Related papers: Human-guided Swarms: Impedance Control-inspired In…
To achieve high fidelity haptic rendering of soft objects in a high mobility virtual environment, we propose a novel haptic display DandelionTouch. The tactile actuators are delivered to the fingertips of the user by a swarm of drones.…
Achieving scalable coordination in large robotic swarms is often constrained by reliance on inter-agent communication, which introduces latency, bandwidth limitations, and vulnerability to failure. To address this gap, a decentralized…
Recent empirical observations of three-dimensional bird flocks and human crowds have challenged the long-prevailing assumption that a metric interaction distance rules swarming behaviors. In some cases, individual agents are found to be…
One of the key issues in human-robot collaboration is the development of computational models that allow robots to predict and adapt to human behavior. Much progress has been achieved in developing such models, as well as control techniques…
Many swarm robotics tasks consist of multiple conflicting objectives. This research proposes a multi-objective evolutionary neural network approach to developing controllers for swarms of robots. The swarm robot controllers are trained in a…
Neurobiological theories of spatial cognition developed with respect to recording data from relatively small and/or simplistic environments compared to animals' natural habitats. It has been unclear how to extend theoretical models to large…
Unmanned Aerial Vehicles (UAVs) have a great potential to support search tasks in unstructured environments. Small, lightweight, low speed and agile UAVs, such as multi-rotors platforms can incorporate many kinds of sensors that are…
Reinforcement learning (RL) methods have been actively applied in the field of robotics, allowing the system itself to find a solution for a task otherwise requiring a complex decision-making algorithm. In this paper, we present a novel…
We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic…
In this paper, we address the problem of collision avoidance for a swarm of UAVs used for continuous surveillance of an urban environment. Our method, LSwarm, efficiently avoids collisions with static obstacles, dynamic obstacles and other…
Recent breakthroughs in multimodal large language models (MLLMs) have endowed AI systems with unified perception, reasoning and natural-language interaction across text, image and video streams. Meanwhile, Unmanned Aerial Vehicle (UAV)…
Humanoid robots must adapt their contact behavior to diverse objects and tasks, yet most controllers rely on fixed, hand-tuned impedance gains and gripper settings. This paper introduces HumanoidVLM, a vision-language driven retrieval…
There has been a growing interest in extending the capabilities of autonomous underwater vehicles (AUVs) in subsea missions, particularly in integrating underwater human-robot interaction (UHRI) for control. UHRI and its subfield,underwater…
Swarm dynamics is the study of collections of agents that interact with one another without central control. In natural systems, insects, birds, fish and other large mammals function in larger units to increase the overall fitness of the…
Rapid advancements in Artificial Intelligence have shifted the focus from traditional human-directed robots to fully autonomous ones that do not require explicit human control. These are commonly referred to as Human-on-the-Loop (HotL)…
Dynamical systems models for controlling multi-agent swarms have demonstrated advances toward resilient, decentralized navigation algorithms. We previously introduced the NeuroSwarms controller, in which agent-based interactions were…
Human-robot teaming offers great potential because of the opportunities to combine strengths of heterogeneous agents. However, one of the critical challenges in realizing an effective human-robot team is efficient information exchange -…
We present a method for controlling a swarm using its spectral decomposition -- that is, by describing the set of trajectories of a swarm in terms of a spatial distribution throughout the operational domain -- guaranteeing scale invariance…
Swarm intelligence is the collective behavior emerging in systems with locally interacting components. Because of their self-organization capabilities, swarm-based systems show essential properties for handling real-world problems such as…
Collective decision-making is a key function of autonomous robot swarms, enabling them to reach a consensus on actions based on environmental features. Existing strategies require the participation of all robots in the decision-making…