Object tracking is a fundamental ability for a robot; manipulation as well as activity recognition relies on the robot being able to follow objects in the scene. This paper presents a tracker that adapts to changes in object appearance and is able to re-discover an object that was lost. At its core is a keypoint-based method that exploits the rigidity assumption: pairs of keypoints maintain the same relations over similarity transforms. Using a structured approach to learning, it is able to incorporate new appearances in its model for increased robustness. We show through quantitative and qualitative experiments the benefits of the proposed approach compared to the state of the art, even for objects that do not strictly follow the rigidity assumption.

This thesis builds on the observation that robots cannot be programmed to handle any possible situation in the world. Like humans, they need mechanisms to deal with previously unseen situations and unknown objects. One of the skills humans rely on to deal with the unknown is the ability to learn by observing others. This thesis addresses the challenge of enabling a robot to learn from a human instructor. In particular, it is focused on objects. How can a robot find previously unseen objects? How can it track the object with its gaze? How can the object be employed in activities? Throughout this thesis, these questions are addressed with the end goal of allowing a robot to observe a human instructor and learn how to perform an activity. The robot is assumed to know very little about the world and it is supposed to discover objects autonomously. Given a visual input, object hypotheses are formulated by leveraging on common contextual knowledge often used by humans (e.g. gravity, compactness, convexity). Moreover, unknown objects are tracked and their appearance is updated over time since only a small fraction of the object is visible from the robot initially. Finally, object functionality is inferred by looking how the human instructor is manipulating objects and how objects are used in relation to others. All the methods included in this thesis have been evaluated on datasets that are publicly available or that we collected, showing the importance of these learning abilities.

Knowledge of the physical properties of objects is essential in a wide range of robotic manipulation scenarios. A robot may not always be aware of such properties prior to interaction. If an object is incorrectly assumed to be rigid, it may exhibit unpredictable behavior when grasped. In this paper, we use vision based observation of the behavior of an object a robot is interacting with and use it as the basis for estimation of its elastic deformability. This is estimated in a local region around the interaction point using a physics simulator. We use optical flow to estimate the parameters of a position-based dynamics simulation using meshless shape matching (MSM). MSM has been widely used in computer graphics due to its computational efficiency, which is also important for closed-loop control in robotics. In a controlled experiment we demonstrate that our method can qualitatively estimate the physical properties of objects with different degrees of deformability.

Visual tracking of unknown objects is an essential task in robotic perception, of importance to a wide range of applications. In the general scenario, the robot has no full 3D model of the object beforehand, just the partial view of the object visible in the first video frame. A tracker with this information only will inevitably lose track of the object after occlusions or large out-of-plane rotations. The way to overcome this is to incrementally learn the appearances of new views of the object. However, this bootstrapping approach is sensitive to drifting due to occasional inclusion of the background into the model. In this paper we propose a method that exploits 3D point coherence between views to overcome the risk of learning the background, by only learning the appearances at the faces of an inscribed cuboid. This is closely related to the popular idea of 2D object tracking using bounding boxes, with the additional benefit of recovering the full 3D pose of the object as well as learning its full appearance from all viewpoints. We show quantitatively that the use of an inscribed cuboid to guide the learning leads to significantly more robust tracking than with other state-of-the-art methods. We show that our tracker is able to cope with 360 degree out-of-plane rotation, large occlusion and fast motion.

Humans are able to merge information from multiple perceptional modalities and formulate a coherent representation of the world. Our thesis is that robots need to do the same in order to operate robustly and autonomously in an unstructured environment. It has also been shown in several fields that multiple sources of information can complement each other, overcoming the limitations of a single perceptual modality. Hence, in this paper we introduce a data set of actions that includes both visual data (RGB-D video and 6DOF object pose estimation) and acoustic data. We also propose a method for recognizing and segmenting actions from continuous audio-visual data. The proposed method is employed for extensive evaluation of the descriptive power of the two modalities, and we discuss how they can be used jointly to infer a coherent interpretation of the recorded action.

In this paper we describe a probabilistic framework that models the interaction between multiple objects in a scene.We present a spatio-temporal feature encoding pairwise interactions between each object in the scene. By the use of a kernel representation we embed object interactions in a vector space which allows us to define a metric comparing interactions of different temporal extent. Using this metric we define a probabilistic model which allows us to represent and extract the affordances of individual objects based on the structure of their interaction. In this paper we focus on the presented pairwise relationships but the model can naturally be extended to incorporate additional cues related to a single object or multiple objects. We compare our approach with traditional kernel approaches and show a significant improvement.

In order for robots to function in unstructured environments in interaction with humans, they must be able to reason about the world in a semantic meaningful way. An essential capability is to segment the world into semantic plausible object hypotheses. In this paper we propose a general framework which can be used for reasoning about objects and their functionality in manipulation activities. Our system employs a hierarchical segmentation framework that extracts object hypotheses from RGB-D video. Motivated by cognitive studies on humans, our work leverages on contextual information, e.g., that objects obey the laws of physics, to formulate object hypotheses from regions in a mathematically principled manner.

The ability to learn from human demonstration is essential for robots in human environments. The activity models that the robot builds from observation must take both the human motion and the objects involved into account. Object models designed for this purpose should reflect the role of the object in the activity - its function, or affordances. The main contribution of this paper is to represent object directly in terms of their interaction with human hands, rather than in terms of appearance. This enables the direct representation of object affordances/function, while being robust to intra-class differences in appearance. Object hypotheses are first extracted from a video sequence as tracks of associated image segments. The object hypotheses are encoded as strings, where the vocabulary corresponds to different types of interaction with human hands. The similarity between two such object descriptors can be measured using a string kernel. Experiments show these functional descriptors to capture differences and similarities in object affordances/function that are not represented by appearance.