Automating adaptive execution behaviors for robot manipulation

Robotic manipulation in semi-structured and changing environments requires systems with: a) perception and reasoning capabilities able to capture and understand the state of the environment; b) planning and replanning capabilities at both symbolic and geometric levels; c) automatic and robust execution capabilities. To cope with these issues, this paper presents a framework with the following features. First, it uses perception and ontology-based reasoning procedures to obtain the Planning Description Domain Language files that describe the manipulation problem at task level. This is used in the planning stage as well as during task execution in order to adapt to new situations, if required. Second, the proposed framework is able to plan at both task and motion levels, intertwining them by incorporating geometric reasoning modules to determine some of the symbolic predicates needed to describe the states. Finally, the framework automatically generates the behavior trees required to execute the task. The proposal takes advantage of the ability of behavior trees to be edited during run time, allowing adaptation of the action plan or of the trajectories according to changes in the state of the environment. The approach allows for robot manipulation tasks to be automatically planned and robustly executed, contributing to achieve fully functional service robots.Peer ReviewedPostprint (published version

Master of Science

thesisIn this work we consider task-based planning in uncertainty. To make progress in this problem, we propose an end-to-end method that makes progress toward the unification of perception and manipulation. Critical for this unification is the geometric primitive. A geometric primitive is a 3D geometry that can be fit to a single view from a 3D image. Geometric primitives are a consistent structure in many scenes, and by leveraging this, perceptual tasks such as segmentation, localization, and recognition can be solved. Sharing this information between these subroutines also makes the method computationally efficient. Geometric primitives can be used to define a set of actions the robot can use to influence the world. Leveraging the rich 3D information in geometric primitives allows the designer to develop actions with a high chance of success. In this work, we consider a pick-and-place action, parameterized by the object and scene constraints. The design of the perceptual capabilities and actions is independent of the task given to the robot, giving the robot more versatility to complete a range of tasks. With a large number of available actions, the robot needs to select which action the robot performs. We propose a task-specific reward function to determine the next-best action for the robot to complete the task. A key insight into making the action selection tractable is reasoning about the occluded regions of the scene. We propose to not reason about what could be in the occluded regions, but instead to treat the occluded regions as parts of the scene to explore. Defining reward functions that encourage this exploration while balancing trying to solve the given task gives the robot more versatility to perform many different tasks. Reasoning about occlusion in this way also makes actions in the scene more robust to scene uncertainty and increases the computational efficiency of the method overall. In this work, we show results for segmentation of geometric primitives on real data, and discuss problems with fitting their parameters. While positive segmentation results are shown, there are problems with fitting consistent parameters to the geometric primitives. We also present simulation results showing the action selection process solving a singulation task. We show that our method is able to perform this task in several scenes with varying levels of complexity. We compare against selecting actions at random, and show our method consistently takes fewer actions to solve the scene

Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version

Perception, cognition, and action in hyperspaces: implications on brain plasticity, learning, and cognition

We live in a three-dimensional (3D) spatial world; however, our retinas receive a pair of 2D projections of the 3D environment. By using multiple cues, such as disparity, motion parallax, perspective, our brains can construct 3D representations of the world from the 2D projections on our retinas. These 3D representations underlie our 3D perceptions of the world and are mapped into our motor systems to generate accurate sensorimotor behaviors. Three-dimensional perceptual and sensorimotor capabilities emerge during development: the physiology of the growing baby changes hence necessitating an ongoing re-adaptation of the mapping between 3D sensory representations and the motor coordinates. This adaptation continues in adulthood and is quite general to successfully deal with joint-space changes (longer arms due to growth), skull and eye size changes (and still being able of accurate eye movements), etc. A fundamental question is whether our brains are inherently limited to 3D representations of the environment because we are living in a 3D world, or alternatively, our brains may have the inherent capability and plasticity of representing arbitrary dimensions; however, 3D representations emerge from the fact that our development and learning take place in a 3D world. Here, we review research related to inherent capabilities and limitations of brain plasticity in terms of its spatial representations and discuss whether with appropriate training, humans can build perceptual and sensorimotor representations of spatial 4D environments, and how the presence or lack of ability of a solid and direct 4D representation can reveal underlying neural representations of space.Published versio

A conceptual framework for interactive virtual storytelling

This paper presents a framework of an interactive storytelling system. It can integrate five components: management centre, evaluation centre, intelligent virtual agent, intelligent virtual environment, and users, making possible interactive solutions where the communication among these components is conducted in a rational and intelligent way. Environment plays an important role in providing heuristic information for agents through communicating with the management centre. The main idea is based on the principle of heuristic guiding of the behaviour of intelligent agents for guaranteeing the unexpectedness and consistent themes