Adaptive collaborative control is a decision-making approach that enables humans and robots to work together as partners rather than in traditional master-slave relationships. Unlike conventional robotic systems where humans directly control every action, adaptive collaborative control allows autonomous agents (robots) and human operators to collaborate as peers, sharing decision-making responsibilities to accomplish common goals.
This methodology is implemented through hybrid computational models that combine finite-state machines with functional models as subcomponents. These models simulate the behavior of multi-agent systems where both human and robotic participants contribute to task execution and work product development. The approach represents a fundamental shift from traditional control theory applications in teleoperation, moving away from the paradigm of "humans as controllers/robots as tools" toward genuine human-robot collaboration.
The concept of "collaborative control" was first developed in the late 1990s and early 2000s by Fong, Thorpe, and Baur (1999). According to Fong et al., robots operating under collaborative control must possess three essential characteristics: they must be self-reliant (capable of independent operation), aware (able to perceive and understand their environment and situation), and adaptive (able to modify their behavior based on changing conditions). The "adaptive" qualifier, while not always explicitly stated in literature, is considered fundamental to the official definition of collaborative control.
Early implementations focused primarily on vehicle teleoperation, where the approach demonstrated its potential to enhance remote control operations. Modern applications have expanded significantly to include training systems, analytical tools, and engineering applications across various domains: teleoperations involving humans and multiple robots, multi-robot collaborative systems, unmanned vehicle control, and fault-tolerant controller design.
Operational framework Like traditional control methodologies, adaptive collaborative control processes system inputs and regulates outputs according to predefined rules. However, it differs fundamentally in how these rules are applied. The constraints operate primarily at the strategic level, governing high-level goals and tasks as defined by human operators. Lower-level tactical decisions remain adaptive and flexible, accommodating varying degrees of autonomy, interaction patterns, and capabilities of different agents (both human and robotic).
When uncertainty arises in task execution that could impact the overarching strategy, models operating under this methodology can query external sources for guidance. This interaction may generate alternative courses of action if they provide greater certainty in supporting the strategic objectives. If no suitable alternative emerges or if there is no response from queried sources, the model continues with its original planned actions.
Implementation considerations Several critical factors must be addressed when implementing adaptive collaborative control in simulation environments. The fundamental requirement for data fusion arises from the need to integrate information from multiple collaborating agents. This often necessitates establishing prioritization schemes to manage continuous streams of recommendations and data inputs.
The degree of autonomy granted to robotic agents in human–robot interaction scenarios and the appropriate weighting of decisional authority in robot-robot interactions are crucial architectural considerations. Interface design represents a vital human systems integration challenge, as varied human interpretational frameworks require careful attention to ensure that robots accurately convey their intentions and status when interacting with human partners.
History The history of adaptive collaborative control began in 1999 through the efforts of Terrence Fong and Charles Thorpe of Carnegie Mellon University and Charles Baur of École Polytechnique Fédérale de Lausanne. Fong et al. believed existing telerobotic practices, which centered on a human point of view, while sufficient for some domains were sub-optimal for operating multiple vehicles or controlling planetary rovers. The new approach devised by Fong et al. focused on a robot-centric teleoperation model that treated the human as a peer and made requests to them in the manner a person would seek advice from experts.
In the nominal work, Fong et al. implemented collaborative control design using a PioneerAT mobile robot and a UNIX workstation with wireless communications and distributed message-based computing. Two years later, Fong utilized collaborative control for several more applications, including the collaboration of a single human operator with multiple mobile robots for surveillance and reconnaissance. Around this same time, Goldberg and Chen presented an adaptive collaborative control system that possessed malfunctioning sources. The control design proved to create a model that maintained a robust performance when subjected to a sizeable fraction of malfunctioning sources. In the work, Goldberg and Chen expanded on the definition of collaborative control to include multiple sensors and multiple control processes in addition to human operators as sources. A collaborative, cognitive workspace in the form of a three-dimensional representation developed by Idaho National Laboratory to support understanding of tasks and environments for human operators expounds on Fong's seminal work which used textual dialogue as the human-robot interaction.
The success of the 3-D display provided evidence of the use of mental models for increased team success. During that same time, Fong et al. developed a three dimensional display that was formed via a fusion of sensor data. A recent adaptation of adaptive collaborative control in 2010 was used to design a fault tolerant control system using a Lyapunov function based analysis.
Initialization The simuland for adaptive collaborative control centers on robotics. As such, adaptive collaborative control follows the tenets of control theory applied to robotics at its basest level. That means the states of the robot are observed at a given instant and noted if it is within some accepted bound. If it is not, the estimated states of the robot are calculated using equations of dynamics and kinematics at some future time. The process of entering observation data into the model to generate initial conditions is called initialization. The process of initialization for adaptive collaborative control occurs differently depending on the environment: robotics only and human-robotic interaction. Under a robotics only environment, initialization occurs very similarly to the description above. The robotics, systems, subsystems, non-human entities observe some state it finds not in accordance with the higher-level strategy. The entities that are aware of this error use the appropriate equations to present a revised value for a future time step to its peers. For human-robotic interactions, initialization can occur at two different levels. The first level is what was previously described. In this instance, the robot notices some anomaly in its states that is not wholly consistent or is problematic with its higher-level strategy. It queries the human seeking advice to regulate its dilemma. In the other case, the human feels cause to either query some aspect of the robot's state (e.g. health, trajectory, speed) or present advice to the robot that is challenged against the robot's existing tactical approach to the higher-level strategy. The main inputs for adaptive collaborative control are a human-initiated dialogue based command or value presented by either a human or robotic element. The inputs used in the system models serve as the starting point for the collaboration.
A number of ways are available to gather observational data for use in functional models. The easiest method to gather observational data is simple human observation of the robotic system. Self-monitoring attributes such as built-in test (BIT) can provide regular reports on important system characteristics. A common approach to gather observations is to employ sensors throughout the robotic system. Vehicles operating in teleoperations have speedometers to indicate how fast they travel. Robotic systems with either stochastic or cyclic motion often employ accelerometers to note the forces exerted. GPS sensors provide a standardized data type that is used nearly universally for depicting location. Multi-sensor systems have been used to gather heterogeneous observational data for applications in path planning.
Computation
Adaptive collaborative control is most accurately modeled as a closed loop feedback control system. Closed loop feedback control describes the event where the outputs of a system from an input are used to influence the present or future behavior of the system. The feedback control model is governed by a set of equations that are used to predict the future state of the simuland and regulate its behavior. These equations – in conjunction with principles of control theory – are used to evolve physical operations of the simuland to include, but not limited to: dialogue, path planning, motion, monitoring, and lifting objects over time. Many times, these equations are modeled as nonlinear partial differential equations over a continuous time domain.
Due to their complexity, powerful computers are necessary to implement these models. A consequence of using computers to simulate these models is that continuous systems cannot be fully calculated. Instead, numerical solutions, such as the Runge–Kutta methods, are utilized to approximate these continuous models.
These equations are initialized from the response of one or more sources and rates of changes and outputs are calculated. These rates of changes predict the states of the simuland a short time in the future. The time increment for this prediction is called a time step. These new states are applied to the model to determine the new rates of changes and observational data. This behavior is continued until the desired number of iterations is completed. In the event a future state violates or comes within a tolerance of the violation the simuland will confer with its human counterpart seeking advice on how to proceed from that point. The outputs, or observational data, are used by the human operators to determine what they believe is the best course of action for the simuland. Their commands are fed with the input into the control system and assessed regarding its effectiveness in resolving the issues. If the human commands are determined to be valuable, the simuland will adjust its control input to what the human suggested. If the human's commands are determined to be unbeneficial, malicious, or non-existent, the model will seek its own correction approach.
Domain and Codomain