Learning policies in simulation is promising for reducing human effort when training robot controllers. This is especially true for soft robots that are more adaptive and safe but also more difficult to accurately model and control. The sim2real gap is the main barrier to successfully transfer policies from simulation to a real robot. System identification can be applied to reduce this gap but traditional identification methods require a lot of manual tuning. Data-driven alternatives can tune dynamical models directly from data but are often data hungry, which also incorporates human effort in collecting data. This work proposes a data-driven, end-to-end differentiable simulator focused on the exciting but challenging domain of tensegrity robots. To the best of the authors’ knowledge, this is the first differentiable physics engine for tensegrity robots that supports cable, contact, and actuation modeling. The aim is to develop a reasonably simplified, data-driven simulation, which can learn approximate dynamics with limited ground truth data. The dynamics must be accurate enough to generate policies that can be transferred back to the ground-truth system. As a first step in this direction, the current work demonstrates sim2sim transfer, where the unknown physical model of MuJoCo acts as a ground truth system.more »
Compact Tensegrity Robots Capable of Locomotion through Mass-shifting
Robustness, compactness, and portability of tensegrity robots make them suitable candidates for locomotion on unknown terrains. Despite these advantages, challenges remain relating to simplicity of fabrication and locomotion.
The paper introduces a design solution for fabricating tensegrity robots of varying morphologies with modular components created using rapid prototyping techniques, including 3D printing and laser-cutting. %
It explores different robot morphologies that attempt to balance structural complexity while facilitating smooth locomotion. The techniques are utilized to fabricate simple tensegrity structures, followed by tensegrity robots in icosahedron and half-circle arc morphologies. Locomotion strategies for such robots involve altering of the position of center-of-mass to induce `tip-over'. Furthermore, the design of curved links of tensegrity mechanisms facilitates continuous change in the point of contact (along the curve) as compared to piece-wise continuous in the traditional straight links (point contact) which induces impulse reaction forces during locomotion. The resulting two tensegrity robots - six-straight strut icosahedron and two half-circle arc morphology - achieve locomotion through internal mass-shifting utilizing the presented modular mass-shifting mechanism. The curve-link tensegrity robot demonstrates smooth locomotion along with folding-unfolding capability.
- Award ID(s):
- 1830432
- Publication Date:
- NSF-PAR ID:
- 10112207
- Journal Name:
- Proceedings of the ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
- Sponsoring Org:
- National Science Foundation
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Tensegrity robots, composed of rigid rods and flexible cables, are difficult to accurately model and control given the presence of complex dynamics and high number of DoFs. Differentiable physics engines have been recently proposed as a data-driven approach for model identification of such complex robotic systems. These engines are often executed at a high-frequency to achieve accurate simulation. Ground truth trajectories for training differentiable engines, however, are not typically available at such high frequencies due to limitations of real-world sensors. The present work focuses on this frequency mismatch, which impacts the modeling accuracy. We proposed a recurrent structure for a differentiable physics engine of tensegrity robots, which can be trained effectively even with low-frequency trajectories. To train this new recurrent engine in a robust way, this work introduces relative to prior work: (i) a new implicit integration scheme, (ii) a progressive training pipeline, and (iii) a differentiable collision checker. A model of NASA's icosahedron SUPERballBot on MuJoCo is used as the ground truth system to collect training data. Simulated experiments show that once the recurrent differentiable engine has been trained given the low-frequency trajectories from MuJoCo, it is able to match the behavior of MuJoCo's system. The criterion for successmore »
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