Automated sequence and motion planning for robotic spatial extrusion of 3D trusses

While robotic spatial extrusion has demonstrated a new and efficient means to fabricate 3D truss structures in architectural scale, a major challenge remains in automatically planning extrusion sequence and robotic motion for trusses with unconstrained topologies. This paper presents the first attempt in the field to rigorously formulate the extrusion sequence and motion planning (SAMP) problem, using a CSP encoding. Furthermore, this research proposes a new hierarchical planning framework to solve the extrusion SAMP problems that usually have a long planning horizon and 3D configuration complexity. By decoupling sequence and motion planning, the planning framework is able to efficiently solve the extrusion sequence, end-effector poses, joint configurations, and transition trajectories for spatial trusses with nonstandard topologies. This paper also presents the first detailed computation data to reveal the runtime bottleneck on solving SAMP problems, which provides insight and comparing baseline for future algorithmic development. Together with the algorithmic results, this paper also presents an open-source and modularized software implementation called Choreo that is machine-agnostic. To demonstrate the power of this algorithmic framework, three case studies, including real fabrication and simulation results, are presented.Comment: 24 pages, 16 figure

Sampling-Based Methods for Factored Task and Motion Planning

This paper presents a general-purpose formulation of a large class of discrete-time planning problems, with hybrid state and control-spaces, as factored transition systems. Factoring allows state transitions to be described as the intersection of several constraints each affecting a subset of the state and control variables. Robotic manipulation problems with many movable objects involve constraints that only affect several variables at a time and therefore exhibit large amounts of factoring. We develop a theoretical framework for solving factored transition systems with sampling-based algorithms. The framework characterizes conditions on the submanifold in which solutions lie, leading to a characterization of robust feasibility that incorporates dimensionality-reducing constraints. It then connects those conditions to corresponding conditional samplers that can be composed to produce values on this submanifold. We present two domain-independent, probabilistically complete planning algorithms that take, as input, a set of conditional samplers. We demonstrate the empirical efficiency of these algorithms on a set of challenging task and motion planning problems involving picking, placing, and pushing

PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning

Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, collision, visibility, and motion constraints involving robot configurations, object poses, and robot trajectories. These constraints typically require specialized procedures to sample satisfying values. We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes. We provide domain-independent algorithms that reduce PDDLStream problems to a sequence of finite PDDL problems. We also introduce an algorithm that dynamically balances exploring new candidate plans and exploiting existing ones. This enables the algorithm to greedily search the space of parameter bindings to more quickly solve tightly-constrained problems as well as locally optimize to produce low-cost solutions. We evaluate our algorithms on three simulated robotic planning domains as well as several real-world robotic tasks.Comment: International Conference on Automated Planning and Scheduling (ICAPS) 202

Active model learning and diverse action sampling for task and motion planning

The objective of this work is to augment the basic abilities of a robot by learning to use new sensorimotor primitives to enable the solution of complex long-horizon problems. Solving long-horizon problems in complex domains requires flexible generative planning that can combine primitive abilities in novel combinations to solve problems as they arise in the world. In order to plan to combine primitive actions, we must have models of the preconditions and effects of those actions: under what circumstances will executing this primitive achieve some particular effect in the world? We use, and develop novel improvements on, state-of-the-art methods for active learning and sampling. We use Gaussian process methods for learning the conditions of operator effectiveness from small numbers of expensive training examples collected by experimentation on a robot. We develop adaptive sampling methods for generating diverse elements of continuous sets (such as robot configurations and object poses) during planning for solving a new task, so that planning is as efficient as possible. We demonstrate these methods in an integrated system, combining newly learned models with an efficient continuous-space robot task and motion planner to learn to solve long horizon problems more efficiently than was previously possible.Comment: Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain. https://www.youtube.com/playlist?list=PLoWhBFPMfSzDbc8CYelsbHZa1d3uz-W_

Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning

Robots planning long-horizon behavior in complex environments must be able to quickly reason about the impact of the environment's geometry on what plans are feasible, i.e., whether there exist action parameter values that satisfy all constraints on a candidate plan. In tasks involving articulated and movable obstacles, typical Task and Motion Planning (TAMP) algorithms spend most of their runtime attempting to solve unsolvable constraint satisfaction problems imposed by infeasible plan skeletons. We developed a novel Transformer-based architecture, PIGINet, that predicts plan feasibility based on the initial state, goal, and candidate plans, fusing image and text embeddings with state features. The model sorts the plan skeletons produced by a TAMP planner according to the predicted satisfiability likelihoods. We evaluate the runtime of our learning-enabled TAMP algorithm on several distributions of kitchen rearrangement problems, comparing its performance to that of non-learning baselines and algorithm ablations. Our experiments show that PIGINet substantially improves planning efficiency, cutting down runtime by 80% on average on pick-and-place problems with articulated obstacles. It also achieves zero-shot generalization to problems with unseen object categories thanks to its visual encoding of objects

SAPIEN3 Comb Stitch Improvement for Edwards Lifesciences

The complete senior project report was submitted to the project advisor and sponsor. The results of this project are of a confidential nature and will not be published at this time

Assessing the Rate of Success of Alternative Farm Transition Strategies

Research suggests only 30 percent of family owned businesses successfully transfer from the founding generation to the second generation. These success rates continue to decline when transferring to subsequent generations. Development of a decision tool to assist in making choices about strategies best allowing farm families to keep the farm in operation and satisfy heirs could reduce the risk of conflict with respect to the plan's implementation. The question is, what farm transition strategies reduce farm financial stress? A representative Oklahoma farm, family, and set of farm transition strategies are developed. Each strategy is imposed on the model farm subject to time, equity, and cash flow demands. Net farm income and the strategy's cash flow demands are used to determine the plan's feasibility. A Monte Carlo simulation is then utilized to consider variability in net farm income. Each strategy is simulated 500 times. The probability of success for each alternative strategy is then calculated by the number of successful transitions divided by the total number of iterations, based on criteria for leverage and cash flow. Results found strategies with an equal division of assets functionally requiring repurchases of assets from off-farm siblings are more challenging to accomplish. More successful strategies incorporated placing operating and land assets in separate legal entities, with both heirs owning the land entity. Creating financial assets either equal to the value or equal to one-half the value of the operating entity to give to the off-farm heir proved to be more successful. Another approach consisted of a lifetime farm business transfer in which the farm heir purchases shares of the operating entity each year, with help from the preceding generation when funds are deficient. At the end of the transition, cash reserves are split amongst heirs and the heirs are equal owners in the land entity

FFRob: An Efficient Heuristic for Task and Motion Planning

Manipulation problemsinvolvingmany objects present substantial challenges for motion planning algorithms due to the high dimensionality and multi-modality of the search space. Symbolic task planners can efficiently construct plans involving many entities but cannot incorporate the constraints from geometry and kinematics. In this paper, we show how to extend the heuristic ideas from one of the most successful symbolic planners in recent years, the FastForward (FF) planner, to motion planning, and to compute it efficiently. We use a multi-query roadmap structure that can be conditionalized to model different placements of movable objects. The resulting tightly integrated planner is simple and performs efficiently in a collection of tasks involving manipulation of many objects.National Science Foundation (U.S.) (Grant No. 019868)United States. Office of Naval Research. Multidisciplinary University Research Initiative (grant N00014-09-1-1051)United States. Air Force. Office of Scientific Research (grant AOARD-104135)Singapore. Ministry of Educatio

Boise Exploration Project

To being the innovate challenge, we were presented with five undeveloped properties that we needed to turn into something innovative, filled the needs of the community, was feasible, and grounded in evidence. Our wish was to build something so that Downtown Boise could become a place that truly fostered a sense of community, and culture, while emphasizing education by bringing together everyone from adults to children, students to businessmen, and urban to suburban. This vision was formalized through the construction of our idea: to build a large, interactive Boise City Museum. This museum would take visitors on an interactive journey through the world, from dinosaurs, to Idaho history, to space exploration. We wanted visitors to experience education, to not only learn about history in a classroom; therefore, the Boise City Museum would offer an IMAX experience as well as a public-access planetarium. These innovations would not only allow potential partners like Boise State University, Microsoft, and Hewlett Packard the chance to have a foothold in the community, but also they would inspire young students through sponsoring an exhibit. However, our vision did not stop with the Boise City Museum, we wanted to foster all of the arts, so we added an amphitheatre that could house different plays, local orchestras and support other arts. Next to the amphitheatre, a shopping center called The Marketplace is set; it is a place that will be supportive to small, local businesses and restaurants. This place will have beautiful architecture to provide a breathtaking first glimpse of Boise when exiting the connector. Other innovative aspects to our design was the addition of pedestrian bridges to encourage walking and bicycling; also a parking garage, to help address some of the space issues business people downtown experience. Our design, the Boise Exploration Project, is a large scale, innovative project designed around Boise’s strengths as a community