Surgical robot task automation has recently attracted great attention due to its potential to benefit both surgeons and patients. Reinforcement learning (RL) based approaches have demonstrated promising ability to perform automated surgical manipulations on various tasks. To address the exploration challenge, expert demonstrations can be utilized to enhance the learning efficiency via imitation learning (IL) approaches. However, the successes of such methods normally rely on both states and action labels. Unfortunately, action labels can be hard to capture or their manual annotation is prohibitively expensive owing to the requirement for expert knowledge. Emulating expert behaviour using noisy or inaccurate labels poses significant risks, including unintended surgical errors that may result in patient discomfort or, in more severe cases, tissue damage. It therefore remains an appealing and open problem to leverage expert data composed of pure states into RL. In this work, we present an actor-critic RL framework, termed AC-SSIL, to overcome this challenge of improving learning process with state-only demonstrations collected by an unknown expert policy. It adopts a self-supervised IL method, dubbed SSIL, to effectively incorporate expert states into RL paradigms by retrieving from demonstrations the nearest neighbours of the query state and utilizing the bootstrapping of actor networks. It applies similarity-based regularization and improves its prediction capacity jointly with the actor network. We showcase through experiments on an open-source surgical simulation platform that our method delivers remarkable improvements over the RL baseline and exhibits comparable performance against action based IL methods, which implies the efficacy and potential of our method for expert demonstration-guided learning scenarios. Code will be made publicly available at https://github.com/Jingshuai-cqu/AC-SSIL.

Traditional unsupervised optical flow methods are vul-nerable to occlusions and motion boundaries due to lack of object-level information. Therefore, we propose UnSAM-Flow, an unsupervised flow network that also leverages object information from the latest foundation model Segment Anything Model (SAM). We first include a self-supervised semantic augmentation module tailored to SAM masks. We also analyze the poor gradient landscapes of traditional smoothness losses and propose a new smoothness definition based on homography instead. A simple yet effective mask feature module has also been added to further ag-gregate features on the object level. With all these adaptations, our method produces clear optical flow estimation with sharp boundaries around objects, which outperforms state-of-the-art methods on both KITTI and Sintel datasets. Our method also generalizes well across domains and runs very efficiently.

This article presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring thefullposterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challengingnon-Gaussiansetting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association).

This paper presents a novel non-Gaussian inference algorithm, Normalizing Flow iSAM (NF-iSAM), for solving SLAM problems with non-Gaussian factors and/or non-linear measurement models. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to draw samples from the joint posterior of non-Gaussian factor graphs. By leveraging the Bayes tree, NF-iSAM is able to exploit the sparsity structure of SLAM, thus enabling efficient incremental updates similar to iSAM2, albeit in the more challenging non- Gaussian setting. We demonstrate the performance of NF-iSAM and compare it against the state-of-the-art algorithms such as iSAM2 (Gaussian) and mm-iSAM (non-Gaussian) in synthetic and real range-only SLAM datasets.

Fusing disparity maps from different methods is an useful technique to get a refined disparity map by leveraging the complimentary advantage. We present a model for disparity fusion that uses an adversarial network, which can be trained without using ground truth disparity data. We input two initial disparity maps (from the left view) along with auxiliary information (gradient, left & right intensity image) into the generator and train the generator to output a refined disparity map registered on the left view. The refined left disparity map and left intensity image are used to reconstruct a fake right intensity image. Finally, the fake and real right intensity images (from the right stereo vision camera) are fed into a discriminator. The trained network's architecture is effective for the fusion task (90 fps on Kitti2015). The accuracy is on par or even better than the state-of-art supervised methods. A demo video is available https://youtu.be/XTHOF3kZGsU.

Accurately registering point clouds from a cheap low resolution sensor is a challenging task. Existing rigid registration methods failed to use the physical 3D uncertainty distribution of each point from a real sensor in the dynamic alignment process. It is mainly because the uncertainty model for a point is static and invariant and it is hard to describe the change of these physical uncertainty models in different views. Additionally, the existing Gaussian mixture alignment architecture cannot efficiently implement these dynamic changes. This paper proposes a simple architecture combining error estimation from sample covariances and dual dynamic global probability alignment using the convolution of uncertainty-based Gaussian Mixture Models (GMM) from point clouds. Firstly, we propose an efficient way to describe the change of each 3D uncertainty model, which represents the structure of the point cloud much better. Unlike the invariant GMM (representing a fixed point cloud) in traditional Gaussian mixture alignment, we use two uncertainty-based GMMs that change and interact with each other in each iteration. In order to have a wider basin of convergence than other local algorithms, we design a more robust energy function by convolving efficiently the two GMMs over the whole 3D space. Tens of thousands of trials have been conducted on hundreds of models from multiple datasets to demonstrate the proposed method’s superior performance compared with the current state-of-the-art methods. All the materials including our code is available from https://github. com/Canpu999/DUGMA.