Context is an essential capability for robots that are to be as adaptive as possible in challenging environments. Although there are many context modeling efforts, they assume a fixed structure and number of contexts. In this paper, we propose an incremental deep model that extends Restricted Boltzmann Machines. Our model gets one scene at a time, and gradually extends the contextual model when necessary, either by adding a new context or a new context layer to form a hierarchy. We show on a scene classification benchmark that our method converges to a good estimate of the contexts of the scenes, and performs better or on-par on several tasks compared to other incremental models or non-incremental models.
There are several studies in robotics that integrate context into various robot problems, examples including @cite_5 , which used context in determining where to place a new object in the scene; @cite_21 , which modeled local interactions between objects (as context) in determining their labels; and, @cite_6 , which proposed using context in modulating object detections in a scene and planning. Our model differs from all these studies by being incremental and hierarchical.
@cite_5 @cite_21 @cite_6
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Place and object recognition are two fundamental problems for mobile robot to understand its surroundings. In the field of computer vision it has been acknowledged that context plays an important role in image parsing, but in most of the researches contextual information is only used in one direction and little attention is paid to the relative pose context between objects and local features. We observe, however, place and object can serve as context to each other, that is the recognition of one facilitates the recognition of the other. In this paper, a new hierarchical random field which can encode multiple kinds of context including co-occurrence context, temporal context and relative pose context is proposed for simultaneous place and object recognition with a mobile platform. And a new kind of relative pose context, which is scale and rotation invariant, is defined to improve the stability of pose-encoded context. Experimental results with a mobile robot prove that the proposed method significantly improve the precision of the place and object recognition in familiar and unfamiliar environments. We propose a planning method for grasping in cluttered environments, where the robot can make simultaneous contact with multiple objects, manipulating them in a deliberate and controlled fashion. This enables the robot to reach for and grasp the target while simultaneously contacting and moving aside obstacles in order to clear a desired path. We use a physicsbased analysis of pushing to compute the motion of each object in the scene in response to a set of possible robot motions. In order to make the problem computationally tractable, we enable multiple simultaneous robot-object interactions, which we pre-compute and cache, but avoid object-object interactions. Tests on large sets of simulated scenes show that our planner produces more successful grasps in more complex scenes than versions that avoid any interaction with surrounding clutter. Validation on a real robot shows that our grasp evaluation method accurately predicts the outcome of a grasp, and that our approach, in conjunction with state-of-the-art object recognition tools, is applicable in reallife scenes that are highly cluttered and constrained. The paper presents a new model for context dependent interpretation of linguistic expressions about spatial proximity between objects in a natural scene. The paper discusses novel psycholinguistic experimental data that tests and verifies the model. The model has been implemented, and enables a conversational robot to identify objects in a scene through topological spatial relations (e.g. "X near Y"). The model can help motivate the choice between topological and projective prepositions. In a Robot Learning from Demonstration framework involving environments with many objects, one of the key problems is to decide which objects are relevant to a given task. In this paper, we analyze this problem and propose a biologically-inspired computational model that enables the robot to focus on the task-relevant objects. To filter out incompatible task models, we compute a Task Relevance Value (TRV) for each object, which shows a human demonstrator's implicit indication of the relevance to the task. By combining an intentional action representation with ‘motionese’ [2], our model exhibits recognition capabilities compatible with the way that humans demonstrate. We evaluate the system on demonstrations from five different human subjects, showing its ability to correctly focus on the appropriate objects in these demonstrations.