Robots excel at avoiding obstacles but struggle to traverse complex 3-D terrain with cluttered large obstacles. By contrast, insects like cockroaches excel at doing so. Recent research in our lab elucidated how locomotor transitions emerge from locomotor-environment interaction for diverse locomotor challenges abstracted from complex 3-D terrain and the strategies to overcome them. Here we built on these fundamental insights to develop a cockroach-inspired legged robot, Omni-Roach, that integrated these strategies to achieve multi-modal locomotion and provide a robophysical model to study the trade-off between multi-functionality and performance. The robot was based on the RHex design with six compliant legs and featured a rounded body with two wings that can open and a tail with pitch and yaw degrees of freedom. After two development and testing iterations, our robot was capable of overcoming all locomotor challenges with a high performance and success rate. It traversed cluttered rigid pillars only 1.1x robot body width apart, a 2.5x hip height bump, a 0.75x body length gap, densely cluttered flexible beams only 65% body width apart, and self-righted within 4 seconds. Systematic beam traversal experiments further revealed that a downward-pointing tail oscillating laterally helps roll the body into beam gaps and break frictional and interlocking contact to traverse. Our work highlights the usefulness of multi-functional appendages and exaptation for large obstacle traversal.

Attention layers apply a sequence-to-sequence mapping whose parameters depend on the pairwise interactions of the input elements. However, without any structural assumptions, memory and compute scale quadratically with the sequence length. The two main ways to mitigate this are to introduce sparsity by ignoring a sufficient amount of pairwise interactions or to introduce recurrent dependence along them, as SSM does. Although both approaches are reasonable, they both have disadvantages. We propose a novel algorithm that combines the advantages of both concepts. Our idea is based on the efficient inversion of tree-structured matrices.

Sm all animals and robots must often rapidly traverse large bump - like obstacles when moving through complex 3 - D terrains, during which, in addition to leg - ground contact, their body inevitably come s into physical contact with the obstacl es. However, we know little about the performance limits of large bump traversal and how body - terrain interaction affects traversal . To address these, we challenged the discoid cockroach and a n open - loop six - legged robot to dynamically run into a large bump of varying height t o discover the maximal traversal performance, and studied how locomotor modes and traversal performance are affected by body - terrain interaction . Remarkably, d uring rapid running, both t he animal and the robot were cap able of dynamically traversing a bump much higher than its hip height ( up to 4 times the hip height for the animal and 3 times for the robot, respectively) at traversal speeds typical of running, with decreasing traversal probability with increasing bump height. A stability analysis using a novel locomotion energy landscape model explained why traversal was more likely when the animal or robot approach ed the bump with a low initial body yaw and a high initial body pitch, and why deflection was more likely otherwise . Inspired by these principl es, we demonstrated a novel control strategy of active body pitch ing that increase d the robot's maximal traversable bump height by 75%. Our study is a major step in Bioinspiration & Biomimetics (2018), 13, 02600 5; htt ps://li.me.jhu.edu 2 establishing the framework of locomotion energy landscapes to understand locomotion in complex 3 - D terrains .

The uninterpretability of DNNs has led to the adoption of abstract interpretation-based certification as a practical means to establish trust in real-world systems that rely on DNNs. However, the current landscape supports only a limited set of certifiers, and developing new ones or modifying existing ones for different applications remains difficult. This is because the mathematical design of certifiers is expressed at the neuron level, while their implementations are optimized and executed at the tensor level. This mismatch creates a semantic gap between design and implementation, making manual bridging both complex and expertise-intensive -- requiring deep knowledge in formal methods, high-performance computing, etc. We propose a compiler framework that automatically translates neuron-level specifications of DNN certifiers into tensor-based, layer-level implementations. This is enabled by two key innovations: a novel stack-based intermediate representation (IR) and a shape analysis that infers the implicit tensor operations needed to simulate the neuron-level semantics. During lifting, the shape analysis creates tensors in the minimal shape required to perform the corresponding operations. The IR also enables domain-specific optimizations as rewrites. At runtime, the resulting tensor computations exhibit sparsity tied to the DNN architecture. This sparsity does not align well with existing formats. To address this, we introduce g-BCSR, a double-compression format that represents tensors as collections of blocks of varying sizes, each possibly internally sparse. Using our compiler and g-BCSR, we make it easy to develop new certifiers and analyze their utility across diverse DNNs. Despite its flexibility, the compiler achieves performance comparable to hand-optimized implementations.

-- Stand-alone Visual Place Recognition (VPR) systems have little defence against a well-designed adversarial attack, which can lead to disastrous consequences when deployed for robot navigation. We then propose how to close the loop between VPR, an Adversarial Attack Detector (AAD), and active navigation decisions by demonstrating the performance benefit of simulated AADs in a novel experiment paradigm - which we detail for the robotics community to use as a system framework. In the proposed experiment paradigm, we see the addition of AADs across a range of detection accuracies can improve performance over baseline; demonstrating a significant improvement - such as a 50% reduction in the mean along-track localization error - can be achieved with True Positive and False Positive detection rates of only 75% and up to 25% respectively. We examine a variety of metrics including: Along-Track Error, Percentage of Time Attacked, Percentage of Time in an'Unsafe' State, and Longest Continuous Time Under Attack. Expanding further on these results, we provide the first investigation into the efficacy of the Fast Gradient Sign Method (FGSM) adversarial attack for VPR. The analysis in this work highlights the need for AADs in real-world systems for trustworthy navigation, and informs quantitative requirements for system design. Although the impact of adversity in Visual Place Recognition (VPR) is widely understood, with state-of-the-art models offering increasing levels of robustness [1]-[4], the effects of adversarial attacks remain under-explored. Adversarial attacks generally refer to perturbations made to signals or input data by adversaries, with the goal of forcing the output of a system to be incorrect [5]. There has been a significant amount of work researching their effects on perception tasks such as image classification and object detection [5]-[9], yet they have not been widely investigated in the context of VPR. Adversarial attacks on perception systems vary depending on the level of access and information available to an attacker, including digital, physical-world, subtle, or overt attacks [5].

The machine is designed to inspect industrial sites, respond to disasters, carry out logistics operations and support scientific research. Deep Robotics, a company from China, has unveiled a durable four-legged robot built to operate in extreme environments that humans struggle to traverse. It's called the Lynx M20, and it builds upon the agility of its predecessor, the Lynx robot dog. This versatile machine is designed to handle anything from inspecting industrial sites and responding to disasters to carrying out logistics operations and supporting scientific research. Here's what you need to know.

In robotic planetary surface exploration, strategic mobility planning is an important task that involves finding candidate long-distance routes on orbital maps and identifying segments with uncertain traversability. Then, expert human operators establish safe, adaptive traverse plans based on the actual navigation difficulties encountered in these uncertain areas. In this paper, we formalize this challenge as a new, risk-averse variant of the Canadian Traveller Problem (CTP) tailored to global planetary mobility. The objective is to find a traverse policy minimizing a conditional value-at-risk (CVaR) criterion, which is a risk measure with an intuitive interpretation. We propose a novel search algorithm that finds exact CVaR-optimal policies. Our approach leverages well-established optimal AND-OR search techniques intended for (risk-agnostic) expectation minimization and extends these methods to the risk-averse domain. We validate our approach through simulated long-distance planetary surface traverses; we employ real orbital maps of the Martian surface to construct problem instances and use terrain maps to express traversal probabilities in uncertain regions. Our results illustrate different adaptive decision-making schemes depending on the level of risk aversion. Additionally, our problem setup allows accounting for traversability correlations between similar areas of the environment. In such a case, we empirically demonstrate how information-seeking detours can mitigate risk.

Personalized multiple clustering aims to generate diverse partitions of a dataset based on different user-specific aspects, rather than a single clustering. It has recently drawn research interest for accommodating varying user preferences. Recent approaches primarily use CLIP embeddings with proxy learning to extract representations biased toward user clustering preferences. However, CLIP primarily focuses on coarse image-text alignment, lacking a deep contextual understanding of user interests. To overcome these limitations, we propose an agent-centric personalized clustering framework that leverages multi-modal large language models (MLLMs) as agents to comprehensively traverse a relational graph to search for clusters based on user interests. Due to the advanced reasoning mechanism of MLLMs, the obtained clusters align more closely with user-defined criteria than those obtained from CLIP-based representations. To reduce computational overhead, we shorten the agents' traversal path by constructing a relational graph using user-interest-biased embeddings extracted by MLLMs. A large number of weakly connected edges can be filtered out based on embedding similarity, facilitating an efficient traversal search for agents. Experimental results show that the proposed method achieves NMI scores of 0.9667 and 0.9481 on the Card Order and Card Suits benchmarks, respectively, largely improving the SOTA model by over 140%.