Sensor-Based Reactive Symbolic Planning in Partially Known Environments

This paper considers the problem of completing assemblies of passive objects in nonconvex environments, cluttered with convex obstacles of unknown position, shape and size that satisfy a specific separation assumption. A differential drive robot equipped with a gripper and a LIDAR sensor, capable of perceiving its environment only locally, is used to position the passive objects in a desired configuration. The method combines the virtues of a deliberative planner generating high-level, symbolic commands, with the formal guarantees of convergence and obstacle avoidance of a reactive planner that requires little onboard computation and is used online. The validity of the proposed method is verified both with formal proofs and numerical simulations. For more information: Kod*la

Reactive Navigation in Partially Known Non-Convex Environments

This paper presents a provably correct method for robot navigation in 2D environments cluttered with familiar but unexpected non-convex, star-shaped obstacles as well as completely unknown, convex obstacles. We presuppose a limited range onboard sensor, capable of recognizing, localizing and (leveraging ideas from constructive solid geometry) generating online from its catalogue of the familiar, non-convex shapes an implicit representation of each one. These representations underlie an online change of coordinates to a completely convex model planning space wherein a previously developed online construction yields a provably correct reactive controller that is pulled back to the physically sensed representation to generate the actual robot commands. We extend the construction to differential drive robots, and suggest the empirical utility of the proposed control architecture using both formal proofs and numerical simulations. For more information: Kod*la

Composition of Templates for Transitional Pedipulation Behaviors

Abstract. We document the reliably repeatable dynamical mounting and dismounting of wheeled stools and carts, and of fixed ledges, by the Minitaur robot. Because these tasks span a range of length scales that preclude quasi-static execution, we use a hybrid dynamical systems framework to variously compose and thereby systematically reuse a small lexicon of templates (low degree of freedom behavioral primitives). The resulting behaviors comprise the key competences beyond mere locomotion required for robust implementation on a legged mobile manipulator of a simple version of the warehouseman’s problem

SAFETY-GUARANTEED TASK PLANNING FOR BIPEDAL NAVIGATION IN PARTIALLY OBSERVABLE ENVIRONMENTS

Bipedal robots are becoming more capable as basic hardware and control challenges are being overcome, however reasoning about safety at the task and motion planning levels has been largely underexplored. I would like to make key steps towards guaranteeing safe locomotion in cluttered environments in the presence of humans or other dynamic obstacles by designing a hierarchical task planning framework that incorporates safety guarantees at each level. This layered planning framework is composed of a coarse high-level symbolic navigation planner and a lower-level local action planner. A belief abstraction at the global navigation planning level enables belief estimation of non-visible dynamic obstacle states and guarantees navigation safety with collision avoidance. Both planning layers employ linear temporal logic for a reactive game synthesis between the robot and its environment while incorporating lower level safe locomotion keyframe policies into formal task specification design. The high-level symbolic navigation planner has been extended to leverage the capabilities of a heterogeneous multi-agent team to resolve environment assumption violations that appear at runtime. Modifications in the navigation planner in conjunction with a coordination layer allow each agent to guarantee immediate safety and eventual task completion in the presence of an assumption violation if another agent exists that can resolve said violation, e.g. a door is closed that another dexterous agent can open. The planning framework leverages the expressive nature and formal guarantees of LTL to generate provably correct controllers for complex robotic systems. The use of belief space planning for dynamic obstacle belief tracking and heterogeneous robot capabilities to assist one another when environment assumptions are violated allows the planning framework to reduce the conservativeness traditionally associated with using formal methods for robot planning.M.S

Sensor-Based Reactive Symbolic Planning in Partially Known Environments

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Reactive Planning With Legged Robots In Unknown Environments

Unlike the problem of safe task and motion planning in a completely known environment, the setting where the obstacles in a robot\u27s workspace are not initially known and are incrementally revealed online has so far received little theoretical interest, with existing algorithms usually demanding constant deliberative replanning in the presence of unanticipated conditions. Moreover, even though recent advances show that legged platforms are becoming better at traversing rough terrains and environments, legged robots are still mostly used as locomotion research platforms, with applications restricted to domains where interaction with the environment is usually not needed and actively avoided. In order to accomplish challenging tasks with such highly dynamic robots in unexplored environments, this research suggests with formal arguments and empirical demonstration the effectiveness of a hierarchical control structure, that we believe is the first provably correct deliberative/reactive planner to engage an unmodified general purpose mobile manipulator in physical rearrangements of its environment. To this end, we develop the mobile manipulation maneuvers to accomplish each task at hand, successfully anchor the useful kinematic unicycle template to control our legged platforms, and integrate perceptual feedback with low-level control to coordinate each robot\u27s movement. At the same time, this research builds toward a useful abstraction for task planning in unknown environments, and provides an avenue for incorporating partial prior knowledge within a deterministic framework well suited to existing vector field planning methods, by exploiting recent developments in semantic SLAM and object pose and triangular mesh extraction using convolutional neural net architectures. Under specific sufficient conditions, formal results guarantee collision avoidance and convergence to designated (fixed or slowly moving) targets, for both a single robot and a robot gripping and manipulating objects, in previously unexplored workspaces cluttered with non-convex obstacles. We encourage the application of our methods by providing accompanying software with open-source implementations of our algorithms