Abstract-We present a novel approach for determining robot movements that efficiently accomplish the robot's tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrancesensitive robot trajectory planning provided by our approach. I. INTRODUCTIONDetermining appropriate robotic actions in environments with moving people is a well-studied [15], [2], [5], but often difficult task due to the uncertainty of each person's future behavior. Robots should certainly never collide with people [11], but avoiding collisions alone is often unsatisfactory because the disruption of almost colliding can be burdensome to people and sub-optimal for robots. Instead, robots should predict the future locations of people and plan routes that will avoid such hindrances (i.e., situations where the person's natural behavior is disrupted due to a robot's proximity) while still efficiently achieving the robot's objectives. For example, given the origins and target destinations of the robot and person in Figure 1, the robot's hindrance-minimizing trajectory would take the longer way around the center obstacle (a table), leaving a clear path for the pedestrian.One common approach for predicting trajectories is to project the prediction step of a tracking filter [9], [13], [10] forward over time. For example, a Kalman filter's [7] future positions are predicted according to a Gaussian distribution with growing uncertainty and, unfortunately, often high probability for physically impossible locations (e.g., behind walls, within obstacles). Particle filters [16] can incorporate more sophisticated constraints and non-Gaussian distributions, but degrade into random walks of feasible motion over large time horizons rather than purposeful, goal-based motion. Closer to our research are approaches that directly model the policy [6]. These approaches assume that previously observed trajectories capture all purposeful behavior, and the only uncertainty involves determining to which previously observed class of trajectories the current behavior belongs. Models based on mixtures of trajectories and conditioned action distribution modeling (using hidden Markov models) have been employed [17]. This approach often suffers from over-fitting to the particular training trajectories and context of those trajectories. When changes to the environment occur (e.g., rearrangement of the furniture), the model will confidently predict incorrect trajectories through obstacles.

We describe the architecture, algorithms, and experiments with HERB, an autonomous mobile manipulator that performs useful manipulation tasks in the home. We present new algorithms for searching for objects, learning to navigate in cluttered dynamic indoor scenes, recognizing and registering objects accurately in high clutter using vision, manipulating doors and other constrained objects using caging grasps, grasp planning and execution in clutter, and manipulation on pose and torque constraint manifolds. We also present numerous severe real-world test results from the integration of these algorithms into a single mobile manipulator.

Abstract-We present GATMO (Generalized Approach to Tracking Movable Objects), a system for localization and mapping that incorporates the dynamic nature of the environment while maintaining semantic labels. Objects in the environment are broken down into multiple mobility levels, from static (walls) to highly mobile (people), by maintaining a history of object movement. Object classification is accomplished through a multi-layer, multi-hypothesis approach that does not rely on any static features such as shape or size. Maps are stored in an efficient manner that incorporates a history of previous orientations of each object. GATMO is initialized with a static map; it subsequently changes the map over time as objects in the map change position.

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Many materials and electronics need to be tested for the radiation environment expected at linear colliders (LCs) to improve reliability and longevity since both accelerator and detectors will be subjected to large fluences of hadrons, leptons and gammas. Examples include NdFeB magnets, considered for the damping rings, injection and extraction lines and final focus; electronic, electro-and fiber-optics to be utilized in detector readout, accelerator controls and the CCDs required for the vertex detector; as well as high and low temperature superconducting materials (LTSMs) for cavities and some magnets. Our first measurements of fast neutron, stepped doses at the UC Davis McClellan Nuclear Reactor Center (UCD MNRC) were for NdFeB materials at EPAC04 [1]. We have extended the doses, included more manufacturer's samples and measured radioactivities. We also added L and HTSMs and various semiconductor and electro-optic materials such as photonic band-gap (PBG) fiber that we studied previously with gamma rays.