Breakthroughs, discoveries, and DIY tips sent every weekday. With their bubble-net feeding strategies, humpback whales (Megaptera novaeangliae) are a new level of "smart" animal. Their method of making "nets" out of air bubbles is even more special than once thought. It is considered tool use, which is one way to assess an animal's level of intelligence. Out of seven species of baleen whales, humpbacks are likely the only whales that can pull off the high-performance turns required for bubble-net feeding.

We investigated the performance of existing semi- and fully autonomous methods for controlling flipper-based skid-steer robots. Our study involves reimplementation of these methods for fair comparison and it introduces a novel semi-autonomous control policy that provides a compelling trade-off among current state-of-the-art approaches. We also propose new metrics for assessing cognitive load and traversal quality and offer a benchmarking interface for generating Quality-Load graphs from recorded data. Our results, presented in a 2D Quality-Load space, demonstrate that the new control policy effectively bridges the gap between autonomous and manual control methods. Additionally, we reveal a surprising fact that fully manual, continuous control of all six degrees of freedom remains highly effective when performed by an experienced operator on a well-designed analog controller from third person view.

Abstract-- The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARIS (Precision Application Robot for Inaccessible Spaces): a use-inspired teleoperated mobile robot manipulator system that was conceived, developed, and tested for--and selected as a Phase I winner of--the U.S. Department of Energy's E-ROBOT Prize. To improve the thermal efficiency of buildings, the PARIS platform supports: 1) teleoperated mapping and navigation, enabling the human operator to explore compact spaces; 2) inspection and sensing, facilitating the identification and localization of under-insulated areas; and 3) air-sealing targeted gaps and cracks through which thermal energy is lost. The resulting versatile platform can also be tailored for targeted application of treatments and remediation in constrained spaces. Approximately 75% of the world's greenhouse gas (GHG) emissions result from the cumulative energy sector [1].

This research introduces a novel hydrofoil-based propulsion framework for unmanned aquatic robots, inspired by the undulating locomotion observed in select aquatic species. The proposed system incorporates a camber-modulating mechanism to enhance hydrofoil propulsive force generation and eventually efficiency. Through dynamic simulations, we validate the effectiveness of the camber-adjusting hydrofoil compared to a symmetric counterpart. The results demonstrate a significant improvement in horizontal thrust, emphasizing the potential of the cambering approach to enhance propulsive performance. Additionally, a prototype flipper design is presented, featuring individual control of heave and pitch motions, as well as a camber-adjustment mechanism. The integrated system not only provides efficient water-based propulsion but also offers the capacity for generating vertical forces during take-off maneuvers for seaplanes. The design is tailored to harness wave energy, contributing to the exploration of alternative energy resources. This work advances the understanding of bionic oscillatory principles for aquatic robots and provides a foundation for future developments in environmentally safe and agile underwater exploration.

Orientation estimation is a fundamental task in 3D shape analysis which consists of estimating a shape's orientation axes: its side-, up-, and front-axes. Using this data, one can rotate a shape into canonical orientation, where its orientation axes are aligned with the coordinate axes. Developing an orientation algorithm that reliably estimates complete orientations of general shapes remains an open problem. We introduce a two-stage orientation pipeline that achieves state of the art performance on up-axis estimation and further demonstrate its efficacy on fullorientation estimation, where one seeks all three orientation axes. Unlike previous work, we train and evaluate our method on all of Shapenet rather than a subset of classes. We motivate our engineering contributions by theory describing fundamental obstacles to orientation estimation for rotationally-symmetric shapes, and show how our method avoids these obstacles. Orientation estimation is a fundamental task in 3D shape analysis which consists of estimating a shape's orientation axes: its side-, up-, and front-axes. Using this data, one can rotate a shape into canonical orientation, in which the shape's orientation axes are aligned with the coordinate axes.

Despite significant advancements in robotic locomotion, navigating diverse landscapes for tasks such as search and rescue in complex environments (e.g., sandy terrains, wet forests, and regolith-covered landscapes), as well as responding to mudslides and avalanches, remains a formidable challenge for robotic systems (1,2). While conventional wheeled and legged robots excel on solid ground, they often struggle on granular media such as sand, grains, or pebbles (3) due to the non-uniform and deformable nature of the terrain (4). Moreover, factors like high resistance to penetration, instability, and limited load-bearing capacity of granular terrains can impede the mobility of these robots, leading to issues such as entrapment or slippage (5, 6). In addressing the limitations of traditional wheeled and legged robots, flipper-based locomotion offers a promising alternative. This concept draws inspiration from animals such as penguins, with their agile underwater propulsion using flippers (7, 8), and seals, known for their maneuverability in both water and land (9). Similarly, the fin-based locomotion of mudskippers, effective in terrestrial and aquatic settings, mirrors the adaptability of flipper-based systems, offering parallel insights for robotic design (10,11). Drawing inspiration from aquatic and amphibious animals, we can equip robots with flexible and powerful flippers, enhancing adaptable propulsion and maneuverability in diverse environments, from aquatic to granular terrains (12-16). Among the various examples of flipper-based locomotion in nature, sea turtles are particu-2 Figure 1: Biological and robotic sea turtle hatchlings navigating diverse terrains: Sea turtle hatchling (left) and its robotic counterpart (right) are shown traversing dry sand, small and big rocks, wet sand, and vegetation, illustrating the bio-inspired robot's design effectiveness and its capability to adapt to complex environmental conditions.

Autonomous terrain traversal of articulated tracked robots can reduce operator cognitive load to enhance task efficiency and facilitate extensive deployment. We present a novel hybrid trajectory optimization method aimed at generating efficient, stable, and smooth traversal motions. To achieve this, we develop a planar robot-terrain contact model and divide the robot's motion into hybrid modes of driving and traversing. By using a generalized coordinate description, the configuration space dimension is reduced, which facilitates real-time planning. The hybrid trajectory optimization is transcribed into a nonlinear programming problem and divided into subproblems to be solved in a receding-horizon planning fashion. Mode switching is facilitated by associating optimized motion durations with a predefined traversal sequence. A multi-objective cost function is formulated to further improve the traversal performance. Additionally, map sampling, terrain simplification, and tracking controller modules are integrated into the autonomous terrain traversal system. Our approach is validated in simulation and real-world scenarios with the Searcher robotic platform. Comparative experiments with expert operator control and state-of-the-art methods show advantages in terms of time and energy efficiency, stability, and smoothness of motion.

The autonomous control of flippers plays an important role in enhancing the intelligent operation of tracked robots within complex environments. While existing methods mainly rely on hand-crafted control models, in this paper, we introduce a novel approach that leverages deep reinforcement learning (DRL) techniques for autonomous flipper control in complex terrains. Specifically, we propose a new DRL network named AT-D3QN, which ensures safe and smooth flipper control for tracked robots. It comprises two modules, a feature extraction and fusion module for extracting and integrating robot and environment state features, and a deep Q-Learning control generation module for incorporating expert knowledge to obtain a smooth and efficient control strategy. To train the network, a novel reward function is proposed, considering both learning efficiency and passing smoothness. A simulation environment is constructed using the Pymunk physics engine for training. We then directly apply the trained model to a more realistic Gazebo simulation for quantitative analysis. The consistently high performance of the proposed approach validates its superiority over manual teleoperation.

But despite appearances, a robot that imitates the way they flop over dry land might be effective in search and rescue operations where a wheeled robot would struggle, say the team that made it. Dimuthu Kodippili Arachchige at DePaul University in Chicago and his colleagues created a robot that emulates the way pinnipeds – such as seals and sea lions – bounce and lunge on land, bobbing their heads and bodies to gain momentum while pushing along the ground with their flippers. The robot consists of four identical limbs, each 24 centimetres long and 4 centimetres in diameter. Each limb is made of three silicone tubes that can be filled with liquid to become rigid, or drained to become soft, all wrapped in a hard plastic skin. By selectively filling one or more tubes, the robot can steer each limb in any direction.

A new transforming turtle robot can explore treacherous regions where the land meets the sea--and may lead to future machines that navigate complex real-world conditions. Combining the best mobility features of an ocean-swimming turtle and a land-walking tortoise, the Amphibious Robotic Turtle (ART), described recently in Nature, can morph its limbs from turtlelike flippers to tortoiselike legs. "Most amphibious robots … use dedicated propulsion systems in each environment," says Yale University roboticist Rebecca Kramer-Bottiglio, who is the senior author on the paper. "Our system adapts a single unified propulsion mechanism for both environments: it has four limbs, and those limbs can transition between a flipper state for aquatic locomotion and a leg state for terrestrial locomotion." Each morphing limb is surrounded by a composite polymer material that is malleable when hot and stiff when cool.