We contribute a study benchmarking the performance of multiple motion-based learning from demonstration approaches. Given the number and diversity of existing methods, it is critical that comprehensive empirical studies be performed comparing the relative strengths of these techniques. In particular, we evaluate four approaches based on properties an end user may desire for real-world tasks. To perform this evaluation, we collected data from nine participants, across four manipulation tasks. The resulting demonstrations were used to train 180 task models and evaluated on 720 task reproductions on a physical robot. Our results detail how i) complexity of the task, ii) the expertise of the human demonstrator, and iii) the starting configuration of the robot affect task performance. The collected dataset of demonstrations, robot executions, and evaluations are publicly available. Research insights and guidelines are also provided to guide future research and deployment choices about these approaches.

We address the challenge of how a robot can adapt its actions to successfully manipulate objects it has not previously encountered. We introduce Real-time Multisensory Affordance-based Control (RMAC), which enables a robot to adapt existing affordance models using multisensory inputs. We show that using the combination of haptic, audio, and visual information with RMAC allows the robot to learn afforance models and adaptively manipulate two very different objects (drawer, lamp), in multiple novel configurations. Offline evaluations and real-time online evaluations show that RMAC allows the robot to accurately open different drawer configurations and turn-on novel lamps with an average accuracy of 75%.

In realistic environments, fully specifying a task model such that a robot can perform a task in all situations is impractical. In this work, we present Incremental Task Modification via Corrective Demonstrations (ITMCD), a novel algorithm that allows a robot to update a learned model by making use of corrective demonstrations from an end-user in its environment. We propose three different types of model updates that make structural changes to a finite state automaton (FSA) representation of the task by first converting the FSA into a state transition auto-regressive hidden Markov model (STARHMM). The STARHMM's probabilistic properties are then used to perform approximate Bayesian model selection to choose the best model update, if any. We evaluate ITMCD Model Selection in a simulated block sorting domain and the full algorithm on a real-world pouring task. The simulation results show our approach can choose new task models that sufficiently incorporate new demonstrations while remaining as simple as possible. The results from the pouring task show that ITMCD performs well when the modeled segments of the corrective demonstrations closely comply with the original task model.

Our work focuses on robots to be deployed in human environments. These robots, which will need specialized object manipulation skills, should leverage end-users to efficiently learn the affordances of objects in their environment. This approach is promising because people naturally focus on showing salient aspects of the objects [1]. We replicate prior results and build on them to create a combination of self and supervised learning. We present experimental results with a robot learning 5 affordances on 4 objects using 1219 interactions. We compare three conditions: (1) learning through self-exploration, (2) learning from supervised examples provided by 10 naïve users, and (3) self-exploration biased by the user input. Our results characterize the benefits of self and supervised affordance learning and show that a combined approach is the most efficient and successful.

We present a system for learning haptic affordance models of complex manipulation skills. The goal of a haptic affordance model is to improve task completion by characterizing the feel of a particular object-action pair. We use learning from demonstration to provide the robot with an example of a successful interaction with a given object. We then use environmental scaffolding and a wrist-mounted force/torque (F/T) sensor to collect grounded examples (successes and unsuccessful “near misses”) of the haptic data for the object-action pair. From this, we build one “success” Hidden Markov Model (HMM) and one “near-miss” HMM for each object-action pair. We evaluate this approach with five different actions on seven different objects to learn two specific affordances (open-able and scoop-able). We show that by building a library of object-action pairs for each affordance, we can successfully monitor a trajectory of haptic data to determine if the robot finds an affordance.

Our goal is to develop robots that naturally engage people in social exchanges. In this paper, we focus on the problem of recognizing that a person is responsive to a robot's request for interaction. Inspired by human cognition, our approach is to treat this as a contingency detection problem. We present a simple discriminative Support Vector Machine (SVM) classifier to compare against previous generative methods introduced in prior work by Lee et al. [1]. We evaluate these methods in two ways. First, by training three separate SVMs with multi-modal sensory input on a set of batch data collected in a controlled setting, where we obtain an average F1score of 0.82. Second, in an open-ended experiment setting with seven participants, we show that our model is able to perform contingency detection in real-time and generalize to new people with a best F1score of 0.72.

Delivering on the promise of real-world robotics will require robots that can communicate with humans through natural language by learning new words and concepts through their daily experiences. Our research strives to create a robot that can learn the meaning of haptic adjectives by directly touching objects. By equipping the PR2 humanoid robot with state-of-the-art biomimetic tactile sensors that measure temperature, pressure, and fingertip deformations, we created a platform uniquely capable of feeling the physical properties of everyday objects. The robot used five exploratory procedures to touch 51 objects that were annotated by human participants with 34 binary adjective labels. We present both static and dynamic learning methods to discover the meaning of these adjectives from the labeled objects, achieving average F1 scores of 0.57 and 0.79 on a set of eight previously unfelt items.

Hand-crafted textual patterns have been the mainstay device of practical relation extraction for decades. However, there has been little work on reducing the manual effort involved in the discovery of effective textual patterns for relation extraction. In this paper, we propose a clustering-based approach to facilitate the pattern discovery for relation extraction. Specifically, we define the notion of semantic signature to represent the most salient features of a textual fragment. We then propose a novel clustering algorithm based on semantic signature, S2C, and its enhancement S2C+. Experiments on two real-world data sets show that, when compared with k-means clustering, S2C and S2C+ are at least an order of magnitude faster, while generating high quality clusters that are at least comparable to the best clusters generated by k-means without requiring any manual tuning. Finally, a user study confirms that our clustering-based approach can indeed help users discover effective textual patterns for relation extraction with only a fraction of the manual effort required by the conventional approach.

Information Extraction (IE)-the problem of extracting structured information from unstructured text - has become the key enabler for many enterprise applications such as semantic search, business analytics and regulatory compliance. While rule-based IE systems are widely used in practice due to their well-known "explainability," developing high-quality information extraction rules is known to be a labor-intensive and time-consuming iterative process.

The primary goal of the Midas project is to build a system that enables easy and scalable integration of unstructured and semi-structured information present across multiple data sources. As a first step in this direction, we have built a system that extracts and integrates information from regulatory filings submitted to the U.S. Securities and Exchange Commission (SEC) and the Federal Deposit Insurance Corporation (FDIC). Midas creates a repository of entities, events, and relationships by extracting, conceptualizing, integrating, and aggregating data from unstructured and semi-structured documents. This repository enables applications to use the extracted and integrated data in a variety of ways including mashups with other public data and complex risk analysis.

Rule-based information extraction from text is increasingly being used to populate databases and to support structured queries on unstructured text. Specification of suitable information extraction rules requires considerable skill and standard practice is to refine rules iteratively, with substantial effort. In this paper, we show that techniques developed in the context of data provenance, to determine the lineage of a tuple in a database, can be leveraged to assist in rule refinement. Specifically, given a set of extraction rules and correct and incorrect extracted data, we have developed a technique to suggest a ranked list of rule modifications that an expert rule specifier can consider. We implemented our technique in the SystemT information extraction system developed at IBM Research -- Almaden and experimentally demonstrate its effectiveness.