36 accepted papers
Executing pre-planned paths in multi-agent systems is challenging, as a lack of synchronization can lead to collisions or live-/deadlocks, while enforcing strict synchronization may cause a widespread team delay in reaching goals. An Action Dependency Graph (ADG) solves this problem by identifying a
The Multi-agent Path Finding (MAPF) problem involves finding collision-free paths for a team of agents in a known, static environment, with important applications in warehouse automation, logistics, or last-mile delivery. To meet the needs of these large-scale applications, current learning-based me
Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the current robot belief, learning-based methods show the potential to achieve improved performance by drawing on past experiences
Lifelong Multi-Agent Path Finding (LMAPF) repeatedly finds collision-free paths for multiple agents that are continually assigned new goals when they reach current ones. Recently, this field has embraced learning-based methods, which reactively generate single-step actions based on individual local
In this paper, we introduce HDPlanner, a deep reinforcement learning (DRL) based framework designed to tackle two core and challenging tasks for mobile robots: autonomous exploration and navigation, where the robot must optimize its trajectory adaptively to achieve the task objective through continu
Many multi-robot applications require allocating a team of heterogeneous agents (robots) with different abilities to cooperatively complete a given set of spatially distributed tasks as quickly as possible. We focus on tasks that can only be initiated when all required agents are present otherwise a
Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known environment. Recent work introduced a novel MAPF approach, LNS2, which proposed to repair a quickly-obtainable set of infeasib…
In multi-robot exploration, a team of mobile robot is tasked with efficiently mapping an unknown environments. While most exploration planners assume omnidirectional sensors like LiDAR, this is impractical for small robots such as drones, where lightweight, directional sensors like cameras may be th
The Multi-Agent Path Finding (MAPF) problem aims to determine the shortest and collision-free paths for multiple agents in a known, potentially obstacle-ridden environment. It is the core challenge for robotic deployments in large-scale logistics and transportation. Decentralized learningbased appro
The multi-agent pathfinding (MAPF) problem seeks collision-free paths for a team of agents from their current positions to their pre-set goals in a known environment, and is an essential problem found at the core of many logistics, transportation, and general robotics applications. Existing learning…
Optimal Control for legged robots has gone through a paradigm shift from position-based to torque-based control, owing to the latter’s compliant and robust nature. In parallel to this shift, the community has also turned to Deep Reinforcement Learning (DRL) as a promising approach to directly learn…
In this work, we propose a deep reinforcement learning (DRL) based reactive planner to solve large-scale Lidar-based autonomous robot exploration problems in 2D action space. Our DRL-based planner allows the agent to reactively plan its exploration path by making implicit predictions about unknown a
Many multi-robot deployments, such as automated construction of buildings, distributed search, or cooperative mapping, often require agents to intelligently coordinate their trajectories and form coalition over a large domain, to complete spatially distributed tasks as quickly as possible. We focus…
Efficient and scalable adaptive traffic signal control is crucial in reducing congestion, maximizing through-put, and improving mobility experience in ever-expanding cities. Recent advances in multi-agent reinforcement learning (MARL) with parameter sharing have significantly improved the adaptive o…
Information sharing is critical in time-sensitive and realistic multi-robot exploration, especially for smaller robotic teams in large-scale environments where connectivity may be sparse and intermittent. Existing methods often overlook such communication constraints by assuming unrealistic global c
Information-based coverage directs robots to move over an area to optimize a pre-defined objective function based on some measure of information. Our prior work determined that the spectral decomposition of an information map can be used to guide a set of heterogeneous agents, each with different se…
Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired control frameworks often rely on a singular control p
Unmanned aerial vehicles (UAVs) for contact-based tasks at height can greatly improve the safety of the human workers involved. However, performing contact-based tasks with typical under-actuated UAVs is non-trivial. Due to their coupled translational and rotational dynamics and their limited statio…
In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible. Since the environment is being revealed during exploration, the robot needs to frequently re-plan its path online, as new information is acquired by onboard sensors and…
Trading off performance guarantees in favor of scalability, the Multi-Agent Path Finding (MAPF) community has recently started to embrace Multi-Agent Reinforcement Learning (MARL), where agents learn to collaboratively generate individual, collision-free (but often suboptimal) paths. Scalability is…
This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensing range. To keep each target's position estimate as accurate as possible, the robot needs to adaptively plan its path t…
Multi-agent foraging (MAF) involves distributing a team of agents to search an environment and extract resources from it. Nature provides several examples of highly effective foragers, where individuals within the foraging collective use biological markers (e.g., pheromones) to communicate critical
From insects to larger mammals, legged animals can be seen easily traversing a wide variety of challenging environments, by carefully selecting, reaching, and exploiting high-quality contacts with the terrain. In contrast, existing robotic foothold planning methods remain computationally expensive,
The long-term goal of this work is to enable agents with low-information sensors to perform tasks usually restricted to ones with more sophisticated, high-information sensing capabilities. Our approach is to regulate the motion of these low-information agents to obtain “high-information” results. As…
In this work, we focus on the autonomous navigation of snake robots in densely-cluttered environments, where collisions between the robot and obstacles are frequent, which could happen often in disaster scenarios, underground caves, or grassland/forest environments. This work takes the view that obs…
Multi-agent path finding (MAPF) is an indispensable component of large-scale robot deployments in numerous domains ranging from airport management to warehouse automation. In particular, this work addresses lifelong MAPF (LMAPF) - an online variant of the problem where agents are immediately assigne
Decentralized multi-agent reinforcement learning has been demonstrated to be an effective solution to large multiagent control problems. However, agents typically can only make decisions based on local information, resulting in suboptimal performance in partially-observable settings. The addition of
Recent approaches to multi-agent reinforcement learning (MARL) with inter-agent communication have often overlooked important considerations of real-world communication networks, such as limits on bandwidth. In this paper, we propose an approach to learning sparse discrete communication through back…
Motion planning for mobile robots with many degrees-of-freedom (DoF) is challenging due to their high-dimensional configuration spaces. To manage this curse of dimensionality, this paper proposes a new hierarchical framework that decomposes the system into sub-systems (based on shared capabilities o…
Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world robot deployments, from aerial swarms to warehouse automation. However, despite the community's continued efforts, most state-of-the-art MAPF planners still rely on centralized planning and scale poorly past a
In this paper, we focus on the problem of directing the gaze of a vision system mounted to the body of a high-degree-of-freedom (DOF) legged robot for active perception deployments. In particular, we consider the case where the vision system is rigidly attached to the robot's body (i.e., without any…
Inspired by the locomotor nervous system of vertebrates, central pattern generator (CPG) models can be used to design gaits for articulated robots, such as crawling, swimming or legged robots. Incorporating sensory feedback for gait adaptation in these models can improve the locomotive performance o…
Many quadrupedal animals have lateral degrees of freedom in their backs that assist locomotion. This paper seeks to use a robotic model to demonstrate that back bending assists not only forward motion, but also lateral and turning motions. This paper uses geometric mechanics to prescribe gaits that…
Decentralized control architectures, such as those conventionally defined by central pattern generators, independently coordinate spatially distributed portions of articulated bodies to achieve system-level objectives. State of the art distributed algorithms for reinforcement learning employ a diffe…
Inspired by the ability of animals to rely on proprioception and vestibular feedback to adapt their gait, we propose a modular framework for autonomous locomotion that relies on force sensing and inertial information. A first controller exploits anti-compliance, a new application of positive force f…
We present a distributed control strategy that enables a swarm of autonomous surface vehicles (ASVs) to cooperatively grasp and manipulate a large floating object from an initial position and orientation to a desired final position and orientation. Given an initial set of random robot positions, our