2 research outputs found
Efficient Model Identification for Tensegrity Locomotion
This paper aims to identify in a practical manner unknown physical
parameters, such as mechanical models of actuated robot links, which are
critical in dynamical robotic tasks. Key features include the use of an
off-the-shelf physics engine and the Bayesian optimization framework. The task
being considered is locomotion with a high-dimensional, compliant Tensegrity
robot. A key insight, in this case, is the need to project the model
identification challenge into an appropriate lower dimensional space for
efficiency. Comparisons with alternatives indicate that the proposed method can
identify the parameters more accurately within the given time budget, which
also results in more precise locomotion control
Learning to Transfer Dynamic Models of Underactuated Soft Robotic Hands
Transfer learning is a popular approach to bypassing data limitations in one
domain by leveraging data from another domain. This is especially useful in
robotics, as it allows practitioners to reduce data collection with physical
robots, which can be time-consuming and cause wear and tear. The most common
way of doing this with neural networks is to take an existing neural network,
and simply train it more with new data. However, we show that in some
situations this can lead to significantly worse performance than simply using
the transferred model without adaptation. We find that a major cause of these
problems is that models trained on small amounts of data can have chaotic or
divergent behavior in some regions. We derive an upper bound on the Lyapunov
exponent of a trained transition model, and demonstrate two approaches that
make use of this insight. Both show significant improvement over traditional
fine-tuning. Experiments performed on real underactuated soft robotic hands
clearly demonstrate the capability to transfer a dynamic model from one hand to
another.Comment: ICRA 202