Prototype Resilient, Self-Modeling Robots
The main contribution described in the Report "Resilient machines
through continuous self-modeling" (J. Bongard et al., 17 Nov. 2006,
p. 1118, and the accompanying Perspective, "What do robots dream of?",
C. Adami, p. 1093) is a robot that can autonomously recover from certain
types of unexpected damage, through an adaptive selfmodel derived from
actuation-sensation relationships, used to generate forward locomotion.
Neither the Report nor the Perspective mention that the first resilient,
self-modeling machines of this type were built by
(now at IDSIA)
et al., who won the 2004 RoboCup in the very fast, small-size league
(where human adversaries with a joystick have no chance). Gloye-Foerster
et al. equipped the RoboCup robots with self-models based on artificial
neural networks, to model current properties of their four-wheel
omni-directional drives. They showed that when a robot gets damaged and is
no longer able to execute a precise driving pattern, it can heal itself
by quickly adapting its model of the relation between motor commands
and sensory inputs, and using the modified model to plan and optimize
future driving trajectories (1, 2).
1. A. Gloye, F. Wiesel, O. Tenchio, M. Simon, IT Information Technol. 47 (no. 5), 250 (2005).
2. See also
Resilient, Self-Modeling Robots by
The correspondence to the left appeared in
4 May 2007:
Vol. 316. no. 5825, p. 688,
DOI: 10.1126/ science.316.5825.688c
IDSIA, Galleria 2, 6928 Manno-Lugano, Switzerland &
Robotics and Embedded Systems,
Tech. Univ. München,
Boltzmannstr. 3, 85748 Garching, Germany
The authors published a
2. More papers (1990-2007) on our
that invent and conduct experiments,
actively determining which data to collect in
order to improve their self-model.
3. More on
with self-models (papers since 1989).
4. More on