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Kant and the Philosophy of Mind: Perception, Reason, and the Self
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Just today I was talking with another friend of mine who, I just found out, also happens to be following your blog. So there are epistemic requirements upon the very possibility of empirical belief. The crucial epistemological role of experience lies in its essential contribution to the subject's understanding of certain perceptual demonstrative contents, simply grasping which provides him with a reason to endorse them in belief. Forgot password?
Don't have an account? All Rights Reserved. OSO version 0. University Press Scholarship Online. Sign in. Not registered? Data can come from a single or multiple sensors, usually mounted onboard the robot, but can also come from the infrastructure or from another robot e. In multiple-sensors perception, either the same modality or multimodal, an efficient approach is usually necessary to combine and process data from the sensors before an ML method can be employed. Data alignment and calibration steps are necessary depending on the nature of the problem and the type of sensors used.
This semantic mapping process uses ML at various levels, e. However, in the majority of applications, the primary role of environment mapping is to model data from exteroceptive sensors, mounted onboard the robot, in order to enable reasoning and inference regarding the real-world environment where the robot operates. Robot perception functions, like localization and navigation, are dependent on the environment where the robot operates. Essentially, a robot is designed to operate in two categories of environments: indoors or outdoors.
Therefore, different assumptions can be incorporated in the mapping representation and perception systems considering indoor or outdoor environments. Moreover, the sensors used are different depending on the environment, and therefore, the sensory data to be processed by a perception system will not be the same for indoors and outdoors scenarios. An example to clarify the differences and challenges between a mobile robot navigating in an indoor versus outdoor environment is the ground, or terrain, where the robot operates.
Most of indoor robots assume that the ground is regular and flat which, in some manner, facilitates the environment representation models; on the other hand, for field outdoors robots, the terrain is quite often far from being regular and, as consequence, the environment modeling is itself a challenge and, without a proper representation, the subsequent perception tasks are negatively affected.
"Perception and Reason in Ancient Stoicism"
Moreover, in outdoors, robotic perception has to deal with weather conditions and variations in light intensities and spectra. Moreover, one of the participating teams from benchmarked a pose estimation method on a warehouse logistics dataset, and found large variations in performance depending on clutter level and object type [ 2 ].
Thus, perception systems currently require expert knowledge in order to select, adapt, extend, and fine-tune the various employed components. Apart from the increased training data sizes and robustness, the end-to-end training aspect of deep-learning DL approaches made the development of perception systems easier and more accessible for newcomers, as one can obtain the desired results directly from raw data in many cases, by providing a large number of training examples. The method selection often boils down to obtaining the latest pretrained network from an online repository and fine-tuning it to the problem at hand, hiding all the traditional feature detection, description, filtering, matching, optimization steps behind a relatively unified framework.
Unfortunately, at the moment an off-the-shelf DL solution for every problem does not exist, or at least no usable pretrained network, making the need for huge amounts of training data apparent. However, the danger is to overfit to such benchmarks, as the deployment environment of mobile robots is almost sure to differ from the one used in teaching the robot to perceive and understand the surrounding environment.
Thus, the suggestions formulated by Wagstaff [ 19 ] still hold true today and should be taken to heart by researchers and practitioners. Therefore, perception is a very important part of a complex, embodied, active, and goal-driven robotic system. Among the numerous approaches used in environment representation for mobile robotics, and for autonomous robotic-vehicles, the most influential approach is the occupancy grid mapping [ 20 ]. This 2D mapping is still used in many mobile platforms due to its efficiency, probabilistic framework, and fast implementation.
Although many approaches use 2D-based representations to model the real world, presently 2. The main reasons for using higher dimensional representations are essentially twofold: 1 robots are demanded to navigate and make decisions in higher complex environments where 2D representations are insufficient; 2 current 3D sensor technologies are affordable and reliable, and therefore 3D environment representations became attainable.
The advent and proliferation of RGBD sensors has enabled the construction of larger and ever-more detailed 3D maps. In addition, considerable effort has been made in the semantic labeling of these maps, at pixel and voxels levels. Most of the relevant approaches can be split into two main trends: methods designed for online and those designed for offline use. Online methods process data as it is being acquired by the mobile robot, and generate a semantic map incrementally.
These methods are usually coupled with a SLAM framework, which ensures the geometric consistency of the map.
Building maps of the environment is a crucial part of any robotic system and arguably one of the most researched areas in robotics. Early work coupled mapping with localization as part of the simultaneous localization and mapping SLAM problem [ 22 , 23 ]. More recent work has focused on dealing with or incorporating time-dependencies short or long term into the underlying structure, using either grid maps as described in [ 8 , 24 ], pose-graph representations in [ 25 ], and normal distribution transform NDT [ 16 , 26 ].
A number of semantic mapping approaches are designed to operate offline, taking as input a complete map of the environment. However, the main limitation in [ 34 ] is that the approach requires knowledge of the positions from which the environment was scanned when the input data were collected. Processing sensory data and storing it in a representation of the environment i. The approaches covered range from metric representations 2D or 3D to higher semantic or topological maps, and all serve specific purposes key to the successful operation of a mobile robot, such as localization, navigation, object detection, manipulation, etc.
Moreover, the ability to construct a geometrically accurate map further annotated with semantic information also can be used in other applications such as building management or architecture, or can be further fed back into a robotic system, increasing the awareness of its surroundings and thus improving its ability to perform certain tasks in human-populated environments e. Once a robot is self localized, it can proceed with the execution of its task.
In the case of autonomous mobile manipulators, this involves localizing the objects of interest in the operating environment and grasping them. In a typical setup, the robot navigates to the region of interest, observes the current scene to build a 3D map for collision-free grasp planning and for localizing target objects. The target could be a table or container where something has to be put down, or an object to be picked up.
Bill Brewer, Perception and Reason - PhilPapers
Especially in the latter case, estimating all 6 degrees of freedom of an object is necessary. Subsequently, a motion and a grasp are computed and executed. There are cases where a tighter integration of perception and manipulation is required, e. However, in every application, there is a potential improvement for treating perception and manipulation together.
Perception and manipulation are complementary ways to understand and interact with the environment and according to the common coding theory, as developed and presented by Sperry [ 35 ], they are also inextricably linked in the brain. The argument for embodied learning and grounding of new information evolved, considering the works of Steels and Brooks [ 38 ] and Vernon [ 39 ], and more recently in [ 40 ], robot perception involves planning and interactive segmentation.