Path Clearance

In military scenarios, agents (i.e., troops of soldiers, convoys, and unmanned vehicles) may often have to traverse environments with only a limited intelligence about the locations of adversaries. We study a particular instance of this problem that we refer to as path clearance problem.This article presents a survey of our work on scalable and suitable for real-time use approaches to solving the path clearance problem. In particular, in the first part of the article, we show that the path clearance problem exhibits clear preferences on uncertainty. It turns out that these clear preferences can be used to develop an efficient algorithm called probabilistic planning with clear preferences (PPCP). The algorithm is anytime usable, converges to an optimal solution under certain conditions, and scales well to large-scale problems. We briefly describe the PPCP algorithm and show how it can be used to solve the path clearance problem when no scouts are present. In the second part of the article, we show several strategies for how to use the PPCP algorithm in case multiple scouting unmanned aerial vehicles (UAVs) are available. The experimental analysis shows that planning with PPCP results in a substantially smaller execution cost than when ignoring uncertainty, and employing scouts can decrease this execution cost even further

R* Search

Optimal heuristic searches such as A* search are widely used for planning but can rarely scale to large complex problems. The suboptimal versions of heuristic searches such as weighted A* search can often scale to much larger planning problems by trading off the quality of the solution for efficiency. They do so by relying more on the ability of the heuristic function to guide them well towards the goal. For complex planning problems, however, the heuristic function may often guide the search into a large local minimum and make the search examine most of the states in the minimum before proceeding. In this paper, we propose a novel heuristic search, called R* search, which depends much less on the quality of the heuristic function. The search avoids local minima by solving the whole planning problem with a series of short-range and easy-to-solve searches, each guided by the heuristic function towards a randomly chosen goal. In addition, R* scales much better in terms of memory because it can discard a search state-space after each of its searches. On the theoretical side, we derive probabilistic guarantees on the sub-optimality of the solution returned by R*. On the experimental side, we show that R* can scale to large complex problems

Learning Models for Following Natural Language Directions in Unknown Environments

Natural language offers an intuitive and flexible means for humans to communicate with the robots that we will increasingly work alongside in our homes and workplaces. Recent advancements have given rise to robots that are able to interpret natural language manipulation and navigation commands, but these methods require a prior map of the robot's environment. In this paper, we propose a novel learning framework that enables robots to successfully follow natural language route directions without any previous knowledge of the environment. The algorithm utilizes spatial and semantic information that the human conveys through the command to learn a distribution over the metric and semantic properties of spatially extended environments. Our method uses this distribution in place of the latent world model and interprets the natural language instruction as a distribution over the intended behavior. A novel belief space planner reasons directly over the map and behavior distributions to solve for a policy using imitation learning. We evaluate our framework on a voice-commandable wheelchair. The results demonstrate that by learning and performing inference over a latent environment model, the algorithm is able to successfully follow natural language route directions within novel, extended environments.Comment: ICRA 201

Improved Path Planning Onboard the Mars Exploration Rovers

A revised version of the AutoNav (autonomous navigation with hazard avoidance) software running onboard each Mars Exploration Rover (MER) affords better obstacle avoidance than does the previous version. Both versions include GESTALT (Grid-based Estimation of Surface Traversability Applied to Local Terrain), a navigation program that generates local-terrain models from stereoscopic image pairs captured by onboard rover cameras; uses this information to evaluate candidate arcs that extend across the terrain from the current rover location; ranks the arcs with respect to hazard avoidance, minimization of steering time, and the direction towards the goal; and combines the rankings in a weighted vote to select an arc, along which the rover is then driven. GESTALT works well in navigating around small isolated obstacles, but tends to fail when the goal is on the other side of a large obstacle or multiple closely spaced small obstacles. When that occurs, the goal seeking votes and hazard avoidance votes conflict severely. The hazard avoidance votes will not allow the rover to drive through the unsafe area, and the waypoint votes will not allow enough deviation from the straight-line path for the rover to get around the hazard. The rover becomes stuck and is unable to reach the goal. The revised version of AutoNav utilizes a global path-planning program, Field D*, to evaluate the cost of traveling from the end of each GESTALT arc to the goal. In the voting process, Field D* arc votes supplant GESTALT goal-seeking arc votes. Hazard avoidance, steering bias, and Field D* votes are merged and the rover is driven a preset distance along the arc with the highest vote. Then new images are acquired and the process as described is repeated until the goal is reached. This new technology allows the rovers to autonomously navigate around much more complex obstacle arrangements than was previously possible. In addition, this improved autonomy enables longer traverses per Sol (a day on Mars), and can make planning drives easier for operators on Earth

Learning to locate from demonstrated searches

Abstract—We consider the problem of learning to locate targets from demonstrated searches. In this concept, a human demonstrates tours of environments that are assumed to minimize the human’s expected time to locate the target, given the person’s latent prior over potential target locations. The latent prior is then learned as a function of environmental features, enabling a robot to search novel environments in a way that would be deemed efficient by the teacher. We present novel approaches to solve both the inference problem of planning an expected-time-optimal tour given a prior and the learning problem of deducing the prior from observed tours. Our learning algorithm is inspired by and similar to maximum margin planning (MMP), although it differs in key ways. On the inference side, we advance the state-of-the-art by proposing novel relaxations that are integrated into a heuristic-driven search algorithm. An application to a home assistant scenario is discussed, and experimental results are given validating our methods in this domain. I

Anytime search in dynamic graphs

Agents operating in the real world often have limited time available for planning their next actions. Producing optimal plans is infeasible in these scenarios. Instead, agents must be satisfied with the best plans they can generate within the time available. One class of planners well-suited to this task are anytime planners, which quickly find an initial, highly suboptimal plan, and then improve this plan until time runs out. A second challenge associated with planning in the real world is that models are usually imperfect and environments are often dynamic. Thus, agents need to update their models and consequently plans over time. Incremental planners, which make use of the results of previous planning efforts to generate a new plan, can substantially speed up each planning episode in such cases. In this paper, we present an A^*-based anytime search algorithm that produces significantly better solutions than current approaches, while also providing suboptimality bounds on the quality of the solution at any point in time. We also present an extension of this algorithm that is both anytime and incremental. This extension improves its current solution while deliberation time allows and is able to incrementally repair its solution when changes to the world model occur. We provide a number of theoretical and experimental results and demonstrate the effectiveness of the approaches in a robot navigation domain involving two physical systems. We believe that the simplicity, theoretical properties, and generality of the presented methods make them well suited to a range of search problems involving dynamic graphs

Enhancing Robot Perception Using Human Teammates * (Extended Abstract)

ABSTRACT In robotics research, perception is one of the most challenging tasks. In contrast to existing approaches that rely only on computer vision, we propose an alternative method for improving perception by learning from human teammates. To evaluate, we apply this idea to a door detection problem. A set of preliminary experiments has been completed using software agents with real vision data. Our results demonstrate that information inferred from teammate observations significantly improves the perception precision. Categories and Subject Descriptors I.2.11 [Distributed Artificial Intelligence]: Intelligent agents General Terms Human Factors Keywords Robot perception, robot-human hybrid teams BACKGROUND Robot perception is generally formulated as a problem of analyzing and interpreting various sensory inputs, e.g., camera feeds. In this paper, we approach robot perception from a completely different direction. Our approach utilizes a team setting where a robot collaborates with human teammates. Motivated by the fact that humans possess superior perception skills relative to their robotic counterparts, we investigate how a robot can take advantage of its teammate's perfect vision. In general, an agent acquires new information through perception, and in turn, the agent chooses actions based on the information acquired. Let us suppose that a robot has a mental model of its human teammate such that a causal relationship is specified between information and actions. Then, by understanding the human mental model of such decision making (or planning), the robot can infer what the human teammate has seen based on the human's behavior. In other words, an observation of a human teammate can be * This work was conducted (in part) through collaborative participation in the Robotics Consortium sponsored by the U. used as evidence to infer the information perceived by the human. This, in turn, can be used to reduce uncertainty in robot perception. In this paper, we specifically focus on a motivating problem of door detection in the following scenario. Consider a team consisting of a robot and a human performing a military operation in a hostile environment. According to intelligence, armed insurgents are hiding in an urban street. The team is deployed to cover the buildings in the surrounding area, focusing on doors from which the insurgents may try to egress. This is a stealth operation. We make two specific assumptions that are reasonable in a team context. First, observing a teammate is generally more manageable than perceiving an unfamiliar environment. Second, team members share common objectives in reaching the team's goals. PERCEPTION USING VISION This section describes a purely camera-based approach. First, we find a likely semantic image segmentation using a computer vision technique called stacked hierarchical labeling It is not constrained by shape grammars and can model a more general class of objects, but its method of constructing a hierarchical segmentation does not convey semantic meaning at a finer detail, as would be necessary to detect doors on a building. It is, however, reliable in detecting buildings as a whole, significantly reducing the search space for detecting doors in the next step. Once buildings are identified, we can apply a broad feature detector to detect likely openings on the façade of the building. As i