Are ICE agents trained to use'deadly force' and evade lawsuits? In the weeks since United States Immigration and Customs Enforcement agent Jonathan Ross shot and killed Renee Nicole Good in Minneapolis, Minnesota, another ICE agent shot a Latino man in the leg, according to the Department of Homeland Security. Good's killing and the subsequent shooting have ignited a wave of calls and queries about whether ICE officers can be prosecuted. But the shootings in Minnesota are not outliers, and the history of ICE shootings shows that holding officers to account has been next to impossible. I know, because I investigated the agency's practices, obtaining documents that reveal how it operates and how its officers are trained to shield themselves from scrutiny and lawsuits.

Abstract--Autonomous robots must go from zero prior knowledge to safe control within seconds to operate in unstructured environments. Abrupt terrain changes, such as a sudden transition to ice, create dynamics shifts that can destabilize planners unless the model adapts in real-time. We present a method for online adaptation that combines function encoders with recursive least squares, treating the function encoder coefficients as latent states updated from streaming odometry. We evaluate our approach on a V an der Pol system to highlight algorithmic behavior, in a Unity simulator for high-fidelity off-road navigation, and on a Clearpath Jackal robot, including on a challenging terrain at a local ice rink. Across these settings, our method improves model accuracy and downstream planning, reducing collisions compared to static and meta-learning baselines. High-speed ground vehicles require dynamics models that evolve as quickly as the terrain itself. When operating near the limits of controllability, even modest prediction errors in ground terrain interaction can lead to instability, skidding, or rollover. This problem is particularly apparent in off-road navigation: transitions such as pavement to loose gravel can change friction properties within seconds, while mixed terrain features introduce variation in the terrain properties that are difficult to accurately predict. Planning frameworks such as Model Predictive Path Integral Control (MPPI) [27] rely on an accurate model of the system dynamics to predict rollouts and select optimal control actions in real-time.

--Depth estimation plays a great potential role in obstacle avoidance and navigation for further Mars exploration missions. Compared to traditional stereo matching, learning-based stereo depth estimation provides a data-driven approach to infer dense and precise depth maps from stereo image pairs. However, these methods always suffer performance degradation in environments with sparse textures and lacking geometric constraints, such as the unstructured terrain of Mars. Depth, a depth estimation model tailored for Mars rovers. Considering the sparse and smooth texture of Martian terrain, which is primarily composed of low-frequency features, our model incorporates a convolutional kernel based on wavelet transform that effectively captures low-frequency response and expands the receptive field. Additionally, we introduce a consistency loss that explicitly models the complementary relationship between depth map and surface normal map, utilizing the surface normal as a geometric constraint to enhance the accuracy of depth estimation. Besides, a pixel-wise refinement module with mutual boosting mechanism is designed to iteratively refine both depth and surface normal predictions. Depth achieves a 16% improvement in depth estimation accuracy compared to other state-of-the-art methods in depth estimation. Furthermore, the model demonstrates strong applicability in real-world Martian scenarios, offering a promising solution for future Mars exploration missions. IMITED scene perception capabilities have become a critical bottleneck in the traveling speed of current Mars rovers [1], which hinders the efficient completion of scientific tasks. For example, the Curiosity Rover encounters delays and slowdowns when navigating around obstacles like rocks, resulting in an average travel distance of only 28.9 meters per sol [2]. Similarly, the Zhurong Rover covers merely 6.2 This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFB2902705, in part by Beijing University of Posts and Telecommunications (BUPT) Excellent Ph.D. Students Foundation under Grant CX20241090, and in part by BUPT Innovation and Entrepreneurship Support Program under Grant 2025-YC-T025. Wang are with the School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: junjie@bupt.edu.cn; J. Wang is with State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China (e-mail: wangjiawei98@bupt.edu.cn). H. Xu is with National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China (e-mail: xuhaitao@nssc.ac.cn) Figure 1. Depth estimation holds great potential for enhancing scene perception. It provides a more comprehensive understanding of the 3D structure [4] compared to 2D approaches, such as terrain categorization [5] and semantic segmentation [6].

Ice conditions often require ships to reduce speed and deviate from their main course to avoid damage to the ship. In addition, broken ice fields are becoming the dominant ice conditions encountered in the Arctic, where the effects of collisions with ice are highly dependent on where contact occurs and on the particular features of the ice floes. In this paper, we present AUTO-IceNav, a framework for the autonomous navigation of ships operating in ice floe fields. Trajectories are computed in a receding-horizon manner, where we frequently replan given updated ice field data. During a planning step, we assume a nominal speed that is safe with respect to the current ice conditions, and compute a reference path. We formulate a novel cost function that minimizes the kinetic energy loss of the ship from ship-ice collisions and incorporate this cost as part of our lattice-based path planner. The solution computed by the lattice planning stage is then used as an initial guess in our proposed optimization-based improvement step, producing a locally optimal path. Extensive experiments were conducted both in simulation and in a physical testbed to validate our approach.

The main question we address in this paper is how to scale up visual recognition of unseen classes, also known as zero-shot learning, to tens of thousands of categories as in the ImageNet-21K benchmark. At this scale, especially with many fine-grained categories included in ImageNet-21K, it is critical to learn quality visual semantic representations that are discriminative enough to recognize unseen classes and distinguish them from seen ones. We propose a \emph{H}ierarchical \emph{G}raphical knowledge \emph{R}epresentation framework for the confidence-based classification method, dubbed as HGR-Net. Our experimental results demonstrate that HGR-Net can grasp class inheritance relations by utilizing hierarchical conceptual knowledge. Our method significantly outperformed all existing techniques, boosting the performance by 7\% compared to the runner-up approach on the ImageNet-21K benchmark. We show that HGR-Net is learning-efficient in few-shot scenarios. We also analyzed our method on smaller datasets like ImageNet-21K-P, 2-hops and 3-hops, demonstrating its generalization ability. Our benchmark and code are available at https://kaiyi.me/p/hgrnet.html.

Antarctica contains the most fertile meteorite hunting grounds on Earth. The pristine, dry and cold environment ensures that meteorites deposited there are preserved for long periods. Subsequent glacial flow of the ice sheets where they land concentrates them in particular areas. To date, most meteorites recovered throughout history have been done so in Antarctica in the last 20 years. Furthermore, they are less likely to be contaminated by terrestrial compounds.

A Bayes network based classifier for distinguishing terrestrial rocks from meteorites is implemented onboard the Nomad robot. Equipped with a camera, spectrometer and eddy current sensor, this robot searched the ice sheets of Antarctica and autonomously made the first robotic identification of a meteorite, in January 2000 at the Elephant Moraine. This paper discusses rock classification from a robotic platform, and describes the system onboard Nomad. 1 Introduction Figure 1: Human meteorite search with snowmobiles on the Antarctic ice sheets, and on foot in the moraines. Antarctica contains the most fertile meteorite hunting grounds on Earth. The pristine, dry and cold environment ensures that meteorites deposited there are preserved for long periods.