2 research outputs found
Autonomous Functional Locomotion in a Tendon-Driven Limb via Limited Experience
Robots will become ubiquitously useful only when they can use few attempts to
teach themselves to perform different tasks, even with complex bodies and in
dynamical environments. Vertebrates, in fact, successfully use trial-and-error
to learn multiple tasks in spite of their intricate tendon-driven anatomies.
Roboticists find such tendon-driven systems particularly hard to control
because they are simultaneously nonlinear, under-determined (many tendon
tensions combine to produce few net joint torques), and over-determined (few
joint rotations define how many tendons need to be reeled-in/payed-out). We
demonstrate---for the first time in simulation and in hardware---how a
model-free approach allows few-shot autonomous learning to produce effective
locomotion in a 3-tendon/2-joint tendon-driven leg. Initially, an artificial
neural network fed by sparsely sampled data collected using motor babbling
creates an inverse map from limb kinematics to motor activations, which is
analogous to juvenile vertebrates playing during development. Thereafter,
iterative reward-driven exploration of candidate motor activations
simultaneously refines the inverse map and finds a functional locomotor
limit-cycle autonomously. This biologically-inspired algorithm, which we call
G2P (General to Particular), enables versatile adaptation of robots to changes
in the target task, mechanics of their bodies, and environment. Moreover, this
work empowers future studies of few-shot autonomous learning in biological
systems, which is the foundation of their enviable functional versatility.Comment: 39 pages, 6 figure
Autonomous Control of a Tendon-driven Robotic Limb with Elastic Elements Reveals that Added Elasticity can Enhance Learning
Passive elastic elements can contribute to stability, energetic efficiency,
and impact absorption in both biological and robotic systems. They also add
dynamical complexity which makes them more challenging to model and control.
The impact of this added complexity to autonomous learning has not been
thoroughly explored. This is especially relevant to tendon-driven limbs whose
cables and tendons are inevitably elastic. Here, we explored the efficacy of
autonomous learning and control on a simulated bio-plausible tendon-driven leg
across different tendon stiffness values. We demonstrate that increasing
stiffness of the simulated muscles can require more iterations for the inverse
map to converge but can then perform more accurately, especially in discrete
tasks. Moreover, the system is robust to subsequent changes in muscle
stiffnesses and can adapt on-the-go within 5 attempts. Lastly, we test the
system for the functional task of locomotion, and found similar effects of
muscle stiffness to learning and performance. Given that a range of stiffness
values led to improved learning and maximized performance, we conclude the
robot bodies and autonomous controllers---at least for tendon-driven
systems---can be co-developed to take advantage of elastic elements.
Importantly, this opens also the door to development efforts that recapitulate
the beneficial aspects of the co-evolution of brains and bodies in vertebrates