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1. Learning Reactive and Predictive Differentiable Controllers for Switching Linear Dynamical Models

   Saxena, S. ; LaGrassa, A. ; Kroemer, O. ( April 2021 , IEEE International Conference on Robotics and Automation)

   null (Ed.)

   Humans leverage the dynamics of the environment and their own bodies to accomplish challenging tasks such as grasping an object while walking past it or pushing off a wall to turn a corner. Such tasks often involve switching dynamics as the robot makes and breaks contact. Learning these dynamics is a challenging problem and prone to model inaccuracies, especially near contact regions. In this work, we present a framework for learning composite dynamical behaviors from expert demonstrations. We learn a switching linear dynamical model with contacts encoded in switching conditions as a close approximation of our system dynamics. We then use discrete-time LQR as the differentiable policy class for data-efficient learning of control to develop a control strategy that operates over multiple dynamical modes and takes into account discontinuities due to contact. In addition to predicting interactions with the environment, our policy effectively reacts to inaccurate predictions such as unanticipated contacts. Through simulation and real world experiments, we demonstrate generalization of learned behaviors to different scenarios and robustness to model inaccuracies during execution. 

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2. Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control

   Mamakoukas, Giorgos ; Xherija, Orest ; Murphey, Todd ( January 2020 , Advances in neural information processing systems)

   null (Ed.)

   Learning a stable Linear Dynamical System (LDS) from data involves creating models that both minimize reconstruction error and enforce stability of the learned representation. We propose a novel algorithm for learning stable LDSs. Using a recent characterization of stable matrices, we present an optimization method that ensures stability at every step and iteratively improves the reconstruction error using gradient directions derived in this paper. When applied to LDSs with inputs, our approach—in contrast to current methods for learning stable LDSs—updates both the state and control matrices, expanding the solution space and allowing for models with lower reconstruction error. We apply our algorithm in simulations and experiments to a variety of problems, including learning dynamic textures from image sequences and controlling a robotic manipulator. Compared to existing approaches, our proposed method achieves an orders-of-magnitude improvement in reconstruction error and superior results in terms of control performance. In addition, it is provably more memory efficient, with an O(n2) space complexity compared to O(n4) of competing alternatives, thus scaling to higher-dimensional systems when the other methods fail. The code of the proposed algorithm and animations of the results can be found at https: //github.com/giorgosmamakoukas/MemoryEfficientStableLDS. 

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3. ASAP-CORPS: A Semi-Autonomous Platform for COntact-Rich Precision Surgery

   https://doi.org/10.1093/milmed/usad175  

   Balakuntala, Mythra V. ; Gonzalez, Glebys T. ; Wachs, Juan P. ; Voyles, Richard M. ( November 2023 , Military Medicine)

   Remote military operations require rapid response times for effective relief and critical care. Yet, the military theater is under austere conditions, so communication links are unreliable and subject to physical and virtual attacks and degradation at unpredictable times. Immediate medical care at these austere locations requires semi-autonomous teleoperated systems, which enable the completion of medical procedures even under interrupted networks while isolating the medics from the dangers of the battlefield. However, to achieve autonomy for complex surgical and critical care procedures, robots require extensive programming or massive libraries of surgical skill demonstrations to learn effective policies using machine learning algorithms. Although such datasets are achievable for simple tasks, providing a large number of demonstrations for surgical maneuvers is not practical. This article presents a method for learning from demonstration, combining knowledge from demonstrations to eliminate reward shaping in reinforcement learning (RL). In addition to reducing the data required for training, the self-supervised nature of RL, in conjunction with expert knowledge-driven rewards, produces more generalizable policies tolerant to dynamic environment changes. A multimodal representation for interaction enables learning complex contact-rich surgical maneuvers. The effectiveness of the approach is shown using the cricothyroidotomy task, as it is a standard procedure seen in critical care to open the airway. In addition, we also provide a method for segmenting the teleoperator’s demonstration into subtasks and classifying the subtasks using sequence modeling.

   A database of demonstrations for the cricothyroidotomy task was collected, comprising six fundamental maneuvers referred to as surgemes. The dataset was collected by teleoperating a collaborative robotic platform—SuperBaxter, with modified surgical grippers. Then, two learning models are developed for processing the dataset—one for automatic segmentation of the task demonstrations into a sequence of surgemes and the second for classifying each segment into labeled surgemes. Finally, a multimodal off-policy RL with rewards learned from demonstrations was developed to learn the surgeme execution from these demonstrations.

   The task segmentation model has an accuracy of 98.2%. The surgeme classification model using the proposed interaction features achieved a classification accuracy of 96.25% averaged across all surgemes compared to 87.08% without these features and 85.4% using a support vector machine classifier. Finally, the robot execution achieved a task success rate of 93.5% compared to baselines of behavioral cloning (78.3%) and a twin-delayed deep deterministic policy gradient with shaped rewards (82.6%).

   Results indicate that the proposed interaction features for the segmentation and classification of surgical tasks improve classification accuracy. The proposed method for learning surgemes from demonstrations exceeds popular methods for skill learning. The effectiveness of the proposed approach demonstrates the potential for future remote telemedicine on battlefields.

    

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4. Learning to Simulate Complex Physics with Graph Networks

   Sanchez-Gonzalez, A. ; Godwin, J. ; Pfaff, T. ; Ying, R. ; Leskovec, J. ; Battaglia, P. ( April 2020 , International Conference on Machine Learning (ICML))

   Here we present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another. Our framework—which we term “Graph Network-based Simulators” (GNS)—represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time. Our model was robust to hyperparameter choices across various evaluation metrics: the main determinants of long-term performance were the number of message-passing steps, and mitigating the accumulation of error by corrupting the training data with noise. Our GNS framework advances the state-of-the-art in learned physical simulation, and holds promise for solving a wide range of complex forward and inverse problems. 

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5. Physically Invertible System Identification for Monitoring System Edges with Unobservability

   Jingyi Yuan and Yang Weng ( September 2022 , Machine Learning and Knowledge Discovery in Databases: European Conference)

   Nowadays, the data collected in physical/engineering systems allows various machine learning methods to conduct system monitoring and control, when the physical knowledge on the system edge is limited and challenging to recover completely. Solving such problems typically requires identifying forward system mapping rules, from system states to the output measurements. However, the forward system identification based on digital twin can hardly provide complete monitoring functions, such as state estimation, e.g., to infer the states from measurements. While one can directly learn the inverse mapping rule, it is more desirable to re-utilize the forward digital twin since it is relatively easy to embed physical law there to regularize the inverse process and avoid overfitting. For this purpose, this paper proposes an invertible learning structure based on designing parallel paths in structural neural networks with basis functionals and embedding virtual storage variables for information preservation. For such a two-way digital twin modeling, there is an additional challenge of multiple solutions for system inverse, which contradict the reality of one feasible solution for the current system. To avoid ambiguous inverse, the proposed model maximizes the physical likelihood to contract the original solution space, leading to the unique system operation status of interest. We validate the proposed method on various physical system monitoring tasks and scenarios, such as inverse kinematics problems, power system state estimation, etc. Furthermore, by building a perfect match of a forward-inverse pair, the proposed method obtains accurate and computation-efficient inverse predictions, given observations. Finally, the forward physical interpretation and small prediction errors guarantee the explainability of the invertible structure, compared to standard learning methods. 

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