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1. A Recurrent Differentiable Engine for Modeling Tensegrity Robots Trainable with Low-Frequency Data

   Wang, K. ; Aanjaneya, M. ; Bekris, K. E. ( January 2022 , IEEE International Conference on Robotics and Automation (ICRA))

   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 »is whether a locomotion strategy learned using the differentiable engine can be transferred back to the ground-truth system and result in a similar motion. Notably, the amount of ground truth data needed to train the differentiable engine, such that the policy is transferable to the ground truth system, is 1% of the data needed to train the policy directly on the ground-truth system.« less
2. Sim2Sim Evaluation of a Novel Data-Efficient Differentiable Physics Engine for Tensegrity Robots

   Wang, Kun ; Aanjaneya, Mridul ; Bekris, Kostas E ( September 2021 , IEEE/RSJ International Conference on Intelligent Robots and Systems)

   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 »Two different tensegrity robots are used for evaluation and learning of locomotion policies, a 6-bar and a 3-bar tensegrity. The results indicate that only 0.25% of ground truth data are needed to train a policy that works on the ground truth system when the differentiable engine is used for training against training the policy directly on the ground truth system.« less
3. Fast Model Identification via Physics Engines for Data-Efficient Policy Search

   Zhu, Shaojun ; Kimmel, Andrew ; Bekris, Kostas ; Boularias, Abdeslam ( July 2018 , International Joint Conference on Artificial Intelligence (IJCAI))

   This paper presents a practical approach for identifying unknown mechanical parameters, such as mass and friction models of manipulated rigid objects or actuated robotic links, in a succinct manner that aims to improve the performance of policy search algorithms. Key features of this approach are the use of off-the-shelf physics engines and the adaptation of a black-box Bayesian optimization framework for this purpose. The physics engine is used to reproduce in simulation experiments that are performed on a real robot, and the mechanical parameters of the simulated system are automatically fine-tuned so that the simulated trajectories match with the real ones. The optimized model is then used for learning a policy in simulation, before safely deploying it on the real robot. Given the well-known limitations of physics engines in modeling real-world objects, it is generally not possible to find a mechanical model that reproduces in simulation the real trajectories exactly. Moreover, there are many scenarios where a near-optimal policy can be found without having a perfect knowledge of the system. Therefore, searching for a perfect model may not be worth the computational effort in practice. The proposed approach aims then to identify a model that is good enough to approximatemore »the value of a locally optimal policy with a certain confidence, instead of spending all the computational resources on searching for the most accurate model. Empirical evaluations, performed in simulation and on a real robotic manipulation task, show that model identification via physics engines can significantly boost the performance of policy search algorithms that are popular in robotics, such as TRPO, PoWER and PILCO, with no additional real-world data.« less
4. 6N-DoF Pose Tracking for Tensegrity Robots

   Lu, S. ; Johnson, W. ; Wang, K. ; Huang, X. ; Booth, J. ; Kramer-Bottiglio, R. ; Bekris, K. E. ( January 2022 , International Symposium on Robotics Research (ISRR))

   Tensegrity robots, which are composed of compressive elements (rods) and flexible tensile elements (e.g., cables), have a variety of advantages, including flexibility, low weight, and resistance to mechanical impact. Nevertheless, the hybrid soft-rigid nature of these robots also complicates the ability to localize and track their state. This work aims to address what has been recognized as a grand challenge in this domain, i.e., the state estimation of tensegrity robots through a markerless, vision-based method, as well as novel, onboard sensors that can measure the length of the robot's cables. In particular, an iterative optimization process is proposed to track the 6-DoF pose of each rigid element of a tensegrity robot from an RGB-D video as well as endcap distance measurements from the cable sensors. To ensure that the pose estimates of rigid elements are physically feasible, i.e., they are not resulting in collisions between rods or with the environment, physical constraints are introduced during the optimization. Real-world experiments are performed with a 3-bar tensegrity robot, which performs locomotion gaits. Given ground truth data from a motion capture system, the proposed method achieves less than 1~cm translation error and 3 degrees rotation error, which significantly outperforms alternatives. At the samemore »time, the approach can provide accurate pose estimation throughout the robot's motion, while motion capture often fails due to occlusions.« less
5. DeepURL: Deep Pose Estimation Framework for Underwater Relative Localization

   https://doi.org/doi: 10.1109/IROS45743.2020.9341201  

   Joshi, B. ; Modasshir, M. ; Manderson, T. ; Damron, H. ; Xanthidis, M. ; Quattrini Li, A. ; Rekleitis, I. ; Dudek, G. ( October 2020 , Proceedings of the IEEERSJ International Conference on Intelligent Robots and Systems)

   In this paper, we propose a real-time deep-learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous robots localizing themselves, in a communicationconstrained underwater environment, is essential for many applications such as underwater exploration, mapping, multirobot convoying, and other multi-robot tasks. Due to the profound difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image translation network is employed to bridge the gap between the rendered and the real images producing synthetic images for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSACbased PnP. Experimental results in underwater environments (swimming pool and ocean) with different cameras demonstrate the robustness of the proposed technique, where the trained system decreased translation error by 75.5\% and orientation error by 64.6\% over the state-of-the-art methods.