Rotor failures in quadrotors may result in high-speed rotation and vibration due to rotor imbalance, which introduces significant challenges for autonomous flight in unknown environments. The mainstream approaches against rotor failures rely on fault-tolerant control (FTC) and predefined trajectory tracking. To the best of our knowledge, online failure detection and diagnosis (FDD), trajectory planning, and FTC of the post-failure quadrotors in unknown and complex environments have not yet been achieved. This paper presents a rotor-failure-aware quadrotor navigation system designed to mitigate the impacts of rotor imbalance. First, a composite FDD-based nonlinear model predictive controller (NMPC), incorporating motor dynamics, is designed to ensure fast failure detection and flight stability. Second, a rotor-failure-aware planner is designed to leverage FDD results and spatial-temporal joint optimization, while a LiDAR-based quadrotor platform with four anti-torque plates is designed to enable reliable perception under high-speed rotation. Lastly, extensive benchmarks against state-of-the-art methods highlight the superior performance of the proposed approach in addressing rotor failures, including propeller unloading and motor stoppage. The experimental results demonstrate, for the first time, that our approach enables autonomous quadrotor flight with rotor failures in challenging environments, including cluttered rooms and unknown forests.

Despite significant advancements in the research of aquatic-aerial robots, existing configurations struggle to efficiently perform underwater, surface, and aerial movement simultaneously. In this paper, we propose a novel multimodal surfing aquatic-aerial vehicle, SurfAA V, which efficiently integrates underwater navigation, surface gliding, and aerial flying capabilities. Thanks to the design of the novel differential thrust vectoring hydrofoil, SurfAA V can achieve efficient surface gliding and underwater navigation without the need for a buoyancy adjustment system. This design provides flexible operational capabilities for both surface and underwater tasks, enabling the robot to quickly carry out underwater monitoring activities. Additionally, when it is necessary to reach another water body, SurfAA V can switch to aerial mode through a gliding takeoff, flying to the target water area to perform corresponding tasks. The main contribution of this letter lies in proposing a new solution for underwater, surface, and aerial movement, designing a novel hybrid prototype concept, developing the required control laws, and validating the robot's ability to successfully perform surface gliding and gliding takeoff. SurfAA V achieves a maximum surface gliding speed of 7.96 m/s and a maximum underwater speed of 3.1 m/s. The prototype's surface gliding maneuverability and underwater cruising maneuverability both exceed those of existing aquatic-aerial vehicles. N recent years, with the rapid development of robotics technology, unmanned aquatic-aerial vehicles(UAA Vs) capable of adapting to complex environments and performing diversified tasks have gradually become a research hotspot. These robots integrate the advantages of both autonomous underwater vehicles(AUVs) and unmanned aerial vehicles(UA Vs), allowing them to freely switch between motion modes in water and air. This capability greatly broadens the application scope of traditional robots, demonstrating enormous potential in multi-domain missions such as environmental monitoring[1], disaster rescue[2], and national defense[3].

Enhancing the explosive power output of the knee joints is critical for improving the agility and obstacle-crossing capabilities of humanoid robots. However, a mismatch between the knee-to-center-of-mass (CoM) transmission ratio and jumping demands, coupled with motor performance degradation at high speeds, restricts the duration of high-power output and limits jump performance. To address these problems, this paper introduces a novel knee joint design paradigm employing a dynamically decreasing reduction ratio for explosive output during jump. Analysis of motor output characteristics and knee kinematics during jumping inspired a coupling strategy in which the reduction ratio gradually decreases as the joint extends. A high initial ratio rapidly increases torque at jump initiation, while its gradual reduction minimizes motor speed increments and power losses, thereby maintaining sustained high-power output. A compact and efficient linear actuator-driven guide-rod mechanism realizes this coupling strategy, supported by parameter optimization guided by explosive jump control strategies. Experimental validation demonstrated a 63 cm vertical jump on a single-joint platform (a theoretical improvement of 28.1\% over the optimal fixed-ratio joints). Integrated into a humanoid robot, the proposed design enabled a 1.1 m long jump, a 0.5 m vertical jump, and a 0.5 m box jump.

But it's high time we talked about another player: Africa. As MIT Technology Review has written before, AI is creating a new colonial world order, where the technology is enriching a small minority of people at the expense of the rest of the world. African AI researchers are determined to change that. However, they face many barriers. AI research is eye-wateringly expensive, and African startups and researchers get a fraction as much funding as their Western or Asian counterparts. They have to innovate and rely on open-source resources to do more with less.

Being widespread in human motor behavior, dynamic movements demonstrate higher efficiency and greater capacity to address a broader range of skill domains compared to their quasi-static counterparts. Among the frequently studied dynamic manipulation problems, robotic juggling tasks stand out due to their inherent ability to scale their difficulty levels to arbitrary extents, making them an excellent subject for investigation. In this study, we explore juggling patterns with mixed throw heights, following the vanilla siteswap juggling notation, which jugglers widely adopted to describe toss juggling patterns. This requires extending our previous analysis of the simpler cascade juggling task by a throw-height sequence planner and further constraints on the end effector trajectory. These are not necessary for cascade patterns but are vital to achieving patterns with mixed throw heights. Using a simulated environment, we demonstrate successful juggling of most common 3-9 ball siteswap patterns up to 9 ball height, transitions between these patterns, and random sequences covering all possible vanilla siteswap patterns with throws between 2 and 9 ball height. https://kai-ploeger.com/beyond-cascades

Acting is an important decisional function to ensure proper deliberation on an autonomous system (Ingrand and Ghallab, 2017). It often sits between planning and the platform, but unlike planning it is an online process, which must stay reactive to the dynamic of the environment and the platform and cannot devote resources to long computations and complex searches. Acting often relies on models, called skills, which specify how to perform actions (as an operational model), while the action models used for planning are more what is abstractly needed to perform the action (as a descriptive model) (Ghallab et al., 2016). The most basic skills need to connect to the commands made available by the functional level to the acting component, call them asynchronously, get execution status and result, but it also needs means to receive exogenous events as they occur in the environment. This action/command dispatching may also rely on preconditions and invariants checking, interruptions, temporal constraints, etc. Above the basic skills one often finds more complex skills, similar to programs with control structures to allow for local choices and local recoveries with test, branching, looping, parallel and asynchronous execution. Considering the expected functionalities of an acting component, its skill language/framework should provide the following features: Support for Validation and Verification (V&V). Notwithstanding the other functionalities, this is the feature the work presented in this paper focuses on. One cannot only rely on basic skills connecting to the robot commands, one also needs some programming primitives (e.g., test, branching, loop). 1

This study delves into the flight behaviors of Budgerigars (Melopsittacus undulatus) to gain insights into their flight trajectories and movements. Using 3D reconstruction from stereo video camera recordings, we closely examine the velocity and acceleration patterns during three flight motion takeoff, flying and landing. The findings not only contribute to our understanding of bird behaviors but also hold significant implications for the advancement of algorithms in Unmanned Aerial Vehicles (UAVs). The research aims to bridge the gap between biological principles observed in birds and the application of these insights in developing more efficient and autonomous UAVs. In the context of the increasing use of drones, this study focuses on the biologically inspired principles drawn from bird behaviors, particularly during takeoff, flying and landing flight, to enhance UAV capabilities. The dataset created for this research sheds light on Budgerigars' takeoff, flying, and landing techniques, emphasizing their ability to control speed across different situations and surfaces. The study underscores the potential of incorporating these principles into UAV algorithms, addressing challenges related to short-range navigation, takeoff, flying, and landing.

Urban Air Mobility (UAM) offers a solution to current traffic congestion by providing on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present VertiSync, a centralized traffic management policy for on-demand UAM networks. VertiSync schedules the aircraft for either servicing trip requests or rebalancing in the network subject to aircraft safety margins and separation requirements during takeoff and landing. We characterize the system-level throughput of VertiSync, which determines the demand threshold at which travel times transition from being stabilized to being increasing over time. We show that the proposed policy is able to maximize the throughput for sufficiently large fleet sizes. We demonstrate the performance of VertiSync through a case study for the city of Los Angeles. We show that VertiSync significantly reduces travel times compared to a first-come first-serve scheduling policy.

Self-flying airplanes are gearing up for take-off, as Boeing, Airbus and other companies are testing autonomous systems and craft - but pilots are pushing back over safety risks. The technologies enable autonomous landings, handle-inflight emergencies and relax the Federal Aviation Administration's law requiring two pilots in the cockpit. Pilots have shared their concerns on Twitter, with many stating that two pilots are required in an emergency. Tony Driza, who has been an airline pilot for 40 years, posted that he can'equivocally state that when an emergency situation arises in the cockpit, a full crew is necessary to deal with it.' While autonomous airplanes are still early, Boeing's CEO Dave Calhoun said in a Bloomberg TV interview the technology will'come to all airplanes eventually.' Boeing has developed an autonomous refueling plane for the US Navy, the MQ-25.

It has been suggested that in a rapid enough takeoff scenario, governance would not be useful, because the transition to superintelligence would be too rapid for human actors - whether governments, corporations, or individuals - to respond to. This seems to imply that we only care about takeoff speed. And if that is the only relevant factor, the case for governance only applies if you believe slow takeoff is likely. Of course, it also matters how long we have until takeoff - but even so, I think this leaves a fair amount on the table in terms of what governance could do, and I want to try to make the case that even in that world, governance (still defined broadly1) is important - though in different ways. To make the argument, I will lay out three possibilities about AI alignment which are orthogonal to takeoff speed and timing; alignment-by-default, prosaic alignment, and provable alignment.