Reinforcement Learning from Human Feedback (RLHF) is a methodology that aligns agent behavior with human preferences by integrating human feedback into the agent's training process. We introduce a possible framework that employs passive Brain-Computer Interfaces (BCI) to guide agent training from implicit neural signals. We present and release a novel dataset of functional near-infrared spectroscopy (fNIRS) recordings collected from 25 human participants across three domains: a Pick-and-Place Robot, Lunar Lander, and Flappy Bird. We train classifiers to predict levels of agent performance (optimal, sub-optimal, or worst-case) from windows of preprocessed fNIRS feature vectors, achieving an average F1 score of 67% for binary classification and 46% for multi-class models averaged across conditions and domains. We also train regressors to predict the degree of deviation between an agent's chosen action and a set of near-optimal policies, providing a continuous measure of performance. We evaluate cross-subject generalization and demonstrate that fine-tuning pre-trained models with a small sample of subject-specific data increases average F1 scores by 17% and 41% for binary and multi-class models, respectively. Our work demonstrates that mapping implicit fNIRS signals to agent performance is feasible and can be improved, laying the foundation for future brain-driven RLHF systems.

With the rise of Artificial Intelligence (AI) systems in society, our children have routine interactions with these technologies. It has become increasingly important for them to understand how these technologies are trained, what their limitations are and how they work. To introduce children to AI and Machine Learning (ML) concepts, recent efforts introduce tools that integrate ML concepts with physical computing and robotics. However, some of these tools cannot be easily integrated into building projects and the high price of robotics kits can be a limiting factor to many schools. We address these limitations by offering a low-cost hardware and software toolkit that we call the Smart Motor to introduce supervised machine learning to elementary school students. Our Smart Motor uses the nearest neighbor algorithm and utilizes visualizations to highlight the underlying decision-making of the model. We conducted a one week long study using Smart Motors with 9- to 12- year old students and measured their learning through observation, questioning and examining what they built. We found that students were able to integrate the Smart Motors into their building projects but some students struggled with understanding how the underlying model functioned. In this paper we discuss these findings and insights for future directions for the Smart Motor.

Infusing artificial intelligence algorithms into production aerospace systems can be challenging due to costs, timelines, and a risk-averse industry. We introduce the Onboard Artificial Intelligence Research (OnAIR) platform, an open-source software pipeline and cognitive architecture tool that enables full life cycle AI research for on-board intelligent systems. We begin with a description and user walk-through of the OnAIR tool. Next, we describe four use cases of OnAIR for both research and deployed onboard applications, detailing their use of OnAIR and the benefits it provided to the development and function of each respective scenario. We conclude with remarks on future work, future planned deployments, and goals for the forward progression of OnAIR as a tool to enable a larger AI and aerospace research community.

Learning to manipulate objects efficiently, particularly those involving sustained contact (e.g., pushing, sliding) and articulated parts (e.g., drawers, doors), presents significant challenges. Traditional methods, such as robot-centric reinforce-ment learning (RL), imitation learning, and hybrid techniques, require massive training and often struggle to generalize across different objects and robot platforms. We propose a novel framework for learning object-centric manipulation policies in force space, decoupling the robot from the object. By directly applying forces to selected regions of the object, our method simplifies the action space, reduces unnecessary exploration, and decreases simulation overhead. This approach, trained in simulation on a small set of representative objects, captures ob-ject dynamics—such as joint configurations—allowing policies to generalize effectively to new, unseen objects. Decoupling these policies from robot-specific dynamics enables direct transfer to different robotic platforms (e.g., Kinova, Panda, URS) with-out retraining. Our evaluations demonstrate that the method significantly outperforms baselines, achieving over an order of magnitude improvement in training efficiency compared to other state-of-the-art methods. Additionally, operating in force space enhances policy transferability across diverse robot plat-forms and object types. We further showcase the applicability of our method in a real-world robotic setting. Link: https://tufts-ai-robotics-group.github.io/FLEX/

People use tools to interact with and perceive the world, with multimodal sensory inputs forming the basis of how we understand our environment. For example, a blind person uses a walking cane to tap the road and detect obstacles, and a builder uses a hammer to strike a wall to assess its structural integrity. Using tools extends our sensory capabilities during exploratory behaviors, enabling us to perceive object properties that are otherwise inaccessible. Inspired by this cognitive process, we propose a framework in which a multisensory robot employs exploratory behaviors using various tools to recognize granular substances. Our framework effectively integrates multiple non-visual sensory inputs (e.g., audio, haptic, and tactile) gathered through multiple tools (e.g., spoon, fork) and behaviors (e.g., stirring, poking) to perceive object properties. The framework segments interactions into time windows and aligns different modalities, enhancing data efficiency and interactive perception. Additionally, we conducted tool-transfer experiments to evaluate similarities between tools. Our experiments demonstrate that combining multiple tools and behaviors outperforms single-tool and singlebehavior approaches. While the audio modality dominates the non-visual multimodal system, other modalities contribute. We further demonstrate that tool similarities vary depending on the behavior, and notably, the robot does not need to complete entire interactions to achieve optimal recognition accuracy.

Reinforcement Learning (RL) has made significant strides in enabling artificial agents to learn diverse behaviors. However, learning an effective policy often requires a large number of environment interactions. To mitigate sample complexity issues, recent approaches have used high-level task specifications, such as Linear Temporal Logic (LTLf) formulas or Reward Machines (RM), to guide the learning progress of the agent. In this work, we propose a novel approach, called Logical Specifications-guided Dynamic Task Sampling (LSTS), that learns a set of RL policies to guide an agent from an initial state to a goal state based on a high-level task specification, while minimizing the number of environmental interactions. Unlike previous work, LSTS does not assume information about the environment dynamics or the Reward Machine, and dynamically samples promising tasks that lead to successful goal policies. We evaluate LSTS on a gridworld and show that it achieves improved time-to-threshold performance on complex sequential decision-making problems compared to state-of-the-art RM and Automaton-guided RL baselines, such as Q-Learning for Reward Machines and Compositional RL from logical Specifications (DIRL). Moreover, we demonstrate that our method outperforms RM and Automaton-guided RL baselines in terms of sample-efficiency, both in a partially observable robotic task and in a continuous control robotic manipulation task.

A holistic understanding of object properties across diverse sensory modalities (e.g., visual, audio, and haptic) is essential for tasks ranging from object categorization to complex manipulation. Drawing inspiration from cognitive science studies that emphasize the significance of multi-sensory integration in human perception, we introduce MOSAIC (Multimodal Object property learning with Self-Attention and Interactive Comprehension), a novel framework designed to facilitate the learning of unified multi-sensory object property representations. While it is undeniable that visual information plays a prominent role, we acknowledge that many fundamental object properties extend beyond the visual domain to encompass attributes like texture, mass distribution, or sounds, which significantly influence how we interact with objects. In MOSAIC, we leverage this profound insight by distilling knowledge from multimodal foundation models and aligning these representations not only across vision but also haptic and auditory sensory modalities. Through extensive experiments on a dataset where a humanoid robot interacts with 100 objects across 10 exploratory behaviors, we demonstrate the versatility of MOSAIC in two task families: object categorization and object-fetching tasks. Our results underscore the efficacy of MOSAIC's unified representations, showing competitive performance in category recognition through a simple linear probe setup and excelling in the fetch object task under zero-shot transfer conditions. This work pioneers the application of sensory grounding in foundation models for robotics, promising a significant leap in multi-sensory perception capabilities for autonomous systems. We have released the code, datasets, and additional results: https://github.com/gtatiya/MOSAIC.

People effortlessly manipulate fluids due to their learned understanding of fluid dynamics, while robots struggle with complex fluid dynamic calculations, particularly in tasks like pouring. To enhance assistive robots in such tasks, we propose involving users in correcting and providing feedback by visualizing the planned pouring trajectories before they are executed. This paper investigates whether people can predict robotic pouring outcomes and make adjustments to minimize spills, using visualization devices like augmented reality. In a human-participant study, participants evaluated and adjusted robot pouring behaviors of unique configurations for various source containers. Results highlight the effectiveness of visualization tools such as augmented reality headsets, as well as traditional 2D display, especially with specific pouring parameters, and users noted their benefits in open-ended responses. This research illuminates the potential for human-robot collaboration in fluid manipulation tasks, with visualization tools reducing spills in robot-controlled pours.

Humans leverage multiple sensor modalities when interacting with objects and discovering their intrinsic properties. Using the visual modality alone is insufficient for deriving intuition behind object properties (e.g., which of two boxes is heavier), making it essential to consider non-visual modalities as well, such as the tactile and auditory. Whereas robots may leverage various modalities to obtain object property understanding via learned exploratory interactions with objects (e.g., grasping, lifting, and shaking behaviors), challenges remain: the implicit knowledge acquired by one robot via object exploration cannot be directly leveraged by another robot with different morphology, because the sensor models, observed data distributions, and interaction capabilities are different across these different robot configurations. To avoid the costly process of learning interactive object perception tasks from scratch, we propose a multi-stage projection framework for each new robot for transferring implicit knowledge of object properties across heterogeneous robot morphologies. We evaluate our approach on the object-property recognition and object-identity recognition tasks, using a dataset containing two heterogeneous robots that perform 7,600 object interactions. Results indicate that knowledge can be transferred across robots, such that a newly-deployed robot can bootstrap its recognition models without exhaustively exploring all objects. We also propose a data augmentation technique and show that this technique improves the generalization of models. We release code, datasets, and additional results, here: https://github.com/gtatiya/Implicit-Knowledge-Transfer.

Creative Problem Solving (CPS) is a sub-area within artificial intelligence that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning in AI, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways, remains a challenge. To stimulate further research in CPS, we contribute a definition and a framework of CPS, which we use to categorize existing AI methods in this field. We conclude our survey with open research questions, and suggested future directions.

Despite recent progress in Reinforcement Learning for robotics applications, many tasks remain prohibitively difficult to solve because of the expensive interaction cost. Transfer learning helps reduce the training time in the target domain by transferring knowledge learned in a source domain. Sim2Real transfer helps transfer knowledge from a simulated robotic domain to a physical target domain. Knowledge transfer reduces the time required to train a task in the physical world, where the cost of interactions is high. However, most existing approaches assume exact correspondence in the task structure and the physical properties of the two domains. This work proposes a framework for Few-Shot Policy Transfer between two domains through Observation Mapping and Behavior Cloning. We use Generative Adversarial Networks (GANs) along with a cycle-consistency loss to map the observations between the source and target domains and later use this learned mapping to clone the successful source task behavior policy to the target domain. We observe successful behavior policy transfer with limited target task interactions and in cases where the source and target task are semantically dissimilar.

Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems. Despite many advancements in planning and learning, resolving novel problems or adapting existing knowledge to a new context, especially in cases where the environment may change in unpredictable ways post deployment, remains a limiting factor in the safe and useful integration of intelligent systems. The emergence of increasingly autonomous systems dictates the necessity for AI agents to deal with environmental uncertainty through creativity. To stimulate further research in CPS, we present a definition and a framework of CPS, which we adopt to categorize existing AI methods in this field. Our framework consists of four main components of a CPS problem, namely, 1) problem formulation, 2) knowledge representation, 3) method of knowledge manipulation, and 4) method of evaluation. We conclude our survey with open research questions, and suggested directions for the future.

Predicting future sensory states is crucial for learning agents such as robots, drones, and autonomous vehicles. In this paper, we couple multiple sensory modalities with exploratory actions and propose a predictive neural network architecture to address this problem. Most existing approaches rely on large, manually annotated datasets, or only use visual data as a single modality. In contrast, the unsupervised method presented here uses multi-modal perceptions for predicting future visual frames. As a result, the proposed model is more comprehensive and can better capture the spatio-temporal dynamics of the environment, leading to more accurate visual frame prediction. The other novelty of our framework is the use of sub-networks dedicated to anticipating future haptic, audio, and tactile signals. The framework was tested and validated with a dataset containing 4 sensory modalities (vision, haptic, audio, and tactile) on a humanoid robot performing 9 behaviors multiple times on a large set of objects. While the visual information is the dominant modality, utilizing the additional non-visual modalities improves the accuracy of predictions.

Humans learn about object properties using multiple modes of perception. Recent advances show that robots can use non-visual sensory modalities (i.e., haptic and tactile sensory data) coupled with exploratory behaviors (i.e., grasping, lifting, pushing, dropping, etc.) for learning objects' properties such as shape, weight, material and affordances. However, non-visual sensory representations cannot be easily transferred from one robot to another, as different robots have different bodies and sensors. Therefore, each robot needs to learn its task-specific sensory models from scratch. To address this challenge, we propose a framework for knowledge transfer using kernel manifold alignment (KEMA) that enables source robots to transfer haptic knowledge about objects to a target robot. The idea behind our approach is to learn a common latent space from multiple robots' feature spaces produced by respective sensory data while interacting with objects. To test the method, we used a dataset in which 3 simulated robots interacted with 25 objects and showed that our framework speeds up haptic object recognition and allows novel object recognition.

In this work, we present methods for using human-robot dialog to improve language understanding for a mobile robot agent. The agent parses natural language to underlying semantic meanings and uses robotic sensors to create multi-modal models of perceptual concepts like red and heavy. The agent can be used for showing navigation routes, delivering objects to people, and relocating objects from one location to another. We use dialog clari_cation questions both to understand commands and to generate additional parsing training data. The agent employs opportunistic active learning to select questions about how words relate to objects, improving its understanding of perceptual concepts. We evaluated this agent on Amazon Mechanical Turk. After training on data induced from conversations, the agent reduced the number of dialog questions it asked while receiving higher usability ratings. Additionally, we demonstrated the agent on a robotic platform, where it learned new perceptual concepts on the y while completing a real-world task.

Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over the past three decades, learning in many domains still requires a large amount of interaction with the environment, which can be prohibitively expensive in realistic scenarios. To address this problem, transfer learning has been applied to reinforcement learning such that experience gained in one task can be leveraged when starting to learn the next, harder task. More recently, several lines of research have explored how tasks, or data samples themselves, can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch. In this article, we present a framework for curriculum learning (CL) in reinforcement learning, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals. Finally, we use our framework to find open problems and suggest directions for future RL curriculum learning research.

This paper outlines the system design, capabilities and potential applications of an Augmented Reality (AR) framework developed for Robot Operating System (ROS) powered robots. The goal of this framework is to enable high-level human-robot collaboration and interaction. It allows the users to visualize the robot's state in intuitive modalities overlaid onto the real world and interact with AR objects as a means of communication with the robot. Thereby creating a shared environment in which humans and robots can interact and collaborate.

When identifying an object and its properties, humans use features from multiple sensory modalities produced when manipulating the object. Motivated by this cognitive process, we propose a deep learning methodology for object category recognition which uses visual, auditory, and haptic sensory data coupled with exploratory behaviors (e.g., grasping, lifting, pushing, etc.). In our method, as the robot performs an action on an object, it uses a Tensor-Train Gated Recurrent Unit network to process its visual data, and Convolutional Neural Networks to process haptic and auditory data. We propose a novel strategy to train a single neural network that inputs video, audio and haptic data, and demonstrate that its performance is better than separate neural networks for each sensory modality. The proposed method was evaluated on a dataset in which the robot explored 100 different objects, each belonging to one of 20 categories. While the visual information was the dominant modality for most categories, adding the additional haptic and auditory networks further improves the robot's category recognition accuracy. For some of the behaviors, our approach outperforms the previous published baseline for the dataset which used handcrafted features for each modality. We also show that a robot does not need the sensory data from the entire interaction, but instead can make a good prediction early on during behavior execution.

Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept words like red can to physical object properties. One way to alleviate this engineering for a new domain is to enable robots in human environments to adapt dynamically-continually learning new language constructions and perceptual concepts. In this work, we present an end-to-end pipeline for translating natural language commands to discrete robot actions, and use clarification dialogs to jointly improve language parsing and concept grounding. We train and evaluate this agent in a virtual setting on Amazon Mechanical Turk, and we transfer the learned agent to a physical robot platform to demonstrate it in the real world.

A major goal of grounded language learning research is to enable robots to connect language predicates to a robot's physical interactive perception of the world. Coupling object exploratory behaviors such as grasping, lifting, and looking with multiple sensory modalities (e.g., audio, haptics, and vision) enables a robot to ground non-visual words like ``heavy'' as well as visual words like ``red''. A major limitation of existing approaches to multi-modal language grounding is that a robot has to exhaustively explore training objects with a variety of actions when learning a new such language predicate. This paper proposes a method for guiding a robot's behavioral exploration policy when learning a novel predicate based on known grounded predicates and the novel predicate's linguistic relationship to them. We demonstrate our approach on two datasets in which a robot explored large sets of objects and was tasked with learning to recognize whether novel words applied to those objects.

Intelligent robots frequently need to explore the objects in their working environments. Modern sensors have enabled robots to learn object properties via perception of multiple modalities. However, object exploration in the real world poses a challenging trade-off between information gains and exploration action costs. Mixed observability Markov decision process (MOMDP) is a framework for planning under uncertainty, while accounting for both fully and partially observable components of the state. Robot perception frequently has to face such mixed observability. This work enables a robot equipped with an arm to dynamically construct query-oriented MOMDPs for multi-modal predicate identification (MPI) of objects. The robot's behavioral policy is learned from two datasets collected using real robots. Our approach enables a robot to explore object properties in a way that is significantly faster while improving accuracies in comparison to existing methods that rely on hand-coded exploration strategies.

When mobile robots navigate crowded, human-populated environments, the potential for conflict arises in the form of intersecting trajectories. This study investigates the use of light-emitting diodes (LEDs) arranged along the chassis of a robot in an arrangement similar to a turn signal on a car as a non-anthropomorphic, yet familiar signal to convey the intended path of a mobile service robot. We study the scenario of a human and a robot heading directly toward each other in a hallway, which may give rise to the familiar human experience in which both parties step to the right, then the left, then the right, continuing to block each other's paths until they are able to coordinate their movements and pass each other. We conducted a pilot study which revealed that people do not always interpret this signal as one may expect, which would be similar to how a car uses its turn signal. This motivated a 2 × 2 experiment in which the robot either does or does not use LEDs to indicate its intended direction of travel, and in which study participants either are able to or unable to witness the robot's “lane-changing” behavior further down the hallway prior to coming into direct proximal contact with the robot. The results demonstrate that exposing participants to the robot's use of the LED signal only once prior to passing each other in the hallway is sufficient to disambiguate its meaning to the user, and thus greatly enhances its utility in-situ, with no direct instruction or training to the user. These findings suggest a paradigm of passive demonstration of such signals in future applications.

In recent years, research has shown that transfer learning methods can be leveraged to construct curricula that sequence a series of simpler tasks such that performance on a final target task is improved. A major limitation of existing approaches is that such curricula are handcrafted by humans that are typically domain experts. To address this limitation, we introduce a method to generate a curriculum based on task descriptors and a novel metric of transfer potential. Our method automatically generates a curriculum as a directed acyclic graph (as opposed to a linear sequence as done in existing work). Experiments in both discrete and continuous domains show that our method produces curricula that improve the agent's learning performance when compared to the baseline condition of learning on the target task from scratch.

Transfer learning is a method where an agent reuses knowledge learned in a source task to improve learning on a target task. Recent work has shown that transfer learning can be extended to the idea of curriculum learning, where the agent incrementally accumulates knowledge over a sequence of tasks (i.e. a curriculum). In most existing work, such curricula have been constructed manually. Furthermore, they are fixed ahead of time, and do not adapt to the progress or abilities of the agent. In this paper, we formulate the design of a curriculum as a Markov Decision Process, which directly models the accumulation of knowledge as an agent interacts with tasks, and propose a method that approximates an execution of an optimal policy in this MDP to produce an agent-specific curriculum. We use our approach to automatically sequence tasks for 3 agents with varying sensing and action capabilities in an experimental domain, and show that our method produces curricula customized for each agent that improve performance relative to learning from scratch or using a different agent's curriculum.

This paper proposes a framework for learning human-provided category labels that describe individual objects, pairwise object relationships, as well as groups of objects. The framework was evaluated using an experiment in which the robot interactively explored 36 objects that varied by color, weight, and contents. The proposed method allowed the robot not only to learn categories describing individual objects, but also to learn categories describing pairs and groups of objects with high recognition accuracy. Furthermore, by grounding the category representations in its own sensorimotor repertoire, the robot was able to estimate how similar two categories are in terms of the behaviors and sensory modalities that are used to recognize them. Finally, this grounded measure of similarity enabled the robot to boost its recognition performance when learning a new category by relating it to a set of familiar categories.

This paper proposes a theoretical model that enables a robot to partition its unlabeled sensorimotor experience with different objects into discrete clusters, each corresponding to a specific object. To solve this object individuation problem, the robot was trained to detect whether two perceptual stimuli were produced by the same object or by two different objects. The model was tested using a large-scale experiment in which a humanoid robot explored 100 different objects by performing a variety of exploratory behaviors on them and detecting the resulting sensory feedback from several sensory modalities. The results show that with a small amount of prior training, the robot's model was able to successfully individuate the objects with a high degree of accuracy.

The ability to form and recognize object categories is fundamental to human intelligence. This paper proposes a behavior-grounded relational classification model that allows a robot to recognize the categories of household objects. In the proposed approach, the robot initially explores the objects by applying five exploratory behaviors (lift, shake, drop, crush and push) on them while recording the proprioceptive and auditory sensory feedback produced by each interaction. The sensorimotor data is used to estimate multiple measures of similarity between the objects, each corresponding to a specific coupling between an exploratory behavior and a sensory modality. A graph-based recognition model is trained by extracting features from the estimated similarity relations, allowing the robot to recognize the category memberships of a novel object based on the object's similarity to the set of familiar objects. The framework was evaluated on an upper-torso humanoid robot with two large sets of household objects. The results show that the robot's model is able to recognize complex object categories (e.g., metal objects, empty bottles, etc.) significantly better than chance.

This paper proposes a method for interactive surface recognition and surface categorization by a humanoid robot using a vibrotactile sensory modality. The robot was equipped with an artificial fingernail that had a built-in three-axis accelerometer. The robot interacted with 20 different surfaces by performing five different exploratory scratching behaviors on them. Surface-recognition models were learned by coupling frequency-domain analysis of the vibrations detected by the accelerometer with machine learning algorithms, such as support vector machine (SVM) and k-nearest neighbors (k -NN). The results show that by applying several different scratching behaviors on a test surface, the robot can recognize surfaces better than with any single behavior alone. The robot was also able to estimate a measure of similarity between any two surfaces, which was used to construct a grounded hierarchical surface categorization.

Active object exploration is one of the hallmarks of human and animal intelligence. Research in psychology has shown that the use of multiple exploratory behaviors is crucial for learning about objects. Inspired by such research, recent work in robotics has demonstrated that by performing multiple exploratory behaviors a robot can dramatically improve its object recognition rate. But what is the cause of this improvement? To answer this question, this paper examines the conditions under which combining information from multiple behaviors and sensory modalities leads to better object recognition results. Two different problems are considered: interactive object recognition using auditory and proprioceptive feedback, and surface texture recognition using tactile and proprioceptive feedback. Analysis of the results shows that metrics designed to estimate classifier model diversity can explain the improvement in recognition accuracy. This finding establishes, for the first time, an important link between empirical studies of exploratory behaviors in robotics and theoretical results on boosting in machine learning.

This paper describes an approach to interactive object categorization that couples exploratory behaviors and their resulting acoustic signatures to form object categories. The framework was tested with an upper-torso humanoid robot on a container/non-container categorization task. The robot used six exploratory behaviors (drop block, grasp, move, shake, flip, and drop object) and applied them to twenty objects. The results from this large-scale experimental study show that the robot was able to learn meaningful object categories using only acoustic information. The results also show that the quality of the categorization depends on the exploratory behavior used to derive it as some behaviors elicit more salient acoustic signatures than others.

Human beings can perceive object properties such as size, weight, and material type based solely on the sounds that the objects make when an action is performed on them. In order to be successful, the household robots of the near future must also be capable of learning and reasoning about the acoustic properties of everyday objects. Such an ability would allow a robot to detect and classify various interactions with objects that occur outside of the robot's field of view. This paper presents a framework that allows a robot to infer the object and the type of behavioral interaction performed with it from the sounds generated by the object during the interaction. The framework is evaluated on a 7-d.o.f. Barrett WAM robot which performs grasping, shaking, dropping, pushing and tapping behaviors on 36 different household objects. The results show that the robot can learn models that can be used to recognize objects (and behaviors performed on objects) from the sounds generated during the interaction. In addition, the robot can use the learned models to estimate the similarity between two objects in terms of their acoustic properties.

BACKGROUND: Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences. RESULTS: We present a mixture of experts approach to biomolecular sequence labeling that takes into account the global similarity between biomolecular sequences. Our approach combines unsupervised and supervised learning techniques. Given a set of sequences and a similarity measure defined on pairs of sequences, we learn a mixture of experts model by using spectral clustering to learn the hierarchical structure of the model and by using bayesian techniques to combine the predictions of the experts. We evaluate our approach on two biomolecular sequence labeling problems: RNA-protein and DNA-protein interface prediction problems. The results of our experiments show that global sequence similarity can be exploited to improve the performance of classifiers trained to label biomolecular sequence data. CONCLUSION: The mixture of experts model helps improve the performance of machine learning methods for identifying functionally important sites in biomolecular sequences.

Identifying functionally important sites from biological sequences, formulated as a biological sequence labeling problem, has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. In this paper, we present an approach to biological sequence labeling that takes into account the global similarity between biological sequences. Our approach combines unsupervised and supervised learning techniques. Given a set of sequences and a similarity measure defined on pairs of sequences, we learn a mixture of experts model by using spectral clustering to learn the hierarchical structure of the model and by using bayesian approaches to combine the predictions of the experts. We evaluate our approach on two important biological sequence labeling problems: RNA-protein and DNA-protein interface prediction problems. The results of our experiments show that global sequence similarity can be exploited to improve the performance of classifiers trained to label biological sequence data.

Machine learning approaches offer some of the most cost-effective approaches to building predictive models (e.g., classifiers) in a broad range of applications in computational biology. Comparing the effectiveness of different algorithms requires reliable procedures for accurately assessing the performance (e.g., accuracy, sensitivity, and specificity) of the resulting predictive classifiers. The difficulty of this task is compounded by the use of different data selection and evaluation procedures and in some cases, even different definitions for the same performance measures. We explore the problem of assessing the performance of predictive classifiers trained on macromolecular sequence data, with an emphasis on cross-validation and data selection methods. Specifically, we compare sequence-based and window-based cross-validation procedures on three sequence-based prediction tasks: identification of glycosylation sites, RNA-Protein interface residues, and Protein-Protein interface residues from amino acid sequence. Our experiments with two representative classifiers (Naive Bayes and Support Vector Machine) show that sequence-based and windows-based cross-validation procedures and data selection methods can yield different estimates of commonly used performance measures such as accuracy, Matthews correlation coefficient and area under the Receiver Operating Characteristic curve. We argue that the performance estimates obtained using sequence-based cross-validation provide more realistic estimates of performance than those obtained using window-based cross-validation.

BACKGROUND: Glycosylation is one of the most complex post-translational modifications (PTMs) of proteins in eukaryotic cells. Glycosylation plays an important role in biological processes ranging from protein folding and subcellular localization, to ligand recognition and cell-cell interactions. Experimental identification of glycosylation sites is expensive and laborious. Hence, there is significant interest in the development of computational methods for reliable prediction of glycosylation sites from amino acid sequences. RESULTS: We explore machine learning methods for training classifiers to predict the amino acid residues that are likely to be glycosylated using information derived from the target amino acid residue and its sequence neighbors. We compare the performance of Support Vector Machine classifiers and ensembles of Support Vector Machine classifiers trained on a dataset of experimentally determined N-linked, O-linked, and C-linked glycosylation sites extracted from O-GlycBase version 6.00, a database of 242 proteins from several different species. The results of our experiments show that the ensembles of Support Vector Machine classifiers outperform single Support Vector Machine classifiers on the problem of predicting glycosylation sites in terms of a range of standard measures for comparing the performance of classifiers. The resulting methods have been implemented in EnsembleGly, a web server for glycosylation site prediction. CONCLUSION: Ensembles of Support Vector Machine classifiers offer an accurate and reliable approach to automated identification of putative glycosylation sites in glycoprotein sequences.