Author name cluster

Robin Schmucker

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

AAAI Conference 2021 Conference Paper

Bandit Linear Optimization for Sequential Decision Making and Extensive-Form Games

Gabriele Farina
Robin Schmucker
Tuomas Sandholm

Tree-form sequential decision making (TFSDM) extends classical one-shot decision making by modeling tree-form interactions between an agent and a potentially adversarial environment. It captures the online decision-making problems that each player faces in an extensive-form game, as well as Markov decision processes and partially-observable Markov decision processes where the agent conditions on observed history. Over the past decade, there has been considerable effort into designing online optimization methods for TFSDM. Virtually all of that work has been in the full-feedback setting, where the agent has access to counterfactuals, that is, information on what would have happened had the agent chosen a different action at any decision node. Little is known about the bandit setting, where that assumption is reversed (no counterfactual information is available), despite this latter setting being well understood for almost 20 years in one-shot decision making. In this paper, we give the first algorithm for the bandit linear optimization problem for TFSDM that offers both (i) linear-time iterations (in the size of the decision tree) and (ii) O( √ T) cumulative regret in expectation compared to any fixed strategy, at all times T. This is made possible by new results that we derive, which may have independent uses as well: 1) geometry of the dilated entropy regularizer, 2) autocorrelation matrix of the natural sampling scheme for sequence-form strategies, 3) construction of an unbiased estimator for linear losses for sequence-form strategies, and 4) a refined regret analysis for mirror descent when using the dilated entropy regularizer.

PDF Details

AAMAS Conference 2018 Conference Paper

Towards a Robust Interactive and Learning Social Robot

Michiel De Jong
Kevin Zhang
Aaron M. Roth
Travers Rhodes
Robin Schmucker
Chenghui Zhou
Sofia Ferreira
Jo�o Cartucho

Pepper is a humanoid robot, specifically designed for social interaction, that has been deployed in a variety of public environments. A programmable version of Pepper is also available, enabling our focused research on perception and behavior robustness and capabilities of an interactive social robot. We address Pepper perception by integrating state-of-the-art vision and speech recognition systems and experimentally analyzing their effectiveness. As we recognize limitations of the individual perceptual modalities, we introduce a multi-modality approach to increase the robustness of human social interaction with the robot. We combine vision, gesture, speech, and input from an onboard tablet, a remote mobile phone, and external microphones. Our approach includes the proactive seeking of input from a different modality, adding robustness to the failures of the separate components. We also introduce a learning algorithm to improve communication capabilities over time, updating speech recognition through social interactions. Finally, we realize the rich robot body-sensory data and introduce both a nearest-neighbor and a deep learning approach to enable Pepper to classify and speak up a variety of its own body motions. We view the contributions of our work to be relevant both to Pepper specifically and to other general social robots.

PDF