Interactive Robot Learning of Gestures, Language and Affordances (original) (raw)
Related papers
If robots are to cooperate with humans in an increasingly human-like manner, then significant progress must be made in their abilities to observe and learn to perform novel goal directed actions in a flexible and adaptive manner. The current research addresses this challenge. In CHRIS.I [1], we developed a platform-independent perceptual system that learns from observation to recognize human actions in a way which abstracted from the specifics of the robotic platform, learning actions including "put X on Y" and "take X". In the current research, we extend this system from action perception to execution, consistent with current developmental research in human understanding of goal directed action and teleological reasoning. We demonstrate the platform independence with experiments on three different robots. In Experiments 1 and 2 we complete our previous study of perception of actions "put" and "take" demonstrating how the system learns to execute these same actions, along with new related actions "cover" and "uncover" based on the composition of action primitives "grasp X" and "release X at Y". Significantly, these compositional action execution specifications learned on one iCub robot are then executed on another, based on the abstraction layer of motor primitives. Experiment 3 further validates the platformindependence of the system, as a new action that is learned on the iCub in Lyon is then executed on the Jido robot in Toulouse. In Experiment 4 we extended the definition of action perception to include the notion of agency, again inspired by developmental studies of agency attribution, exploiting the Kinect motion capture system for tracking human motion.
Unsupervised learning of affordance relations on a humanoid robot
… and Information Sciences …, 2009
In this paper, we study how the concepts learned by a robot can be linked to verbal concepts that humans use in language. Specifically, we develop a simple tapping behaviour on the iCub humanoid robot simulator and allow the robot to interact with a set of objects of different types and sizes to learn affordance relations in its environment. The robot records its perception, obtained from a range camera, as a feature vector, before and after applying tapping on an object. We compute effect features by subtracting initial features from final ...
Learning Object Affordances: From Sensory-Motor Coordination to Imitation
IEEE Transactions on Robotics, 2008
Affordances encode relationships between actions, objects and effects. They play an important role on basic cognitive capabilities such as prediction and planning. We address the problem of learning affordances through the interaction of a robot with the environment, a key step to understand the world properties and develop social skills. We present a general model for learning object affordances using Bayesian networks integrated within a general developmental architecture for social robots. Since learning is based on a probabilistic model, the approach is able to deal with uncertainty, redundancy and irrelevant information. We demonstrate successful learning in the real world by having an humanoid robot interacting with objects. We demonstrate the benefits of the acquired knowledge in imitation games.
IEEE Transactions on Autonomous Mental Development, 2012
Robots should be capable of interacting in a cooperative and adaptive manner with their human counterparts in open-ended tasks that can change in real-time. An important aspect of the robot behavior will be the ability to acquire new knowledge of the cooperative tasks by observing and interacting with humans. The current research addresses this challenge. We present results from a cooperative human–robot interaction system that has been specifically developed for portability between different humanoid platforms, by abstraction layers at the perceptual and motor interfaces. In the perceptual domain, the resulting system is demonstrated to learn to recognize objects and to recognize actions as sequences of perceptual primitives, and to transfer this learning, and recognition, between different robotic platforms. For execution, composite actions and plans are shown to be learnt on one robot and executed successfully on a different one. Most importantly, the system provides the ability to link actions into shared plans, that form the basis of human–robot cooperation, applying principles from human cognitive development to the domain of robot cognitive systems.
Beyond the Self: Using Grounded Affordances to Interpret and Describe Others’ Actions
IEEE Transactions on Cognitive and Developmental Systems, 2019
We propose a developmental approach that allows a robot to interpret and describe the actions of human agents by reusing previous experience. The robot first learns the association between words and object affordances by manipulating the objects in its environment. It then uses this information to learn a mapping between its own actions and those performed by a human in a shared environment. It finally fuses the information from these two models to interpret and describe human actions in light of its own experience. In our experiments, we show that the model can be used flexibly to do inference on different aspects of the scene. We can predict the effects of an action on the basis of object properties. We can revise the belief that a certain action occurred, given the observed effects of the human action. In an early action recognition fashion, we can anticipate the effects when the action has only been partially observed. By estimating the probability of words given the evidence and feeding them into a pre-defined grammar, we can generate relevant descriptions of the scene. We believe that this is a step towards providing robots with the fundamental skills to engage in social collaboration with humans.
Robot anticipation of human intentions through continuous gesture recognition
Proceedings of the 2013 International Conference on Collaboration Technologies and Systems, CTS 2013, 2013
In this paper, we propose a method to recognize human body movements and we combine it with the contextual knowledge of human-robot collaboration scenarios provided by an object affordances framework that associates actions with its effects and the objects involved in them. The aim is to equip humanoid robots with action prediction capabilities, allowing them to anticipate effects as soon as a human partner starts performing a physical action, thus enabling interactions between man and robot to be fast and natural.
Learning Semantics of Gestural Instructions for Human-Robot Collaboration
Frontiers in neurorobotics, 2018
Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behavior for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the aspect, the robot is competent to predict the human's intent and perform an action without waiting for an instruction. The aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to compl...
In this study, we present a framework that infers human activities from observations using semantic representations. The proposed framework can be utilized to address the difficult and challenging problem of transferring tasks and skills to humanoid robots. We propose a method that allows robots to obtain and determine a higher-level understanding of a demonstrator’s behavior via semantic representations. This abstraction from observations captures the “essence” of the activity, thereby indicating which aspect of the demonstrator’s actions should be executed in order to accomplish the required activity. Thus, a meaningful semantic description is obtained in terms of human motions and object properties. In addition, we validated the semantic rules obtained in different conditions, i.e., three different and complex kitchen activities: 1) making a pancake; 2) making a sandwich; and 3) setting the table. We present quantitative and qualitative results, which demonstrate that without any further training, our system can deal with time restrictions, different execution styles of the same task by several participants, and different labeling strategies. This means, the rules obtained from one scenario are still valid even for new situations, which demonstrates that the inferred representations do not depend on the task performed. The results show that our system correctly recognized human behaviors in real-time in around 87.44% of cases, which was even better than a random participant recognizing the behaviors of another human (about 76.68%). In particular, the semantic rules acquired can be used to effectively improve the dynamic growth of the ontology-base knowledge representation. Hence, this method can be used flexibly across different demonstrations and constraints to infer and achieve a similar goal to that observed. Furthermore, the inference capability introduced in this study was integrated into a joint space control loop for a humanoid robot, an iCub, for achieving similar goals to the human demonstrator online.
Human-style interaction with a robot for cooperative learning of scene objects
Proceedings of the 7th …, 2005
In research on human-robot interaction the interest is currently shifting from uni-modal dialog systems to multi-modal interaction schemes. We present a system for human-style interaction with a robot that is integrated on our mobile robot BIRON. To model the dialog we adopt an extended grounding concept with a mechanism to handle multi-modal in-and output where object references are resolved by the interaction with an object attention system (OAS). The OAS integrates multiple input from, e.g., the object and gesture recognition systems and provides the information for a common representation. This representation can be accessed by both modules and combines symbolic verbal attributes with sensor-based features. We argue that such a representation is necessary to achieve a robust and efficient information processing.