Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes

We present the Semantic Robot Programming (SRP) paradigm as a convergence of robot programming by demonstration and semantic mapping. In SRP, a user can directly program a robot manipulator by demonstrating a snapshot of their intended goal scene in workspace. The robot then parses this goal as a scene graph comprised of object poses and inter-object relations, assuming known object geometries. Task and motion planning is then used to realize the user's goal from an arbitrary initial scene configuration. Even when faced with different initial scene configurations, SRP enables the robot to seamlessly adapt to reach the user's demonstrated goal. For scene perception, we propose the Discriminatively-Informed Generative Estimation of Scenes and Transforms (DIGEST) method to infer the initial and goal states of the world from RGBD images. The efficacy of SRP with DIGEST perception is demonstrated for the task of tray-setting with a Michigan Progress Fetch robot. Scene perception and task execution are evaluated with a public household occlusion dataset and our cluttered scene dataset.Comment: published in ICRA 201

Robust Scene Estimation for Goal-directed Robotic Manipulation in Unstructured Environments

To make autonomous robots "taskable" so that they function properly and interact fluently with human partners, they must be able to perceive and understand the semantic aspects of their environments. More specifically, they must know what objects exist and where they are in the unstructured human world. Progresses in robot perception, especially in deep learning, have greatly improved for detecting and localizing objects. However, it still remains a challenge for robots to perform a highly reliable scene estimation in unstructured environments that is determined by robustness, adaptability and scale. In this dissertation, we address the scene estimation problem under uncertainty, especially in unstructured environments. We enable robots to build a reliable object-oriented representation that describes objects present in the environment, as well as inter-object spatial relations. Specifically, we focus on addressing following challenges for reliable scene estimation: 1) robust perception under uncertainty results from noisy sensors, objects in clutter and perceptual aliasing, 2) adaptable perception in adverse conditions by combined deep learning and probabilistic generative methods, 3) scalable perception as the number of objects grows and the structure of objects becomes more complex (e.g. objects in dense clutter). Towards realizing robust perception, our objective is to ground raw sensor observations into scene states while dealing with uncertainty from sensor measurements and actuator control . Scene states are represented as scene graphs, where scene graphs denote parameterized axiomatic statements that assert relationships between objects and their poses. To deal with the uncertainty, we present a pure generative approach, Axiomatic Scene Estimation (AxScEs). AxScEs estimates a probabilistic distribution across plausible scene graph hypotheses describing the configuration of objects. By maintaining a diverse set of possible states, the proposed approach demonstrates the robustness to the local minimum in the scene graph state space and effectiveness for manipulation-quality perception based on edit distance on scene graphs. To scale up to more unstructured scenarios and be adaptable to adversarial scenarios, we present Sequential Scene Understanding and Manipulation (SUM), which estimates the scene as a collection of objects in cluttered environments. SUM is a two-stage method that leverages the accuracy and efficiency from convolutional neural networks (CNNs) with probabilistic inference methods. Despite the strength from CNNs, they are opaque in understanding how the decisions are made and fragile for generalizing beyond overfit training samples in adverse conditions (e.g., changes in illumination). The probabilistic generative method complements these weaknesses and provides an avenue for adaptable perception. To scale up to densely cluttered environments where objects are physically touching with severe occlusions, we present GeoFusion, which fuses noisy observations from multiple frames by exploring geometric consistency at object level. Geometric consistency characterizes geometric compatibility between objects and geometric similarity between observations and objects. It reasons about geometry at the object-level, offering a fast and reliable way to be robust to semantic perceptual aliasing. The proposed approach demonstrates greater robustness and accuracy than the state-of-the-art pose estimation approach.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163060/1/zsui_1.pd

Efficient Belief Propagation for Perception and Manipulation in Clutter

Autonomous service robots are required to perform tasks in common human indoor environments. To achieve goals associated with these tasks, the robot should continually perceive, reason its environment, and plan to manipulate objects, which we term as goal-directed manipulation. Perception remains the most challenging aspect of all stages, as common indoor environments typically pose problems in recognizing objects under inherent occlusions with physical interactions among themselves. Despite recent progress in the field of robot perception, accommodating perceptual uncertainty due to partial observations remains challenging and needs to be addressed to achieve the desired autonomy. In this dissertation, we address the problem of perception under uncertainty for robot manipulation in cluttered environments using generative inference methods. Specifically, we aim to enable robots to perceive partially observable environments by maintaining an approximate probability distribution as a belief over possible scene hypotheses. This belief representation captures uncertainty resulting from inter-object occlusions and physical interactions, which are inherently present in clutterred indoor environments. The research efforts presented in this thesis are towards developing appropriate state representations and inference techniques to generate and maintain such belief over contextually plausible scene states. We focus on providing the following features to generative inference while addressing the challenges due to occlusions: 1) generating and maintaining plausible scene hypotheses, 2) reducing the inference search space that typically grows exponentially with respect to the number of objects in a scene, 3) preserving scene hypotheses over continual observations. To generate and maintain plausible scene hypotheses, we propose physics informed scene estimation methods that combine a Newtonian physics engine within a particle based generative inference framework. The proposed variants of our method with and without a Monte Carlo step showed promising results on generating and maintaining plausible hypotheses under complete occlusions. We show that estimating such scenarios would not be possible by the commonly adopted 3D registration methods without the notion of a physical context that our method provides. To scale up the context informed inference to accommodate a larger number of objects, we describe a factorization of scene state into object and object-parts to perform collaborative particle-based inference. This resulted in the Pull Message Passing for Nonparametric Belief Propagation (PMPNBP) algorithm that caters to the demands of the high-dimensional multimodal nature of cluttered scenes while being computationally tractable. We demonstrate that PMPNBP is orders of magnitude faster than the state-of-the-art Nonparametric Belief Propagation method. Additionally, we show that PMPNBP successfully estimates poses of articulated objects under various simulated occlusion scenarios. To extend our PMPNBP algorithm for tracking object states over continuous observations, we explore ways to propose and preserve hypotheses effectively over time. This resulted in an augmentation-selection method, where hypotheses are drawn from various proposals followed by the selection of a subset using PMPNBP that explained the current state of the objects. We discuss and analyze our augmentation-selection method with its counterparts in belief propagation literature. Furthermore, we develop an inference pipeline for pose estimation and tracking of articulated objects in clutter. In this pipeline, the message passing module with the augmentation-selection method is informed by segmentation heatmaps from a trained neural network. In our experiments, we show that our proposed pipeline can effectively maintain belief and track articulated objects over a sequence of observations under occlusion.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163159/1/kdesingh_1.pd

Semantic Robot Programming for Taskable Goal-Directed Manipulation

Autonomous robots have the potential to assist people to be more productive in factories, homes, hospitals, and similar environments. Unlike traditional industrial robots that are pre-programmed for particular tasks in controlled environments, modern autonomous robots should be able to perform arbitrary user-desired tasks. Thus, it is beneficial to provide pathways to enable users to program an arbitrary robot to perform an arbitrary task in an arbitrary world. Advances in robot Programming by Demonstration (PbD) has made it possible for end-users to program robot behavior for performing desired tasks through demonstrations. However, it still remains a challenge for users to program robot behavior in a generalizable, performant, scalable, and intuitive manner. In this dissertation, we address the problem of robot programming by demonstration in a declarative manner by introducing the concept of Semantic Robot Programming (SRP). In SRP, we focus on addressing the following challenges for robot PbD: 1) generalization across robots, tasks, and worlds, 2) robustness under partial observations of cluttered scenes, 3) efficiency in task performance as the workspace scales up, and 4) feasibly intuitive modalities of interaction for end-users to demonstrate tasks to robots. Through SRP, our objective is to enable an end-user to intuitively program a mobile manipulator by providing a workspace demonstration of the desired goal scene. We use a scene graph to semantically represent conditions on the current and goal states of the world. To estimate the scene graph given raw sensor observations, we bring together discriminative object detection and generative state estimation for the inference of object classes and poses. The proposed scene estimation method outperformed the state of the art in cluttered scenes. With SRP, we successfully enabled users to program a Fetch robot to set up a kitchen tray on a cluttered tabletop in 10 different start and goal settings. In order to scale up SRP from tabletop to large scale, we propose Contextual-Temporal Mapping (CT-Map) for semantic mapping of large scale scenes given streaming sensor observations. We model the semantic mapping problem via a Conditional Random Field (CRF), which accounts for spatial dependencies between objects. Over time, object poses and inter-object spatial relations can vary due to human activities. To deal with such dynamics, CT-Map maintains the belief over object classes and poses across an observed environment. We present CT-Map semantically mapping cluttered rooms with robustness to perceptual ambiguities, demonstrating higher accuracy on object detection and 6 DoF pose estimation compared to state-of-the-art neural network-based object detector and commonly adopted 3D registration methods. Towards SRP at the building scale, we explore notions of Generalized Object Permanence (GOP) for robots to search for objects efficiently. We state the GOP problem as the prediction of where an object can be located when it is not being directly observed by a robot. We model object permanence via a factor graph inference model, with factors representing long-term memory, short-term memory, and common sense knowledge over inter-object spatial relations. We propose the Semantic Linking Maps (SLiM) model to maintain the belief over object locations while accounting for object permanence through a CRF. Based on the belief maintained by SLiM, we present a hybrid object search strategy that enables the Fetch robot to actively search for objects on a large scale, with a higher search success rate and less search time compared to state-of-the-art search methods.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155073/1/zengzhen_1.pd

The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming

Robotic manipulation of multiple objects as a POMDP

This paper investigates manipulation of multiple unknown objects in a crowded environment. Because of incomplete knowledge due to unknown objects and occlusions in visual observations, object observations are imperfect and action success is uncertain, making planning challenging. We model the problem as a partially observable Markov decision process (POMDP), which allows a general reward based optimization objective and takes uncertainty in temporal evolution and partial observations into account. In addition to occlusion dependent observation and action success probabilities, our POMDP model also automatically adapts object specific action success probabilities. To cope with the changing system dynamics and performance constraints, we present a new online POMDP method based on particle filtering that produces compact policies. The approach is validated both in simulation and in physical experiments in a scenario of moving dirty dishes into a dishwasher. The results indicate that: 1) a greedy heuristic manipulation approach is not sufficient, multi-object manipulation requires multi-step POMDP planning, and 2) on-line planning is beneficial since it allows the adaptation of the system dynamics model based on actual experience

Symbolic representation of what robots are taught in one demonstration

To facilitate the use of robots in small and medium-sized enterprises (SMEs), they have to be easily and quickly deployed by non-expert users. Programming by Demonstration (PbD) is considered a fast and intuitive approach to handle this requirement. However, one of the major drawbacks of pure PbD is that it may suffer from poor generalisation capabilities, as it is mainly capable of motion-level representations. This work proposes a method to semantically represent a demonstrated skill, so as to identify the elements of the workspace that are relevant for the characterisation of the skill itself, as well as its preconditions and effects. This way, the robot can automatically abstract from the demonstration and memorise the skill in a more general way. An experimental case study consisting in a manipulation task is reported to validate the approach