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Image-guided minimally invasive robotic surgery is commonly employed for tasks such as needle biopsies or localized therapies. However, the nonlinear deformation of various tissue types presents difficulties for surgeons in achieving precise needle tip placement, particularly when relying on low-fidelity biopsy imaging systems. In this paper, we introduce a method to classify needle biopsy interventions and identify tissue types based on a comprehensive needle-tissue contact model that incorporates both position and force parameters. We trained a transformer model using a comprehensive dataset collected from a formerly developed robotics platform, which consists of synthetic and porcine tissue from various locations (liver, kidney, heart, belly, hock) marked with interaction phases (pre-puncture, puncture, post-puncture, neutral). This model achieves a significant classification accuracy of 0.93. Our demonstrated method can assist surgeons in identifying transitions to different tissues, aiding surgeons with tissue awareness., Comment: 8 pages

Safe reinforcement learning (RL) with assured satisfaction of hard state constraints during training has recently received a lot of attention. Safety filters, e.g., based on control barrier functions (CBFs), provide a promising way for safe RL via modifying the unsafe actions of an RL agent on the fly. Existing safety filter-based approaches typically involve learning of uncertain dynamics and quantifying the learned model error, which leads to conservative filters before a large amount of data is collected to learn a good model, thereby preventing efficient exploration. This paper presents a method for safe and efficient RL using disturbance observers (DOBs) and control barrier functions (CBFs). Unlike most existing safe RL methods that deal with hard state constraints, our method does not involve model learning, and leverages DOBs to accurately estimate the pointwise value of the uncertainty, which is then incorporated into a robust CBF condition to generate safe actions. The DOB-based CBF can be used as a safety filter with model-free RL algorithms by minimally modifying the actions of an RL agent whenever necessary to ensure safety throughout the learning process. Simulation results on a unicycle and a 2D quadrotor demonstrate that the proposed method outperforms a state-of-the-art safe RL algorithm using CBFs and Gaussian processes-based model learning, in terms of safety violation rate, and sample and computational efficiency.

This paper presents an approach to trajectory-centric learning control based on contraction metrics and disturbance estimation for nonlinear systems subject to matched uncertainties. The approach uses deep neural networks to learn uncertain dynamics while still providing guarantees of transient tracking performance throughout the learning phase. Within the proposed approach, a disturbance estimation law is adopted to estimate the pointwise value of the uncertainty, with pre-computable estimation error bounds (EEBs). The learned dynamics, the estimated disturbances, and the EEBs are then incorporated in a robust Riemann energy condition to compute the control law that guarantees exponential convergence of actual trajectories to desired ones throughout the learning phase, even when the learned model is poor. On the other hand, with improved accuracy, the learned model can help improve the robustness of the tracking controller, e.g., against input delays, and can be incorporated to plan better trajectories with improved performance, e.g., lower energy consumption and shorter travel time.The proposed framework is validated on a planar quadrotor example., Comment: 18 pages, 8 figures

A reinforcement learning (RL) policy trained in a nominal environment could fail in a new/perturbed environment due to the existence of dynamic variations. Existing robust methods try to obtain a fixed policy for all envisioned dynamic variation scenarios through robust or adversarial training. These methods could lead to conservative performance due to emphasis on the worst case, and often involve tedious modifications to the training environment. We propose an approach to robustifying a pre-trained non-robust RL policy with $\mathcal{L}_1$ adaptive control. Leveraging the capability of an $\mathcal{L}_1$ control law in the fast estimation of and active compensation for dynamic variations, our approach can significantly improve the robustness of an RL policy trained in a standard (i.e., non-robust) way, either in a simulator or in the real world. Numerical experiments are provided to validate the efficacy of the proposed approach., Comment: A significantly extended version of this paper has been uploaded to arXiv. arXiv:2112.01953

Unmanned aerial vehicles (UAVs) suffer from sensor drifts in GPS denied environments, which can cause safety issues. To avoid intolerable sensor drifts while completing the time-critical coordination task for multi-UAV systems, we propose a safety constrained bi-level control framework. The first level is the time-critical coordination level that achieves a consensus of coordination states and provides a virtual target which is a function of the coordination state. The second level is the safety-critical control level that is designed to follow the virtual target while adapting the attacked UAV(s) at a path re-planning level to support resilient state estimation. In particular, the time-critical coordination level framework generates the desired speed and position profile of the virtual target based on the multi-UAV cooperative mission by the proposed consensus protocol algorithm. The safety-critical control level is able to make each UAV follow its assigned path while detecting the attacks, estimating the state resiliently, and driving the UAV(s) outside the effective range of the spoofing device within the escape time. The numerical simulations of a three-UAV system demonstrate the effectiveness of the proposed safety constrained bi-level control framework., Comment: arXiv admin note: substantial text overlap with arXiv:1910.10826