570980
An efficient on-line path planner for outdoor mobile robots
Abstract Mobile robots operating in outdoor unstructured environments often have only incomplete maps and must deal with new objects found during traversal. Path planning in these environments must be incremental to accommodate new information and must use efficient representations. This article reports recent results in path planning using an efficient data structure (framed-quadtrees) and an optimal algorithm (D*) to incrementally replan optimal paths. We show how the use of framed-quadtrees leads to paths that are shorter and more direct than when other representations are used. We also show the difference in performance when the robot starts with no information about the world versus when it starts with partial information about the world. Our results indicate that, as would be expected, starting with partial information is better than starting with no information. However, in many cases, partial information results in performance that is almost as good as starting out with complete information about the world, while the computational cost incurred is significantly lower. Our system has been tested in simulation as well on an autonomous jeep equipped with local obstacle avoidance capabilities. Results from both simulation and real experimentation are discussed.
668272
First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly
While many multi-robot systems rely on fortuitous cooperation between agents, some tasks, such as the assembly of large structures, require tighter coordination. We present a general software architecture for coordinating heterogeneous robots that allows for both autonomy of the individual agents as well as explicit coordination. This paper presents recent results with three robots with very different configurations. Working as a team, these robots are able to perform a high-precision docking task that none could achieve individually.
105018
Multi-resolution planning for earthmoving
We suggest that planning for automated earthmoving operations such as digging a foundation or leveling a mound of soil, be treated at multiple levels. In a system that we have developed, a coarse-level planner is used to tessellate the volume to be excavated into smaller pieces that are sequenced in order to complete the task efficiently. Each of the smaller volumes is treated with a refined planner that selects digging actions based on constraint optimization over the space of prototypical digging actions. We discuss planners and the associated representations for two types of earthmoving machines: an excavator backhoe and a wheel loader. Experimental results from a full-scale automated excavator and simulated wheel loader are presented.
11487617
Attaining situational awareness for sliding autonomy
We are interested in the problems of a human operator who is responsible for rapidly and accurately responding to requests for help from an autonomous robotic construction team. A difficult aspect of this problem is gaining an awareness of the requesting robot's situation quickly enough to avoid slowing the whole team down. One approach to speeding the initial acquisition of situational awareness is to maintain a buffer of data, and play it back for the human when their help is needed. We report here on an experiment to determine how the composition and length of this buffer affect the human's speed and accuracy in our multi-robot construction domain. The experiments show that, for our scenario, 5 - 10 seconds of one raw video feed led to the fastest operator attainment of situational awareness, while accuracy was maximized by viewing 10 seconds of three video feeds. These results are necessarily specific to our scenario, but we feel that they indicate general trends which may be of use in other situations. We discuss the interacting effects of buffer composition and length on operator speed and accuracy, and draw several conclusions from this experiment which may generalize to other scenarios.
14312550
Obstacle detection using adaptive color segmentation and color stereo homography
Obstacle detection is a key component of autonomous systems. In particular, when dealing with large robots in unstructured environments, robust obstacle detection is vital. In this paper, we describe an obstacle detection methodology which combines two complementary methods: adaptive color segmentation, and stereo-based color homography. This algorithm is particularly suited for environments in which the terrain is relatively flat and of roughly the same color. We will show results in applying this method to an autonomous outdoor robot.
9012983
First results in the autonomous retrieval of buried objects
We have developed an autonomous system for the retrieval of buried objects. It is designed to detect, locate and retrieve buried objects. The system is equipped with a hydraulic robot, laser range finder and a subsurface sensor. First, subsurface sensing is used to detect and locate buried objects. If an object can be reached with one dig, the excavator retrieves it directly. Otherwise a layer of soil above the object is removed and another subsurface scan is made to get a more accurate estimate of the object location. This loop is repeated until the object is retrieved. The paper presents some recent results in sensing and excavation from experiments conducted on our testbed. There are numerous potential applications for this system-hazardous waste removal, maintenance of subsurface structure, construction, and removal of unexploded buried ordnance.<<ETX>>
17617313
Using Eligibility Traces to Find the Best Memoryless Policy in Partially Observable Markov Decision Processes
Recent research on hidden-state reinforcement learning (RL) problems has concentrated on overcoming partial observability by using memory to estimate state. However, such methods are computationally extremely expensive and thus have very limited applicability. This emphasis on state estimation has come about because it has been widely observed that the presence of hidden state or partial observability renders popular RL methods such as Q-learning and Sarsa useless. However, this observation is misleading in two ways: first, the theoretical results supporting it only apply to RL algorithms that do not use eligibility traces, and second these results are worst-case results, which leaves open the possibility that there may be large classes of hidden-state problems in which RL algorithms work well without any state estimation. In this paper we show empirically that Sarsa(λ), a well known family of RL algorithms that use eligibility traces, can work very well on hidden state problems that have good memoryless policies, i.e., on RL problems in which there may well be very poor observability but there also exists a mapping from immediate observations to actions that yields near-optimal return. We apply conventional Sarsa(λ) to four test problems taken from the recent work of Littman, Littman Cassandra and Kaelbling, Parr and Russell, and Chrisman, and in each case we show that it is able to find the best, or a very good, memoryless policy without any of the computational expense of state estimation.
8140980
An MDP-Based Approach to Online Mechanism Design
Online mechanism design (MD) considers the problem of providing incentives to implement desired system-wide outcomes in systems with self-interested agents that arrive and depart dynamically. Agents can choose to misrepresent their arrival and departure times, in addition to information about their value for different outcomes. We consider the problem of maximizing the total long-term value of the system despite the self-interest of agents. The online MD problem induces a Markov Decision Process (MDP), which when solved can be used to implement optimal policies in a truth-revealing Bayesian-Nash equilibrium.
748558
Automated Worm Fingerprinting
Network worms are a clear and growing threat to the security of today's Internet-connected hosts and networks. The combination of the Internet's unrestricted connectivity and widespread software homogeneity allows network pathogens to exploit tremendous parallelism in their propagation. In fact, modern worms can spread so quickly, and so widely, that no human-mediated reaction can hope to contain an outbreak. 
 
In this paper, we propose an automated approach for quickly detecting previously unknown worms and viruses based on two key behavioral characteristics - a common exploit sequence together with a range of unique sources generating infections and destinations being targeted. More importantly, our approach - called "content sifting" - automatically generates precise signatures that can then be used to filter or moderate the spread of the worm elsewhere in the network. 
 
Using a combination of existing and novel algorithms we have developed a scalable content sifting implementation with low memory and CPU requirements. Over months of active use at UCSD, our Earlybird prototype system has automatically detected and generated signatures for all pathogens known to be active on our network as well as for several new worms and viruses which were unknown at the time our system identified them. Our initial experience suggests that, for a wide range of network pathogens, it may be practical to construct fully automated defenses - even against so-called "zero-day" epidemics.
11231260
Cobot in LambdaMOO: An Adaptive Social Statistics Agent
We describe our development of Cobot, a novel software agent who lives in LambdaMOO, a popular virtual world frequented by hundreds of users. Cobot’s goal was to become an actual part of that community. Here, we present a detailed discussion of the functionality that made him one of the objects most frequently interacted with in LambdaMOO, human or artificial. Cobot’s fundamental power is that he has the ability to collect social statistics summarizing the quantity and quality of interpersonal interactions. Initially, Cobot acted as little more than a reporter of this information; however, as he collected more and more data, he was able to use these statistics as models that allowed him to modify his own behavior. In particular, cobot is able to use this data to “self-program,” learning the proper way to respond to the actions of individual users, by observing how others interact with one another. Further, Cobot uses reinforcement learning to proactively take action in this complex social environment, and adapts his behavior based on multiple sources of human reward. Cobot represents a unique experiment in building adaptive agents who must live in and navigate social spaces.
13624034
Learning to Act Using Real-Time Dynamic Programming
Learning methods based on dynamic programming (DP) are receiving increasing attention in artificial intelligence. Researchers have argued that DP provides the appropriate basis for compiling planning results into reactive strategies for real-time control, as well as for learning such strategies when the system being controlled is incompletely known. We introduce an algorithm based on DP, which we call Real-Time DP (RTDP), by which an embedded system can improve its performance with experience. RTDP generalizes Korf''s Learning-Real-Time-A algorithm to problems involving uncertainty. We invoke results from the theory of asynchronous DP to prove that RTDP achieves optimal behavior in several different classes of problems. We also use the theory of asynchronous DP to illuminate aspects of other DP-based reinforcement learning methods such as Watkins'' Q-Learning algorithm. A secondary aim of this article is to provide a bridge between AI research on real-time planning and learning and relevant concepts and algorithms from control theory. This research was supported by grants to A.G. Barto from the National Science Foundation (ECS-8912623 and ECS-9214866) and the Air Force Office of Scientific Research, Bolling AFB (AFOSR-89-0526).