data. The
image shows the path of 5 consecutive Robonaut grasp motions embedded in a
3D space constructed with the use of statio-temporal isomap over the full
joint angle space of the original captured data. The structure of the
motion was succesfully recovered, even though each grasp was performed in a
different cartesian location in the workspace (work in collaboration with
Alan Peters).
Motion primitives are used to construct
vocabularies of behaviors that can be used to various tasks.
Here are some example videos:
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Publications
- Nathan Miller, Odest C. Jenkins, Marcelo Kallmann, and Maja J. Mataric´. "Motion Capture from Inertial Sensing for Untethered Humanoid Teleoperation". In Proceedings of the IEEE-RAS International Conference on Humanoid Robotics (Humanoids), Los Angeles, CA, Nov 2004.(.pdf)
- N. Miller, O. C. Jenkins, M. Kallmann, M. J. Mataric, Motion Capture
from Inertial Sensing for Untethered Humanoid, Technical Report
CRES-04-010 USC.
- Evan Drumwright, Marcelo Kallmann, and Maja J. Mataric´. "Towards Single-Arm Reaching for Humanoid Robots in Dynamic Environments". Poster paper in Proceedings of the IEEE-RAS International Conference on Humanoid Robotics (Humanoids), Santa Monica, CA, Nov 2004.(.pdf)
- Marcelo Kallmann, Robert Bargmann, and Maja J. Mataric´. "Planning the Sequencing of Movement Primitives". To appear in Proceedings of the International Conference on Simulation of Adaptive Behavior (SAB), pages 193-200, 2004.(.pdf) (Also, videos here)
- Marcelo Kallmann and Maja J. Mataric´. "Motion Planning Using Dynamic Roadmaps". To appear in IEEE International Conference on Robotics and Automation (ICRA), pages 4399-4404, 2004.(.pdf)
- R. Amit, A Metric for the Evaluation of Imitation, to appear in the
Doctoral Consortium, Proceedings of the 19th National Conference on
Artificial Intelligence (AAAI'04), San Jose, California, July 25-29, 2004.
- R. Amit, A Correspondence Metric for Imitation, to appear in the
Poster Abstracts, Proceedings of the 19th National Conference on
Artificial Intelligence (AAAI'04), San Jose, California, July 25-29, 2004.
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- O. C. Jenkins and M. Mataric', Automated Derivation of Behavior
Vocabularies for Autonomous Humanoid Motion, Second International Joint
Conference on Autonomous Agents and Multiagent Systems, pp. 225-232,
Melbourne, Australia, July 2003.
- O. C. Jenkins, Data-driven Derivation of Skills for Autonomous Humanoid
Agents, Ph.D. dissertation, The University of Southern California, 2003.
- Odest C. Jenkins and Maja J. Mataric´. "A Spatio-temporal Extension to Isomap Nonlinear Dimension Reduction". In International Conference on Machine Learning (ICML), pages 441-448, Banff, Alberta, Canada, Jul 2004.(.pdf)
- Evan Drumwright, Odest C. Jenkins, and Maja J. Mataric´. "Exemplar-Based Primitives for Humanoid Movement Classification and Control". In IEEE International Conference on Robotics and Automation (ICRA), pages 140-145, Apr 2004.(.gz)(.pdf)
- D. Erol, J. Park, E. Turkay, K. Kawamura, O. C. Jenkins, and M. J.
Mataric', Motion Generation for Humanoid Robots with Automatically
Derived Behaviors, IEEE Systems Man and Cybernetics (SMC 2003), pp
1816-1822, October, 2003, Washington, D.C., USA.
- Odest C. Jenkins and Maja J. Mataric´. "Performance-Derived Behavior Vocabularies: Data-Driven Acquisition of Skills From Motion". In International Journal of Humanoid Robotics, 1(2):237-288, Jun 2004.(.pdf)
- O. C. Jenkins, M. N. Nicolescu, and M. J Mataric', Autonomy and
Supervision for Robot Skills and Tasks Learned from Demonstration, to
appear in the AAAI-04 Workshop on Supervisory Control of Learning and
Adaptive Systems, 2004.
- Evan Drumwright and Maja J. Mataric´. "Generating and Recognizing Free-Space Movement in Humanoid Robotics". In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1672-1678, Las Vegas, Nevada, Oct 2003.(.pdf)
Related Links
Mars2020 links:
Funding Details
This work is supported by the DARPA MARS 2020 Program project
"Acquisition of Autonomous Behaviors by Robotic Assistants", via
the NASA subcontract grant NAG9-1444 "Skill Learning by
Primitives-Based Demonstration & Imitation".
See also:
a complete list of sponsors of the lab.
