Bin-packing is an important problem in the robotic warehouse domain. Traditionally, this problem has been studied only for rigid packages (e.g., boxes or rigid objects). In this work, we tackle the problem of bin-packing with deformable packages that have become a popular choice for fulfillment needs. We present a system that incorporates a dual robot arm bimanual setup, uniquely combining suction and sweeping motions to stably and reliably pack deformable packages in a bin. Additionally, we propose a comprehensive action prediction framework to optimize for bin-packing efficiency by predicting optimal actions for both robots involved. Our methodology leverages a two-pronged learning strategy, where initially, we train a model in a self-supervised manner to predict a scoring metric indicative of bin-packing efficiency and then leverage an online optimization scheme to compute optimal actions in real time. The model is pre-trained in simulation in MuJoCo and fine-tuned on small-scale data from a real-world laboratory setting. Our packing score prediction model predicts bin-packing score ∈ [0, 1] with an MSE of 0.003. Real-world experiments validate our method’s adaptability to novel scenarios and its effectiveness in packing operations. Project Website: https://sites.google.com/usc.edu/bimanual-binpacking/

Suction cups are popular for picking and transporting packages in warehouse applications. To maximize throughput, high transport speeds are desired. Many packages are deformable and may detach from the suction cups due to inertial loading if trajectories use excessive velocities. This paper introduces a novel methodology that analyzes package deformation through its curvature at the package-suction cup contact interface to generate a Factor-of-Safety (FOS) score for each waypoint in a given trajectory. By maintaining the FOS above a predetermined threshold, the trajectory planner is able to generate transport trajectories that are both safe and time-optimized. Experimental results show the method’s efficacy, demonstrating a 21.92% reduction in transport times compared to a conservative trajectory generation. Our FOS predictor identified trajectories that ensured safe package transport with 100% accuracy across all 627 real-world experiments.

We propose a new formulation for the multi-robot task planning and allocation problem that incorporates (a) precedence relationships between tasks; (b) coordination for tasks allowing multiple robots to achieve increased efficiency; and (c) cooperation through the formation of robot coalitions for tasks that cannot be performed by individual robots alone. In our formulation, the tasks and the relationships between the tasks are specified by a task graph. We define a set of reward functions over the task graph's nodes and edges. These functions model the effect of robot coalition size on task performance while incorporating the influence of one task's performance on a dependent task. Solving this problem optimally is NP-hard. However, using the task graph formulation allows us to leverage min-cost network flow approaches to obtain approximate solutions efficiently. Additionally, we explore a mixed integer programming approach, which gives optimal solutions for small instances of the problem but is computationally expensive. We also develop a greedy heuristic algorithm as a baseline. Our modeling and solution approaches result in task plans that leverage task precedence relationships and robot coordination and cooperation to achieve high mission performance, even in large missions with many agents.

Systematically designing local interaction rules to achieve collective behaviors in robot swarms is a challenging endeavor, especially in micro robots, where size restrictions imply severe sensing, communication, and computation limitations. In such robot swarms, performing useful functions is often preconditioned on the formation of high-density aggregations which can facilitate collective signaling and information sharing. In this article, we present a systematic approach to control aggregation behaviors by leveraging the physical interactions in a swarm of 300 3-mm vibration-driven micro bristle robots that we designed and fabricated. We demonstrate the ability to control the degree of aggregation by varying the motility characteristics of the robots through global vibration frequency and amplitude inputs, after comprehensive characterization, modeling, and simulation of the locomotion dynamics and robot interactions. To quantify the degree of aggregation, we also introduce a new metric, the motility-induced phase separation index index, which unlike many existing methods does not require a scenario-specific tuning of parameters. Our investigations reveal how physics-driven interaction mechanisms can be exploited to achieve desired behaviors in minimally equipped robot swarms and highlight the specific ways in which hardware and software developments aid in the achievement of collision-induced aggregations.

This paper develops a decentralized approach to mobile sensor coverage by a multi-robot system. We consider a scenario where a team of robots with limited sensing range must position itself to effectively detect events of interest in a region characterized by areas of varying importance. Towards this end, we develop a decentralized control policy for the robots-realized via a Graph Neural Network-which uses inter-robot communication to leverage non-local information for control decisions. By explicitly sharing information between multi-hop neighbors, the decentralized controller achieves a higher quality of coverage when compared to classical approaches that do not communicate and leverage only local information available to each robot. Simulated experiments demonstrate the efficacy of multi-hop communication for multi-robot coverage and evaluate the scalability and transferability of the learning-based controllers.

In the context of heterogeneous multirobot teams deployed for executing multiple tasks, this article develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Toward this end, the task prioritization and execution—through which the allocation of tasks to robots is effectively realized—are encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations, where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.

For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions. This paper presents an adaptive task allocation and task execution framework which allows individual robots to prioritize among tasks while explicitly taking into account their efficacy at performing the tasks---the parameters of which might be unknown before deployment and/or might vary over time. Such a \emph{specialization} parameter---encoding the effectiveness of a given robot towards a task---is updated on-the-fly, allowing our algorithm to reassign tasks among robots with the aim of executing them. The developed framework requires no explicit model of the changing environment or of the unknown robot capabilities---it only takes into account the progress made by the robots at completing the tasks. Simulations and experiments demonstrate the efficacy of the proposed approach during variations in environmental conditions and when robot capabilities are unknown before deployment.

Executing multiple tasks concurrently is important in many robotic applications. Moreover, the prioritization of tasks is essential in applications where safety-critical tasks need to precede application-related objectives, in order to protect both the robot from its surroundings and vice versa. Furthermore, the possibility of switching the priority of tasks during their execution gives the robotic system the flexibility of changing its objectives over time. In this paper, we present an optimization-based task execution and prioritization framework that lends itself to the case of time-varying priorities as well as variable number of tasks. We introduce the concept of extended set-based tasks, encode them using control barrier functions, and execute them by means of a constrained-optimization problem, which can be efficiently solved in an online fashion. Finally, we show the application of the proposed approach to the case of a redundant robotic manipulator.

In densely-packed robot swarms operating in confined regions, spatial interference-which manifests itself as a competition for physical space-forces robots to spend more time navigating around each other rather than performing the primary task. This paper develops a decentralized algorithm that enables individual robots to decide whether to stay in the region and contribute to the overall mission, or vacate the region so as to reduce the negative effects that interference has on the overall efficiency of the swarm. We develop this algorithm in the context of a distributed collection task, where a team of robots collect and deposit objects from one set of locations to another in a given region. Robots do not communicate and use only binary information regarding the presence of other robots around them to make the decision to stay or retreat. We illustrate the efficacy of the algorithm with experiments on a team of real robots.

In robot swarms operating under highly restrictive sensing and communication constraints, individuals may need to use direct physical proximity to facilitate information exchange. However, in certain task-related scenarios, this requirement might conflict with the need for robots to spread out in the environment, e.g., for distributed sensing or surveillance applications. This paper demonstrates how a swarm of minimally-equipped robots can form high-density robot aggregates that coexist with lower robot densities in space. We envision a scenario where a swarm of vibration-driven robots-which sit atop bristles and achieve directed motion by vibrating them-move randomly in an environment while colliding with each other. Theoretical techniques from the study of far-from-equilibrium collectives and statistical mechanics clarify the mechanisms underlying the formation of these high and low density regions. Specifically, we capitalize on a transformation that connects the collective properties of a system of self-propelled particles with that of a well-studied molecular fluid system, thereby inheriting the rich theory of equilibrium thermodynamics. Real robot experiments as well as simulations illustrate how inter-robot collisions can precipitate the formation of non-uniform robot densities in a closed and bounded region.

In this paper we present a study of a specific class of vibration-driven robots: the brushbots. In a bottom-up fashion, we start by deriving dynamic models of the brushes and we discuss the conditions under which these models can be employed to describe the motion of brushbots. Then, we present two designs of brushbots: a fully-actuated platform and a differential-drive-like one. The former is employed to experimentally validate both the developed theoretical models and the devised motion control algorithms. Finally, a coordinated-control algorithm is implemented on a swarm of differential-drive-like brushbots in order to demonstrate the design simplicity and robustness that can be achieved by employing a vibration-based locomotion strategy.

As the size of robots decreases in multirobot systems, collisions cease to be catastrophic events that need to be avoided at all costs. This implies that less conservative, coordinated control strategies can be employed, where collisions are not only tolerated, but can potentially be harnessed as an information source. In this paper, we follow this line of inquiry by employing collisions as a sensing modality that provides information about the robots' surroundings. We envision a collection of robots moving around with no sensors other than binary, tactile sensors that can determine if a collision occurred, and let the robots use this information to determine their locations. We apply a probabilistic localization technique based on mean-field approximations that allows each robot to maintain and update a probability distribution over all possible locations. Simulations and real multirobot experiments illustrate the feasibility of the proposed approach.

In multi-robot systems where a central decision maker is specifying the movement of each individual robot, a communication failure can severely impair the performance of the system. This paper develops a motion strategy that allows robots to safely handle critical communication failures for such multi-robot architectures. For each robot, the proposed algorithm computes a time horizon over which collisions with other robots are guaranteed not to occur. These safe time horizons are included in the commands being transmitted to the individual robots. In the event of a communication failure, the robots execute the last received velocity commands for the corresponding safe time horizons leading to a provably safe open-loop motion strategy. The resulting algorithm is computationally effective and is agnostic to the task that the robots are performing. The efficacy of the strategy is verified in simulation as well as on a team of differential-drive mobile robots.