Robotic agents that share autonomy with a human should leverage human domain knowledge and account for their preferences when completing a task. This extra knowledge can dramatically improve plan efficiency and user-satisfaction, but these gains are lost if communicating with a robot is taxing and unnatural. In this paper, we show how viewing humanrobot language through the lens of shared autonomy explains the efficiency versus cognitive load trade-offs humans make when deciding how cooperative and explicit to make their instructions.
Language interaction provides useful information in solving many of the challenges in shared autonomy, which include how to correctly and accurately identify a human's intent through interaction @cite_11 and observation @cite_19 . Low-level control strategies from humans have also been successfully integrated and applied to search terrain @cite_12 . Recent research aggressively incorporates high-level human information in shared autonomy @cite_1 @cite_18 . Language, which is a direct and natural way for a human to share information in collaboration, has been widely researched within the supervisor" paradigm @cite_17 @cite_5 @cite_21 where only a goal is provided or where the human acts as a programmer" @cite_2 @cite_23 @cite_15 that instructs the agent in tedious detail. Balancing these roles and investigating user preferences is still an open challenge for deploying communication in shared autonomy.
@cite_18 @cite_21 @cite_1 @cite_17 @cite_19 @cite_23 @cite_2 @cite_5 @cite_15 @cite_12 @cite_11
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In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the goal from user input and assist with the task. Such methods tend to assume some combination of knowledge of the dynamics of the environment, the user's policy given their goal, and the set of possible goals the user might target, which limits their application to real-world scenarios. We propose a deep reinforcement learning framework for model-free shared autonomy that lifts these assumptions. We use human-in-the-loop reinforcement learning with neural network function approximation to learn an end-to-end mapping from environmental observation and user input to agent action, with task reward as the only form of supervision. Controlled studies with users (n = 16) and synthetic pilots playing a video game and flying a real quadrotor demonstrate the ability of our algorithm to assist users with real-time control tasks in which the agent cannot directly access the user's private information through observations, but receives a reward signal and user input that both depend on the user's intent. The agent learns to assist the user without access to this private information, implicitly inferring it from the user's input. This allows the assisted user to complete the task more effectively than the user or an autonomous agent could on their own. This paper is a proof of concept that illustrates the potential for deep reinforcement learning to enable flexible and practical assistive systems. In situated human-robot dialogue, although humans and robots are co-present in a shared environment, they have significantly mismatched capabilities in perceiving the shared environment. Their representations of the shared world are misaligned. In order for humans and robots to communicate with each other successfully using language, it is important for them to mediate such differences and to establish common ground. To address this issue, this paper describes a dialogue system that aims to mediate a shared perceptual basis during human-robot dialogue. In particular, we present an empirical study that examines the role of the robot’s collaborative effort and the performance of natural language processing modules in dialogue grounding. Our empirical results indicate that in situated human-robot dialogue, a low collaborative effort from the robot may lead its human partner to believe a common ground is established. However, such beliefs may not reflect true mutual understanding. To support truly grounded dialogues, the robot should make an extra effort by making its partner aware of its internal representation of the shared world. Shared expectations and mutual understanding are critical facets of teamwork. Achieving these in human-robot collaborative contexts can be especially challenging, as humans and robots are unlikely to share a common language to convey intentions, plans, or justifications. Even in cases where human co-workers can inspect a robot's control code, and particularly when statistical methods are used to encode control policies, there is no guarantee that meaningful insights into a robot's behavior can be derived or that a human will be able to efficiently isolate the behaviors relevant to the interaction. We present a series of algorithms and an accompanying system that enables robots to autonomously synthesize policy descriptions and respond to both general and targeted queries by human collaborators. We demonstrate applicability to a variety of robot controller types including those that utilize conditional logic, tabular reinforcement learning, and deep reinforcement learning, synthesizing informative policy descriptions for collaborators and facilitating fault diagnosis by non-experts. The goal of this work is to develop computational models of social intelligence that enable robots to work side by side with humans, solving problems and achieving task goals through dialogue and collaborative manipulation. A defining problem of collaborative behavior in an embodied setting is the manner in which multiple agents make use of shared resources. In a situated dialogue, these resources include physical bottlenecks such as objects or spatial regions, and cognitive bottlenecks such as the speaking floor. For a robot to function as an effective collaborative partner with a human, it must be able to seize and yield such resources appropriately according to social expectations. We describe a general framework that uses timed Petri nets for the modeling and execution of robot speech, gaze, gesture, and manipulation for collaboration. The system dynamically monitors resource requirements and availability to control real-time turn-taking decisions over resources that are shared with humans, reasoning about different resource types independently. We evaluate our approach with an experiment in which our robot Simon performs a collaborative assembly task with 26 different human partners, showing that the multimodal reciprocal approach results in superior task performance, fluency, and balance of control. Abstract In multi-agent systems, the study of language and communication is an active field of research. In this paper we present the application of Reinforcement Learning (RL) to the self-emergence of a common lexicon in robot teams. By modeling the vocabulary or lexicon of each agent as an association matrix or look-up table that maps the meanings (i.e. the objects encountered by the robots or the states of the environment itself) into symbols or signals we check whether it is possible for the robot team to converge in an autonomous, decentralized way to a common lexicon by means of RL, so that the communication efficiency of the entire robot team is optimal. We have conducted several experiments aimed at testing whether it is possible to converge with RL to an optimal Saussurean Communication System. We have organized our experiments alongside two main lines: first, we have investigated the effect of the team size centered on teams of moderated size in the order of 5 and 10 individuals, typical of multi-robot systems. Second, and foremost, we have also investigated the effect of the lexicon size on the convergence results. To analyze the convergence of the robot team we have defined the team’s consensus when all the robots (i.e. 100 of the population) share the same association matrix or lexicon. As a general conclusion we have shown that RL allows the convergence to lexicon consensus in a population of autonomous agents. Spoken language is one of the most intuitive forms of interaction between humans and agents. Unfortunately, agents that interact with people using natural language often experience communication errors and do not correctly understand the user's intentions. Recent systems have successfully used probabilistic models of speech, language and user behaviour to generate robust dialogue performance in the presence of noisy speech recognition and ambiguous language choices, but decisions made using these probabilistic models are still prone to errors owing to the complexity of acquiring and maintaining a complete model of human language and behaviour. In this paper, a decision-theoretic model for human-robot interaction using natural language is described. The algorithm is based on the Partially Observable Markov Decision Process (POMDP), which allows agents to choose actions that are robust not only to uncertainty from noisy or ambiguous speech recognition but also unknown user models. Like most dialogue systems, a POMDP is defined by a large number of parameters that may be difficult to specify a priori from domain knowledge, and learning these parameters from the user may require an unacceptably long training period. An extension to the POMDP model is described that allows the agent to acquire a linguistic model of the user online, including new vocabulary and word choice preferences. The approach not only avoids a training period of constant questioning as the agent learns, but also allows the agent actively to query for additional information when its uncertainty suggests a high risk of mistakes. The approach is demonstrated both in simulation and on a natural language interaction system for a robotic wheelchair application. In human-computer collaboration involving haptics, a key issue that remains to be solved is to establish an intuitive communication between the partners. Even though computers are widely used to aid human operators in teleoperation, guidance, and training, because they lack the adaptability, versatility, and awareness of a human, their ability to improve efficiency and effectiveness in dynamic tasks is limited. We suggest that the communication between a human and a computer can be improved if it involves a decision-making process in which the computer is programmed to infer the intentions of the human operator and dynamically adjust the control levels of the interacting parties to facilitate a more intuitive interaction setup. In this paper, we investigate the utility of such a dynamic role exchange mechanism, where partners negotiate through the haptic channel to trade their control levels on a collaborative task. We examine the energy consumption, the work done on the manipulated object, and the joint efficiency in addition to the task performance. We show that when compared to an equal control condition, a role exchange mechanism improves task performance and the joint efficiency of the partners. We also show that augmenting the system with additional informative visual and vibrotactile cues, which are used to display the state of interaction, allows the users to become aware of the underlying role exchange mechanism and utilize it in favor of the task. These cues also improve the user's sense of interaction and reinforce his her belief that the computer aids with the execution of the task. Human-centered computing (HCC) involves conforming computer technology to humans while naturally achieving human-machine interaction. In a human-centered system, the interaction focuses on human requirements, capabilities, and limitations. These anthropocentric systems also focus on the consideration of human sensory-motor skills in a wide range of activities. This ensures that the interface between artificial agents and human users accounts for perception and action in a novel interaction paradigm. In turn, this leads to behavior understanding through cognitive models that allow content description and, ultimately, the integration of real and virtual worlds. Our work focuses on building a language that maps to the lower-level sensory and motor languages and to the higher-level natural language. An empirically demonstrated human activity language provides sensory-motor-grounded representations for understanding human actions. A linguistic framework allows the analysis and synthesis of these actions. The paper presents a knowledge representation formalism, in the form of a high-level Action Description Language (ADL) for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specications of the portion of the problem pertaining a single agent. Each of these specications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog. To appear in Theory and Practice of Logic Programming (TPLP). Research partially funded by GNCS-INdAM projects, MUR-PRIN: Innovative and multidisciplinary approaches for constraint and preference reasoning project; NSF grants IIS-0812267 and HRD-0420407; and grants 2009.010.0336 and 2010.011.0403. Natural human-robot interaction requires different and more robust models of language understanding (NLU) than non-embodied NLU systems. In particular, architectures are required that (1) process language incrementally in order to be able to provide early backchannel feedback to human speakers; (2) use pragmatic contexts throughout the understanding process to infer missing information; and (3) handle the underspecified, fragmentary, or otherwise ungrammatical utterances that are common in spontaneous speech. In this paper, we describe our attempts at developing an integrated natural language understanding architecture for HRI, and demonstrate its novel capabilities using challenging data collected in human-human interaction experiments. A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, all learned without any human supervision! In this paper, using a Task and Tell reference game between two agents as a testbed, we present a sequence of 'negative' results culminating in a 'positive' one -- showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional. In essence, we find that natural language does not emerge 'naturally', despite the semblance of ease of natural-language-emergence that one may gather from recent literature. We discuss how it is possible to coax the invented languages to become more and more human-like and compositional by increasing restrictions on how two agents may communicate. Agent interaction takes place at several levels. Current work in the ARPA Knowledge Sharing Effort has addressed the information content level by the KIF language and the intentional level by the KQML language. In this paper we address the coordination level by means of our Coordination Language (COOL) that relies on speech act based communication, but integrates it in a structured conversation framework that captures the coordination mechanisms agents use when working together. We are currently using this language (i) to represent coordination mechanisms for the supply chain of manufacturing enterprises modeled as intelligent agents and (ii) as an environment for designing and validating coordination protocols for multi-agent systems. This paper describes the basic elements of this language: conversation objects, conversation rules, error recovery rules, continuation rules, conversation nesting. The actual COOL source code and a running trace for the n-queens problem are presented in the Appendix. Topic areas: Coordination, Intelligent agents in enterprise integration In shared autonomy, user input and robot autonomy are combined to control a robot to achieve a goal. Often, the robot does not know a priori which goal the user wants to achieve, and must both predict the user's intended goal, and assist in achieving that goal. We formulate the problem of shared autonomy as a Partially Observable Markov Decision Process with uncertainty over the user's goal. We utilize maximum entropy inverse optimal control to estimate a distribution over the user's goal based on the history of inputs. Ideally, the robot assists the user by solving for an action which minimizes the expected cost-to-go for the (unknown) goal. As solving the POMDP to select the optimal action is intractable, we use hindsight optimization to approximate the solution. In a user study, we compare our method to a standard predict-then-blend approach. We find that our method enables users to accomplish tasks more quickly while utilizing less input. However, when asked to rate each system, users were mixed in their assessment, citing a tradeoff between maintaining control authority and accomplishing tasks quickly. Our goal is to build robots that can robustly interact with humans using natural language. This problem is challenging because human language is filled with ambiguity, and furthermore, due to limitations in sensing, the robot's perception of its environment might be much more limited than that of its human partner. To enable a robot to recover from a failure to understand a natural language utterance, this paper describes an information-theoretic strategy for asking targeted clarifying questions and using information from the answer to disambiguate the language. To identify good questions, we derive an estimate of the robot's uncertainty about the mapping between specific phrases in the language and aspects of the external world. This metric enables the robot to ask a targeted question about the parts of the language for which it is most uncertain. After receiving an answer, the robot fuses information from the command, the question, and the answer in a joint probabilistic graphical model in the G3 framework. When using answers to questions, we show the robot is able to infer mappings between parts of the language and concrete object groundings in the external world with higher accuracy than by using information from the command alone. Furthermore, we demonstrate that by effectively selecting which questions to ask, the robot is able to achieve significant performance gains while asking many fewer questions than baseline metrics. As with human?human interaction, spoken human?computer dialog will contain situations where there is miscommunication. One natural strategy for reducing the impact of miscommunication is selective verification of the user utterance meanings. This paper reports on both context-independent and context-dependent strategies for utterance verification that show that the use of dialog context can be very helpful in selecting which utterances to verify. Simulations with data collected during experimental trials with the Circuit Fix-It Shop spoken natural language dialog system are used in the analysis. In addition, the performance of various selection strategies is measured separately for computer-controlled and user-controlled dialogs and general guidelines for selecting an appropriate strategy are presented. Our human-robot collaboration research aims to improve the fluency and efficiency of interactions between humans and robots when executing a set of tasks in a shared workspace. During human-robot collaboration, a robot and a user must often complete a disjoint set of tasks that use an overlapping set of objects, without using the same object simultaneously. A key challenge is deciding what task the robot should perform next in order to facilitate fluent and efficient collaboration. Most prior work does so by first predicting the human's intended goal, and then selecting actions given that goal. However, it is often difficult, and sometimes impossible, to infer the human's exact goal in real time, and this serial predict-then-act method is not adaptive to changes in human goals. In this paper, we present a system for inferring a probability distribution over human goals, and producing assistance actions given that distribution in real time. The aim is to minimize the disruption caused by the nature of human-robot shared workspace. We extend recent work utilizing Partially Observable Markov Decision Processes (POMDPs) for shared autonomy in order to provide assistance without knowing the exact goal. We evaluate our system in a study with 28 participants, and show that our POMDP model outperforms state of the art predict-then-act models by producing fewer human-robot collisions and less human idling time. In language-based interaction between a human and an artificial agent (e.g., robot) in a physical world, because the human and the agent have different knowledge and capabilities in perceiving the shared environment, referential grounding is very difficult. To facilitate such interaction, it is important for the agent to continuously learn and acquire knowledge about the environment through interactions with humans and incorporate the learned knowledge in grounding references from human utterances. To address this issue, this paper presents a graph-based approach for referential grounding and examines how referential grounding and word acquisition influence each other in physical world interaction. Our empirical results have shown that for most words, automated word acquisition through interaction improves referential grounding performance. However, this is not the case for words describing object types, where human supervision is important. Nevertheless, better referential grounding enables more accurate acquisition of word meanings, which in turn further improves grounding performance for references in subsequent utterances. Here, we propose that bidirectionality in implicit motor coordination between humanoid robots and humans could enhance the social competence of human–robot interactions. We first detail some questions pertaining to human–robot interactions, introducing the Uncanny Valley hypothesis. After introducing a framework pertinent for the understanding of natural social interactions, motor resonance, we examine two behaviors derived from this framework: motor coordination, investigated in and informative about human–human interaction, and motor interference, which demonstrate the relevance of the motor resonance framework to describe human perception of humanoid robots. These two lines of investigation are then put together to “close the loop” by proposing to implement a key feature of motor coordination, bidirectionality, in robots’ behavior. Finally, we discuss the feasibility of implementing motor coordination between humanoid robots and humans, and the consequences of this implementation in enhancing the social competence of robots interacting with humans. Keywords: interpersonal interaction, motor resonance, motor coordination, motor interference, social robotics, anthropomorphism The use of spoken instructions in human-robot teams holds the promise of enabling untrained users to effectively control complex robotic systems in a natural and intuitive way. Providing robots with the capability to understand natural language directions would enable effortless coordination in human robot teams that operate in non-specialized unknown environments. However, natural language direction following through unknown environments requires understanding the meaning of language, using a partial semantic world model to generate actions in the world, and reasoning about the environment and landmarks that have not yet been detected. We address the problem of robots following natural language directions through complex unknown environments. By exploiting the structure of spatial language, we can frame direction following as a problem of sequential decision making under uncertainty. We learn a policy which predicts a sequence of actions that follow the directions by exploring the environment and discovering landmarks, backtracking when necessary, and explicitly declaring when it has reached the destination. We use imitation learning to train the policy, using demonstrations of people following directions. By training explicitly in unknown environments, we can generalize to situations that have not been encountered previously. Speaking using unconstrained natural language is an intuitive and flexible way for humans to interact with robots. Understanding this kind of linguistic input is challenging because diverse words and phrases must be mapped into structures that the robot can understand, and elements in those structures must be grounded in an uncertain environment. We present a system that follows natural language directions by extracting a sequence of spatial description clauses from the linguistic input and then infers the most probable path through the environment given only information about the environmental geometry and detected visible objects. We use a probabilistic graphical model that factors into three key components. The first component grounds landmark phrases such as "the computers" in the perceptual frame of the robot by exploiting co-occurrence statistics from a database of tagged images such as Flickr. Second, a spatial reasoning component judges how well spatial relations such as "past the computers" describe a path. Finally, verb phrases such as "turn right" are modeled according to the amount of change in orientation in the path. Our system follows 60 of the directions in our corpus to within 15 meters of the true destination, significantly outperforming other approaches.