Technical session talks from ICRA 2012
TechTalks from event: Technical session talks from ICRA 2012
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AI Reasoning Methods
An Adaptive Nonparametric Particle Filter for State EstimationParticle filter is one of the most widely applied stochastic sampling tools for state estimation problems in practice. However, the proposal distribution in the traditional particle filter is the transition probability based on state equation, which would heavily affect estimation performance in that the samples are blindly drawn without considering the current observation information. Additionally, the fixed particle number in the typical particle filter would lead to wasteful computation, especially when the posterior distribution greatly varies over time. In this paper, an advanced adaptive nonparametric particle filter is proposed by incorporating gaussian process based proposal distribution into KLD-Sampling particle filter framework so that the high-qualified particles with adaptively KLD based quantity are drawn from the learned proposal with observation information at each time step to improve the approximation accuracy and efficiency. Our state estimation experiments on univariate nonstationary growth model and two-link robot arm show that the adaptive nonparametric particle filter outperforms the existing approaches with smaller size of particles.
Online Semantic Exploration of Indoor MapsIn this paper we propose a method to extract an abstracted floor plan from typical grid maps using Bayesian reasoning. The result of this procedure is a probabilistic generative model of the environment defined over abstract concepts. It is well suited for higher-level reasoning and communication purposes. We demonstrate the effectiveness of the approach through real-world experiments.
Game Solving for Industrial Automation and ControlAn ongoing effort within the community of verification and program analysis is to raise the level of abstraction in programming by automatic synthesis. In this paper, we demonstrate how our synthesis engine GAVS+ achieves this goal by automatically creating control code for the FESTO modular production system. The overall approach is model-driven: we reinterpret planning domain definition language (PDDL) (as a design contract) to model two-player games played between control and environment, such that users can describe (i) basic abilities of hardware components, including sensors (as environment moves) and actuators (as control moves), (ii) topologies how components are interconnected, and (iii) desired specification under a restricted class of linear temporal logic. The model is processed by our game-based synthesis engine, and from which intermediate code is generated. By mapping each behavioral-level action to a sequence of low-level PLC control commands, we transform the intermediate code to an executable program. The efficiency of our engine enables to synthesize every scenario presented in this paper within seconds. When the specification evolves, this implies a huge time-gain compared to manual code modification.
Learning Relational Affordance Models for Robots in Multi-Object Manipulation TasksAffordances define the action possibilities on an object in the environment and in robotics they play a role in basic cognitive capabilities. Previous works have focused on affordance models for just one object even though in many scenarios they are defined by configurations of multiple objects that interact with each other. We employ recent advances in statistical relational learning to learn affordance models in such cases. Our models generalize over objects and can deal effectively with uncertainty. Two-object interaction models are learned from robotic interaction with the objects in the world and employed in situations with arbitrary numbers of objects. We illustrate these ideas with experimental results of an action recognition task where a robot manipulates objects on a shelf.
Abstract Planning for Reactive RobotsHybrid reactive-deliberative architectures in robotics combine reactive sub-policies for fast action execution with goal sequencing and deliberation. The need for replanning, however, presents a challenge for reactivity and hinders the potential for guarantees about the plan quality. In this paper, we argue that one can integrate abstract planning provided by symbolic dynamic programming in first order logic into a reactive robotic architecture, and that such an integration is in fact natural and has advantages over traditional approaches. In particular, it allows the integrated system to spend off-line time planning for a policy, and then use the policy reactively in open worlds, in situations with unexpected outcomes, and even in new environments, all by simply reacting to a state change executing a new action proposed by the policy. We demonstrate the viability of the approach by integrating the FODD-Planner with the robotic DIARC architecture showing how an appropriate interface can be defined and that this integration can yield robust goal-based action execution on robots in open worlds.
Searching Objects in Large-Scale Indoor Environments: A Decision-Theoretic ApproachMany of today's mobile robots are supposed to perform everyday manipulation tasks autonomously. However, in large-scale environments, a task-related object might be out of the robot's reach. Hence, the robot first has to search for the object in its environment before it can perform the task. In this paper, we present a decision-theoretic approach for searching objects in large-scale environments using probabilistic environment models and utilities associated with object locations. We demonstrate the feasibility of our approach by integrating it into a robot system and by conducting experiments where the robot is supposed to search different objects with various strategies in the context of fetch-and-delivery tasks within a multi-level building.