Cambridge, MA, United States
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Tellex S.,32 Vassar St | Thaker P.,32 Vassar St | Joseph J.,32 Vassar St | Roy N.,32 Vassar St
Machine Learning | Year: 2014

In order for robots to effectively understand natural language commands, they must be able to acquire meaning representations that can be mapped to perceptual features in the external world. Previous approaches to learning these grounded meaning representations require detailed annotations at training time. In this paper, we present an approach to grounded language acquisition which is capable of jointly learning a policy for following natural language commands such as "Pick up the tire pallet," as well as a mapping between specific phrases in the language and aspects of the external world; for example the mapping between the words "the tire pallet" and a specific object in the environment. Our approach assumes a parametric form for the policy that the robot uses to choose actions in response to a natural language command that factors based on the structure of the language. We use a gradient method to optimize model parameters. Our evaluation demonstrates the effectiveness of the model on a corpus of commands given to a robotic forklift by untrained users. © 2013 The Author(s).

Dong S.,362 Memorial Dr. and 203 | Williams B.,32 Vassar St.
International Journal of Social Robotics | Year: 2012

For robots to work effectively with humans, they must learn and recognize activities that humans perform. We enable a robot to learn a library of activities from user demonstrations and use it to recognize an action performed by an operator in real time. Our contributions are threefold: (1) a novel probabilistic flow tube representation that can intuitively capture a wide range of motions and can be used to support compliant execution; (2) a method to identify the relevant features of a motion, and ensure that the learned representation preserves these features in new and unforeseen situations; (3) a fast incremental algorithm for recognizing user-performed motions using this representation. Our approach provides several capabilities beyond those of existing algorithms. First, we leverage temporal information to model motions that may exhibit non-Markovian characteristics. Second, our approach can identify parameters of a motion not explicitly specified by the user. Third, we model hybrid continuous and discrete motions in a unified representation that avoids abstracting out the continuous details of the data. Experimental results show a 49 % improvement over prior art in recognition rate for varying environments, and a 24 % improvement for a static environment, while maintaining average computing times for incremental recognition of less than half of human reaction time. We also demonstrate motion learning and recognition capabilities on real-world robot platforms. © 2012 Springer Science & Business Media BV.

Conrad P.R.,32 Vassar St | Williams B.C.,32 Vassar St
Journal of Artificial Intelligence Research | Year: 2011

This work presents Drake, a dynamic executive for temporal plans with choice. Dy- namic plan execution strategies allow an autonomous agent to react quickly to unfolding events, improving the robustness of the agent. Prior work developed methods for dynami- cally dispatching Simple Temporal Networks, and further research enriched the expressive- ness of the plans executives could handle, including discrete choices, which are the focus of this work. However, in some approaches to date, these additional choices induce signiffcant storage or latency requirements to make exible execution possible. Drake is designed to leverage the low latency made possible by a preprocessing step called compilation, while avoiding high memory costs through a compact representation. We leverage the concepts of labels and environments, taken from prior work in Assumption- based Truth Maintenance Systems (ATMS), to concisely record the implications of the discrete choices, exploiting the structure of the plan to avoid redundant reasoning or stor- age. Our labeling and maintenance scheme, called the Labeled Value Set Maintenance System, is distinguished by its focus on properties fundamental to temporal problems, and, more generally, weighted graph algorithms. In particular, the maintenance system focuses on maintaining a minimal representation of non-dominated constraints. We benchmark Drake's performance on random structured problems, and ffnd that Drake reduces the size of the compiled representation by a factor of over 500 for large problems, while incurring only a modest increase in run-time latency, compared to prior work in compiled executives for temporal plans with discrete choices. © 2011 AI Access Foundation. All rights reserved.

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