News

  1. The paper “Socially Competent Navigation Planning by Deep Learning of Multi-Agent Path Topologies”, by Christoforos Mavrogiannis, Valts Blukis, and Ross Knepper has been accepted to IROS 2017.

    From the paper:

    We present a novel, data-driven framework for planning socially competent robot behaviors in crowded environments. The core of our approach is a topological model of collective navigation behaviors, based on braid groups. This model constitutes the basis for the design of a human-inspired probabilistic inference mechanism that predicts the topology of multiple agents’ future trajectories, given observations of the context. We derive an approximation of this mechanism by employing a neural network learning architecture on synthetic data of collective navigation behaviors. Our planner makes use of this mechanism as a tool for interpreting the context and understanding what future behaviors are in compliance with it. The planning agent makes use of this understanding to determine a personal action that contributes to the context in the most clear way possible, while ensuring progress to its destination. Our simulations provide evidence that our planning framework results in socially competent navigation behaviors not only for the planning agent, but also for interacting naive agents. Performance benefits include (1) early conflict resolutions and (2) faster uncertainty decrease for the other agents in the scene.

  2. Second-year Ph.D. student Wil Thomason was awarded a four-year fellowship through the National Defense Science and Engineering Graduate Fellowship. He also received a three-year fellowship through the National Science Foundation’s Graduate Research Fellowship Program.

  3. The paper “Implicit Communication in a Joint Action”, by Ross Knepper, Christoforos Mavrogiannis, Julia Proft, and Claire Liang, has been nominated for a Best Paper Award at HRI 2017.

    For more on the paper, please see the announcement of its acceptance.

  4. The paper “Implicit Communication in a Joint Action”, by Ross Knepper, Christoforos Mavrogiannis, Julia Proft, and Claire Liang, has been accepted to HRI 2017.

    From the paper:

    Actions performed in the context of a joint activity comprise two aspects: functional and communicative. The functional component achieves the goal of the action, whereas its communicative component, when present, expresses some information to the actor’s partners in the joint activity. The interpretation of such communication requires leveraging information that is public to all participants, known as common ground. Much of human communication is performed through this implicit mechanism, and humans cannot help but infer some meaning — whether or not it was intended by the actor — from most actions. Robots must be cognizant of how their actions will be interpreted in context. We present a framework for robots to utilize this communicative channel on top of normal functional actions to work more effectively with human partners. We consider the role of the actor and the observer, both individually and jointly, in implicit communication, as well as the effects of timing. We also show how the framework maps onto various modes of action, including natural language and motion. We consider these modes of action in various human-robot interaction domains, including social navigation and collaborative assembly.

  5. The paper “Decentralized Multi-Agent Navigation Planning with Braids”, by Christoforos Mavrogiannis and Ross Knepper, was presented at WAFR 2016.

    From the paper:

    We present a novel planning framework for navigation in dynamic, multi-agent environments with no explicit communication among agents, such as pedestrian scenes. Inspired by the collaborative nature of human navigation, our approach treats the problem as a coordination game, in which players coordinate to avoid each other as they move towards their destinations. We explicitly encode the concept of coordination into the agents’ decision making process through a novel inference mechanism about future joint strategies of avoidance. We represent joint strategies as equivalence classes of topological trajectory patterns using the formalism of braids. This topological representation naturally generalizes to any number of agents and provides the advantage of adaptability to different environments, in contrast to the majority of existing approaches. At every round, the agents simultaneously decide on their next action that contributes collision-free progress towards their destination but also towards a global joint strategy that appears to be in compliance with all agents’ preferences, as inferred from their past behaviors. This policy leads to a smooth and rapid uncertainty decrease regarding the emerging joint strategy that is promising for real world scenarios. Simulation results highlight the importance of reasoning about joint strategies and demonstrate the efficacy of our approach.