Improving robot deployment is a central step towards speeding up robot-based automation in manufacturing. A main challenge in robot deployment is how to best place the robot within the workcell. To tackle this challenge, we combine two knowledge sources: robotic knowledge of the system and workcell context awareness of the user, and intersect them with an Augmented Reality interface. RobotGraffiti is a unique tool that empowers the user in robot deployment tasks. One simply takes a 3D scan of the workcell with their mobile device, adds contextual data points that otherwise would be difficult to infer from the system, and receives a robot base position that satisfies the automation task. The proposed approach is an alternative to expensive and time-consuming digital twins, with a fast and easy-to-use tool that focuses on selected workcell features needed to run the placement optimization algorithm. The main contributions of this paper are the novel user interface for robot base placement data collection and a study comparing the traditional offline simulation with our proposed method. We showcase the method with a robot base placement solution and obtain up to 16 times reduction in time.

Capturing real-world 3D spaces as point clouds is efficient and descriptive, but it comes with sensor errors and lacks object parametrization. These limitations render point clouds unsuitable for various real-world applications, such as robot programming, without extensive post-processing (e.g., outlier removal, semantic segmentation). On the other hand, CAD modeling provides high-quality, parametric representations of 3D space with embedded semantic data, but requires manual component creation that is time-consuming and costly. To address these challenges, we propose a novel solution that combines the strengths of both approaches. Our method for 3D workcell sketching from point clouds allows users to refine raw point clouds using an Augmented Reality (AR) interface that leverages their knowledge and the real-world 3D environment. By utilizing a toolbox and an AR-enabled pointing device, users can enhance point cloud accuracy based on the device’s position in 3D space. We validate our approach by comparing it with ground truth models, demonstrating that it achieves a mean error within 1cm — significant improvement over standard LiDAR scanner apps.

We want to build robots capable of rich social interactions with humans, including natural communication and cooperation. This work explores how imitation as a social learning and teaching process may be applied to building socially intelligent robots, and summarizes our progress toward building a robot capable of learning how to imitate facial expressions from simple imitative games played with a human, using biologically inspired mechanisms. It is possible for the robot to bootstrap from this imitative ability to infer the affective reaction of the human with whom it interacts and then use this affective assessment to guide its subsequent behavior. Our approach is heavily influenced by the ways human infants learn to communicate with their caregivers and come to understand the actions and expressive behavior of others in intentional and motivational terms. Specifically, our approach is guided by the hypothesis that imitative interactions between infant and caregiver, starting with facial mimicry, are a significant stepping-stone to developing appropriate social behavior, to predicting others' actions, and ultimately to understanding people as social beings.

Summary form only given. Recent results from the ethological literature challenge some widely held views about the development of animal behavior and offer new directions for research into the organization of synthetic behavior systems. The process of development is often seen as one of continual refinement, with infantile behaviors representing the primitive precursors of adult behavior. While this notion is intuitively pleasing, there are two ways in which it may be incorrect. First, Coppinger (Coppinger and Smith 1990) argues that infantile behaviors are just as well adapted as adult behaviors, but adapted to the challenges that the organism faces during its early life and early social context. In his view, development is best understood as a process of, 'swapping' elements in the behavioral repertoire rather than refining them; the adult version of a behavior, in other words, is a distinct replacement for its juvenile counterpart, not an outgrowth of it. The second problem with the traditional view of behavioral development is that infantile behaviors are often expressed in an entirely different motivational context than their adult counterparts. Indeed, infantile behaviors that satisfy a particular motivational system such as hunger may be controlled by entirely different motivational systems. Hall and Willams have shown, for example, that during the first several weeks of life, a rat pup's suckling rate is independent of how hungry it is. That is, rat pups suckle because they are innately motivated to do so, not because they are hungry. Similarly, Hogan (Hogan 1999) suggests that during the first 48 hours of a chick's life, the amount that it pecks has nothing to do with its level of hunger. Leyhausen (Lorenz and Leyhausen 1973) has shown that the behaviors that make up the sequence of predatory behaviors in cats are refined through play, often well before the cat ever makes 'the connection' between the acquisition of prey and a subsequent reduction in hunger. What is going on here? In the first two cases, the juvenile behaviors act as 'fail-safe' mechanisms to ensure that the creature's needs are met without needing an innate or learned connection between the behaviors and the critical motivational systems that they act to satisfy. In the latter case, behaviors that will be critically important in adulthood are initially expressed and refined in a context in which the costs of failure are low, i.e. the creature's life doesn't depend on how well they perform the behavior at that given moment. Both of these concerns suggest that by re-examining our assumptions about development in animals, we may be able to glean generally useful organizing principles for the design of adaptive computational systems.

The ability to learn is a potentially compelling and important quality for interactive synthetic characters. To that end, we describe a practical approach to real-time learning for synthetic characters. Our implementation is grounded in the techniques of reinforcement learning and informed by insights from animal training. It simplifies the learning task for characters by (a) enabling them to take advantage of predictable regularities in their world, (b) allowing them to make maximal use of any supervisory signals, and (c) making them easy to train by humans.We built an autonomous animated dog that can be trained with a technique used to train real dogs called "clicker training". Capabilities demonstrated include being trained to recognize and use acoustic patterns as cues for actions, as well as to synthesize new actions from novel paths through its motion space.A key contribution of this paper is to demonstrate that by addressing the three problems of state, action, and state-action space discovery at the same time, the solution for each becomes easier. Finally, we articulate heuristics and design principles that make learning practical for synthetic characters.

This paper presents ways of approaching the design of successful character-based interactive installations. We rationalize our arguments within the context of both Disney's "illusion of life" and Daniel Dennett's "intentional stance". We present six perspectives from which intentional characters can be viewed: as interactors on a variety of time scales; as reciprocal interactors with each other, as entities exhibiting a dynamic expressive range, as creatures with life cycles; as a combination of allusions to existing media, and as a collection of well-balanced components. By conceptualizing characters in these ways, creators can generate installations that enable participants to read the desires, beliefs, and actions of the characters. This approach forms the basis of a successful character-based interactive installation.

Interactive autonomous and directable characters require certain architectural support such as a motor system which moves the character based on decisions made by the behavior system. While previous work has focused on these higher-level systems, we focus on the infrastructure provided by the graphics system. We describe how a graphics system designed with synthetic characters in mind can be behavior friendly, simplifying motor and behavior system construction and making new characters easier to create. The system described in this paper was the underlying support for "Swamped!", an interactive 3D cartoon demonstrated in the Enhanced Realities section of SIGGRAPH 98. We describe how our design and implementation allowed us to achieve real-time performance in this Java application.

We introduce the concept of a sympathetic inter$ace for controlling an animated synthetic character in a 3D virtual environment. A plush doll embedded with wireless sensors is used to manipulate the virtual character in an iconic and intentional manner. The interface extends from the novel physical input device through interpretation of sensor data to the behavioral brain of the virtual character. We discuss the design of the interface and focus on its latest instantiation in the Swamped! exhibit at SIGGRAPH 98. We also present what we learned from hundreds of casual users, who ranged from young children to adults.

The author addresses the role of autonomy in interactive animated characters. He argues that even a small amount of autonomy can lift what is already a great interactive character into a new dimension. He shows how different levels of autonomy may be appropriate for different types of characters. He then argues that autonomy, intentionality, variability and adaptation are all critical components in creating the illusion of life in interactive characters. He reviews current work in the field and proposes a number of practical applications for autonomous characters in both interactive and non-interactive animation.

The cumbersome nature of wired interfaces and the limited nature of the interaction with graphical objects has so far limited the range of application of virtual environments. We discuss the design and implementation of a novel system, called ALIVE, which allows wireless full-body interaction between a human participant and a rich graphical world inhabited by autonomous agents. Based on results obtained with real users, the paper argues that this kind of system can provide more complex and very different experiences than traditional virtual reality systems. The ALIVE system significantly broadens the range of potential applications of virtual reality systems; in particular the paper discusses novel applications in the area of training and teaching, entertainment and last but not least, digital assistants or interface agents.>

There have been several recent efforts to build behavior-based autonomous creatures.While competent autonomous action is highly desirable, there is an important need to integrate autonomy with "directability".In this paper we discuss the problem of building autonomous animated creatures for interactive virtual environments which are also capable of being directed at multiple levels.We present an approach to control which allows an external entity to "direct" an autonomous creature at the motivational level, the task level, and the direct motor level.We also detail a layered architecture and a general behavioral model for perception and action-selection which incorporates explicit support for multi-level direction.These ideas have been implemented and used to develop several autonomous animated creatures.