More Like this

1. A Framework to Study Human-AI Collaborative Design Space Exploration

   https://doi.org/10.1115/DETC2021-67619  

   Viros-i-Martin, Antoni; Selva, Daniel (August 2021, Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.)

   Abstract This paper presents a framework to describe and explain human-machine collaborative design focusing on Design Space Exploration (DSE), which is a popular method used in the early design of complex systems with roots in the well-known design as exploration paradigm. The human designer and a cognitive design assistant are both modeled as intelligent agents, with an internal state (e.g., motivation, cognitive workload), a knowledge state (separated in domain, design process, and problem specific knowledge), an estimated state of the world (i.e., status of the design task) and of the other agent, a hierarchy of goals (short-term and long-term, design and learning goals) and a set of long-term attributes (e.g., Kirton’s Adaption-Innovation inventory style, risk aversion). The framework emphasizes the relation between design goals and learning goals in DSE, as previously highlighted in the literature (e.g., Concept-Knowledge theory, LinD model) and builds upon the theory of common ground from human-computer interaction (e.g., shared goals, plans, attention) as a building block to develop successful assistants and interactions. Recent studies in human-AI collaborative DSE are reviewed from the lens of the proposed framework, and some new research questions are identified. This framework can help advance the theory of human-AI collaborative design by helping design researchers build promising hypotheses, and design studies to test these hypotheses that consider most relevant factors. 

   more » « less
2. A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans

   Peng, Bei; MacGlashan, James; Loftin, Robert; Littman, Michael L.; Roberts, David L.; Taylor, Matthew E. (January 2016, AAMAS)

   As robots become pervasive in human environments, it is important to enable users to effectively convey new skills without programming. Most existing work on Interactive Reinforcement Learning focuses on interpreting and incorporating non-expert human feedback to speed up learning; we aim to design a better representation of the learning agent that is able to elicit more natural and effective communication between the human trainer and the learner, while treating human feedback as discrete communication that depends probabilistically on the trainer’s target policy. This work entails a user study where participants train a virtual agent to accomplish tasks by giving reward and/or punishment in a variety of simulated environments. We present results from 60 participants to show how a learner can ground natural language commands and adapt its action execution speed to learn more efficiently from human trainers. The agent’s action execution speed can be successfully modulated to encourage more explicit feedback from a human trainer in areas of the state space where there is high uncertainty. Our results show that our novel adaptive speed agent dominates different fixed speed agents on several measures of performance. Additionally, we investigate the impact of instructions on user performance and user preference in training conditions. 

   more » « less
3. A Need for Speed: Adapting Agent Action Speed to Improve Task Learning from Non-Expert Humans

   Peng, Bei; MacGlashan, James; Loftin, Robert; Littman, Michael L.; Roberts, David L.; Taylor, Matthew E. (July 2016, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems)

   As robots become pervasive in human environments, it is important to enable users to effectively convey new skills without programming. Most existing work on Interactive Reinforcement Learning focuses on interpreting and incorporating non-expert human feedback to speed up learning; we aim to design a better representation of the learning agent that is able to elicit more natural and effective communication between the human trainer and the learner, while treating human feedback as discrete communication that depends probabilistically on the trainer's target policy. This work entails a user study where participants train a virtual agent to accomplish tasks by giving reward and/or punishment in a variety of simulated environments. We present results from 60 participants to show how a learner can ground natural language commands and adapt its action execution speed to learn more efficiently from human trainers. The agent's action execution speed can be successfully modulated to encourage more explicit feedback from a human trainer in areas of the state space where there is high uncertainty. Our results show that our novel adaptive speed agent dominates different fixed speed agents on several measures of performance. Additionally, we investigate the impact of instructions on user performance and user preference in training conditions. 

   more » « less
4. Figaro: A Tabletop Authoring Environment for Human-Robot Interaction

   Porfirio, D.; Stegner, L.; Cakmak, M.; Sauppé, A.; Albarghouthi, A.; Mutlu, B. (May 2021, Human factors in computing systems)

   null (Ed.)

   Human-robot interaction designers and developers navigate a complex design space, which creates a need for tools that support intuitive design processes and harness the programming capacity of state-of-the-art authoring environments. We introduce Figaro, an expressive tabletop authoring environment for mobile robots, inspired by shadow puppetry, that provides designers with a natural, situated representation of human-robot interactions while exploiting the intuitiveness of tabletop and tangible programming interfaces. On the tabletop, Figaro projects a representation of an environment. Users demonstrate sequences of behaviors, or scenes, of an interaction by manipulating instrumented figurines that represent the robot and the human. During a scene, Figaro records the movement of figurines on the tabletop and narrations uttered by users. Subsequently, Figaro employs real-time program synthesis to assemble a complete robot program from all scenes provided. Through a user study, we demonstrate the ability of Figaro to support design exploration and development for human-robot interaction. 

   more » « less
5. PackIt: A Virtual Environment for Geometric Planning

   Goyal, Ankit; Deng, Jia (January 2020, International Conference on Machine Leaning (ICML))

   null (Ed.)

   The ability to jointly understand the geometry of objects and plan actions for manipulating them is crucial for intelligent agents. We refer to this ability as geometric planning. Recently, many interactive environments have been proposed to evaluate intelligent agents on various skills, however, none of them cater to the needs of geometric planning. We present PackIt, a virtual environment to evaluate and potentially learn the ability to do geometric planning, where an agent needs to take a sequence of actions to pack a set of objects into a box with limited space. We also construct a set of challenging packing tasks using an evolutionary algorithm. Further, we study various baselines for the task that include model-free learning-based and heuristic-based methods, as well as search-based optimization methods that assume access to the model of the environment. Code and data are available at this https URL. 

   more » « less