When reasoning about actions, e.g., by means of task planning or agent
programming with Golog, the robot’s actions are typically modeled on an
abstract level, where complex actions such as picking up an object are treated
as atomic primitives with deterministic effects and preconditions that only
depend on the current state. However, when executing such an action on a robot
it can no longer be seen as a primitive. Instead, action execution is a complex
task involving multiple steps with additional temporal preconditions and timing
constraints. Furthermore, the action may be noisy, e.g., producing erroneous
sensing results and not always having the desired effects. While these aspects
are typically ignored in reasoning tasks, they need to be dealt with during
execution. In this thesis, we propose several approaches towards closing this
gap.
The Multi-disciplinary Nature of Reasoning and Execution in Robotics
When it comes to reasoning about actions in robotics, there is often a disconnect between the abstract models used for planning and programming and the actual execution of those actions. This gap becomes particularly evident when considering complex actions such as picking up an object.
In traditional models, these complex actions are treated as atomic primitives with deterministic effects and preconditions that only depend on the current state. However, this simplistic view does not hold up when it comes to the real-world execution of these actions on a robot.
The Complexity of Action Execution
Executing an action on a robot is a complex task that involves multiple steps, each with its own set of temporal preconditions and timing constraints. Unlike in the abstract models, where actions are assumed to have perfect execution, in reality, there can be noise and uncertainty present.
For example, the robot’s sensors may produce erroneous results, leading to inaccurate perception of the environment. Similarly, the actions performed by the robot may not always have the desired effects due to hardware limitations or external factors. These aspects cannot be ignored during execution and must be addressed in order to achieve successful task completion.
A Multi-disciplinary Approach
To address the gap between reasoning and execution in robotics, a multi-disciplinary approach is needed. This approach combines expertise from various fields such as artificial intelligence, robotics, control theory, and cognitive science.
One possible solution is to integrate feedback mechanisms into the action execution process. By continuously monitoring the robot’s actions and the environment, it becomes possible to detect and correct errors in real-time. This requires techniques from control theory to ensure stability and robustness.
Furthermore, machine learning techniques can be used to improve the performance of robots in executing actions. By analyzing past execution data, the robot can learn from experience and adapt its actions to better handle noisy and uncertain conditions.
Closing the Gap
The proposed approaches aim to bridge the gap between reasoning and execution in robotics. By taking into account the complexity of action execution and incorporating feedback mechanisms and machine learning, robots can become more capable and reliable in performing real-world tasks.
However, there are still many challenges to overcome. Ensuring the safety of the robot and its interactions with humans and the environment is a critical consideration. Additionally, the integration of these different disciplines requires careful planning and coordination.
Nonetheless, the potential benefits of closing the gap between reasoning and execution are enormous. It can lead to more intelligent, efficient, and adaptable robotic systems that are capable of performing complex tasks in a wide range of real-world scenarios.