This work addresses the challenge of a robot using real-time feedback from contact sensors to reliably manipulate a movable object on a cluttered tabletop. We formulate contact manipulation as a partially observable Markov decision process (POMDP) in the joint space of robot configurations and object poses. The POMDP formulation enables the robot to actively gather information and reduce the uncertainty on the object pose. Further, it incorporates all major constraints for robot manipulation: kinematic reachability, self-collision, and collision with obstacles. To solve the POMDP, we apply DESPOT, a state-of-the-art online POMDP algorithm. Our approach leverages two key ideas for computational efficiency. First, it performs lazy construction of a configuration-space lattice by interleaving construction of the lattice and online POMDP planning. Second, it combines online and offline POMDP planning by solving relaxed POMDP offline and using the solution to guide the online search algorithm. We evaluated the proposed approach on a seven degree-of-freedom robot arm in simulation environments. It significantly outperforms several existing algorithms, including some commonly used heuristics for contact manipulation under uncertainty.
Unfortunately, most of this work assumes that the end-effector can move freely in the workspace and that objects do not significantly move when touched. More recent approaches have relaxed the latter assumption by incorporating a stochastic physics model into the POMDP @cite_45 @cite_17 and using SARSOP @cite_34 , an offline point-based @cite_2 POMDP solver, to find a near-optimal policy for manipulating an object relative to the hand. Unfortunately, hand-relative policies often fail when executed on a manipulator due to kinematic constraints or collision with obstacles. We use a hand-relative policy to guide DESPOT @cite_46 , an online POMDP solver @cite_29 , in Lat-POMDP, a mixed-observable model @cite_18 that includes these constraints.
@cite_18 @cite_29 @cite_45 @cite_2 @cite_46 @cite_34 @cite_17
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The partially observable Markov decision process (POMDP) provides a principled general framework for planning under uncertainty, but solving POMDPs optimally is computationally intractable, due to the "curse of dimensionality" and the "curse of history". To overcome these challenges, we introduce the Determinized Sparse Partially Observable Tree (DESPOT), a sparse approximation of the standard belief tree, for online planning under uncertainty. A DESPOT focuses online planning on a set of randomly sampled scenarios and compactly captures the "execution" of all policies under these scenarios. We show that the best policy obtained from a DESPOT is near-optimal, with a regret bound that depends on the representation size of the optimal policy. Leveraging this result, we give an anytime online planning algorithm, which searches a DESPOT for a policy that optimizes a regularized objective function. Regularization balances the estimated value of a policy under the sampled scenarios and the policy size, thus avoiding overfitting. The algorithm demonstrates strong experimental results, compared with some of the best online POMDP algorithms available. It has also been incorporated into an autonomous driving system for real-time vehicle control. The source code for the algorithm is available online. Previous research indicates that during the past 20 years, the highest-quality work has been published in an increasingly diverse and larger group of journals. In this article, we examine whether this diversification has also affected the handful of elite journals that are traditionally considered to be the best. We examine citation patterns during the past 40 years of seven long-standing traditionally elite journals and six journals that have been increasing in importance during the past 20 years. To be among the top 5 or 1 cited papers, papers now need about twice as many citations as they did 40 years ago. Since the late 1980s and early 1990s, elite journals have been publishing a decreasing proportion of these top-cited papers. This also applies to the two journals that are typically considered as the top venues and often used as bibliometric indicators of "excellence": Science and Nature. On the other hand, several new and established journals are publishing an increasing proportion of the most-cited papers. These changes bring new challenges and opportunities for all parties. Journals can enact policies to increase or maintain their relative position in the journal hierarchy. Researchers now have the option to publish in more diverse venues knowing that their work can still reach the same audiences. Finally, evaluators and administrators need to know that although there will always be a certain prestige associated with publishing in "elite" journals, journal hierarchies are in constant flux. POMDPs provide a principled framework for planning under uncertainty, but are computationally intractable, due to the "curse of dimensionality" and the "curse of history". This paper presents an online POMDP algorithm that alleviates these difficulties by focusing the search on a set of randomly sampled scenarios. A Determinized Sparse Partially Observable Tree (DESPOT) compactly captures the execution of all policies on these scenarios. Our Regularized DESPOT (R-DESPOT) algorithm searches the DESPOT for a policy, while optimally balancing the size of the policy and its estimated value obtained under the sampled scenarios. We give an output-sensitive performance bound for all policies derived from a DESPOT, and show that R-DESPOT works well if a small optimal policy exists. We also give an anytime algorithm that approximates R-DESPOT. Experiments show strong results, compared with two of the fastest online POMDP algorithms. Source code along with experimental settings are available at http: bigbird.comp.nus.edu.sg pmwiki farm appl . A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.