Personal Robots

Humans are fundamentally social animals. Why not design cars to leverage this natural propensity for social interaction and understanding? We are working with Audi and the SENSEable City Lab to redefine the relationship between car, driver, and passengers. We are currently developing a new type of in-car system that acts as a partner or friend, providing important information, and intelligently responding to the mood and behavior of the driver.

The Huggable is a new type of robotic companion for health care, education, and social communication applications. The Huggable is much more than a fun, interactive robotic companion; it functions as an essential team member of a triadic interaction. Therefore, the Huggable is not meant to replace any particular person in a social network, but rather to enhance it. The Huggable is being designed with a full-body sensitive skin with over 1500 sensors, quiet back-drivable actuators, video cameras in the eyes, microphones in the ears, an inertial measurement unit, a speaker, and an embedded PC with 802.11g wireless networking. An important design goal for the Huggable is to make the technology invisible to the user. You should not think of the Huggable as a robot but rather as a richly interactive teddy bear.

The goal of this project is to research and test existing compact methods of actuation that are viable for robot applications, and to develop new actuators that will augment the performance of robots intended to interact with people. Metrics for performance include power and force density, controlability, smoothness of motion, ease of implementation, cost, and shape. Current explorations include the use of long-travel voice coils as drop-in replacements for DC motors or pneumatic cylinders, and the development of miniature hydraulic actuators to combine the high force-density of hydraulics with the high power-density of electromagnetic actuators.

We are developing a synthetic skin capable of detecting pressure and location with acceptable resolution over the entire body, while still retaining the look and feel of soft skin. We are particularly interested in having a robot recognize the affective content of touch. We are creating a tactile sensing system where a distributed grid of quantum tunneling composites are placed over the robot's core and under its silicone skin or fur. Using the homunculus distribution of sensing resolution as a guide, we vary the density of sensors so that the robot will have greater resolution in areas that are frequently in contact with objects or people. We are developing a distributed network of tiny processing elements to lie underneath the skin to acquire and process the sensory signals. These sensing elements will cover the entire body of the Huggable.

Communication technologies today fail to produce remote physical presence. By controlling a robotic avatar in a remote location, we can produce this presence. However, Internet latency, synchronization, and the cognitive load of operating a high-tech robot can complicate this interaction process. The Huggable project solves these problems through its unique Web interface that allows for the puppeteering of a multiple DOF robot. This interface empowers the avatar's operator with low-level control (direct manipulation of the robot's limbs) and high-level control (initiating long sequences of actions at the click of a button). The interface communicates how the robot is being interacted with and allows the operator to look through the robot's eyes, speak through its mouth, and hear through its ears; it targets users ranging from grandparents, to grade school teachers, to experts.

The Illustrated Primer is a storytelling system designed to enhance the experience of a parent and child reading a children's story. An animation engine visualizes the story, allowing the storyteller and child novel ways to experience each retelling of the story. The story visually expresses the lexical changes by the storyteller.

The MDS Robot is our new robotics platform, which pushes the limits of existing robotics technology. It synthesizes a novel combination of: (1) mobility—a wheeled base capable of human-speed movement in confined or complex environments; (2) dexterity—a five DOF hand and wrist designed for object manipulation and expressive gesturing; and (3) sociality—a highly expressive face capable of a wide range of human-style facial expressions. The current demonstration highlights the abilities of the MDS robot in a human-robot collaboration scenario.

The Sociable Robots project aims to build capable and appealing robots that can work collaboratively with, communicate with, and learn socially from people. In a unique collaboration with Stan Winston Studio (the creators of "Teddy" in the Kubrick/Spielberg movie A.I.), this project seamlessly merges artistry of character, engineering of robotic technology, and the science of artificial intelligence and psychology to develop robots with social intelligence.

Many new applications for robots require them to work alongside people as capable members of human-robot teams. These include—in the long term—robots for homes, hospitals, and offices, but already exist in more advanced settings, such as space exploration. A robotic member of such a team must be able to work towards a shared goal and be in agreement with the human as to the sequence of actions that will be required to reach that goal, and to adjust dynamically its plan according to the human's actions. We are researching the social and psychological workings of teamwork, and working towards equipping humanoid robots with the social skills needed to perform as useful team members in human-robot teams.

Robotic systems that aim to collaborate effectively with humans in social environments must be able to respond flexibly to the intentions of their human partners. Dynamic environments may further require robots to respond intelligently to the actions of humans with false or incomplete situational beliefs. We are developing an integrated architecture which incorporates simulation-theoretic mechanisms to allow a robot to infer the task-related beliefs and intentions of its interaction partners based on their observable motor behavior and visual perspective. We demonstrate the performance of this architecture on a set of novel benchmark tasks requiring our robot to exhibit appropriate collaborative behaviors in the presence of potentially false beliefs. We compare our results against human performance on similar collaborative tasks.

Robots can currently perform animations, as well as brute imitations of user input (typically given through telemetry devices). More recently, humanoid robots have begun to learn motor tasks through imitation. But, as of yet, no robot has the ability to learn new manipulation skills by watching a user perform those tasks, with any understanding of what the task is accomplishing—the intent of the task. We intend to teach our robot Leonardo how to manipulate objects in goal-directed ways through human demonstration. With a higher-level control system, Leo will be able to watch a user perform a task several times, and slowly take over control of his own body as he gains confidence in the task at hand.

The ability to interpret demonstrations from the teacher's perspective plays a critical role in human learning. Robotic systems that aim to learn effectively from human teachers must similarly be able to engage in perspective taking. We are devloping an integrated architecture wherein the robot's cognitive functionality is organized around the ability to understand the environment from the perspectives of both a social partner and itself. To better understand perspective taking in humans, we are examining its importance in human learning, and have found that it focuses the agent's attention on the subset of the problem space important to the teacher. This constrained attention allows the agent to overcome the ambiguity and incompleteness often present in human demonstrations, thus learning what the teacher intends to teach. We are developing our architecture to use perspective in similar ways, to allow the robot to learn correctly in ambiguous teaching situations.

Social referencing is the tendency to use the emotional reaction of another to help form one's own affective appraisal of a novel situation, which is then used to guide subsequent behavior. It is an important form of emotional communication and is a developmental milestone for human infants in their ability to learn about their environment through social means. We have implemented a biologically inspired computational model of social referencing for our expressive, anthropomorphic robot. Our model consists of three interacting systems: emotional empathy through imitation, a shared attention mechanism, and an affective memory system. These systems interact to enable the robot to demonstrate social referencing behavior similar to that of human infants. This work is an important milestone towards social learning in robots. Additionally, our model presents opportunities for understanding how these mechanisms might interact to enable social referencing behavior in humans.

Learning by human tutelage leverages structure provided through interpersonal interaction. Teachers direct learners' attention, structure experiences, support learning attempts, and regulate the complexity and difficulty of information. Our approach to machine learning takes tutelage as its model. We are studying how social guidance interplays with traditional inference algorithms (such as Bayesian hypothesis testing) in an interactive-learning scenario. In our demonstration, the robot Leonardo pays attention to verbal guidance as well as nonverbal spatial cues that human teachers naturally provide. The robot communicates its current understanding through demonstration and expressive social cues. The human can quickly and effectively help the robot to learn action sequences and secret constraints associated with an interactive construction task. The robot integrates these learned components via hierarchical planning, taking advantage of the human's social guidance to successfully complete various puzzles.

The fusion of "intelligence" and "gentleness" is the foundation of Toyota's partner robot project. Currently, Toyota is focused on implementing a partner robot to enhance the interactive partner robot experience, for example, for patients in hospitals. "Social graces"—the ability of a partner robot to interact with a person in a socially skillful and pleasant manner that is likeable and engaging—is fundamental to the realization of human and partner robot coexistence. To this end, Toyota proposes a collaborative research initiative with the Media Lab to examine how expressive face and neck movements of a partner robot contribute to a human's perception of the "social graces" of a robot using the Media Lab's MDS platform.

The MeBot is a semi-autonomous, robotic, mobile phone avatar that allows the caller to better control its presence in an interactive way in front of a receiving audience. A lot of emphasis is put on the robot conveying the non-verbal channels of social communication. It will take advantage of the current advanced technology in wireless communications and the ever-expanding capabilities of mobile devices. MeBot is a push toward a future where remote presence can be achieved easily in a way that saves traveling time but still achieves the same experience as "being there." We propose to do this by means of robot-mediated presence.

People on the autism spectrum face a number of challenges, including motor movement issues that can cause limbs to cease activity. Circumstantial evidence suggests that autonomic nervous system influences related to stress and overload may arise from and contribute to these problems. We propose allowing individuals to monitor several physiological parameters to see if there are patterns that recognize or predict the onset of their individual motor problems. We plan to develop new, wearable technology to treat these problems via the use of tiny, vibrotactile devices carefully placed at the joints. We hypothesize that some methods of touch-feedback and vibration at the joints may enable individuals to recover motor functioning during episodes of intermittent loss. We are also exploring the development of personally controlled devices that facilitate finer motor movement for augmenting communication as needed for assisting in typing or pointing.

We are studying and implementing a swarm of 36 holonomic-drive robots for use in an upcoming robotic opera. Each robot will eventually comprise roughly eight degrees of freedom, and will follow a centralized control, allowing swarm behaviors as well as pre-scripted paths. In May, a test platform of three 3-DOF robots will be dancing, controlled by an animation with music composed by Tod Machover.

Persuasion is a fundamental part of human-human interaction, though very little is known about how this social concept applies to human-robot interaction (HRI). The goal of truly sociable robots requires a deep understanding of how people perceive and respond to robots across the spectrum of social interaction. A recently completed study at the Museum of Science in Boston explored the way in which people perceive, and are influenced by, the MDS robot.

The vision in Human Robot Interaction is for autonomous robots to have the ability to understand common-sense behavioral patterns. We have developed a multi-modal online game as a method of learning these common-sense behavior patterns. In this two-player game, the players collaborate to create a salad through selection and discussion of available items.

We are demonstrating how a heterogeneous group of robots (MDS and helicopters) can be used as a first disaster response before human teams enter the perimeter. The goal is to have robots engage in the search for victims, build a map of the environment, and report back to human operators at a safe distance through a wireless link. Information collected by the robots is displayed on a remote operator interface in real time so that human personnel can easily create new tasks that reflect the state of the environment. The demonstrated system is semi-autonomous and allows the human operator to be in the loop during the entire search. The robots possess enough autonomy to reduce cognitive load on the operator as much as possible.

Tofu is a robot designed to create what is known in the animation world as “the illusion of life,” by leveraging design principles from 2-D animation and cognitive psychology. Through use of actuation and display technologies, these robot design principles have been applied to create incredibly low-cost and expressive robot characters. To further explore these robots as expressive characters, we are examining their use as a storytelling medium. By combining elements of robot puppetry and autonomous robot agents, we aim to create a storytelling medium that enables children to create engaging stories within a constructivist-learning environment.

Trans-Fiction (xF) characters promote new levels of bi-directional interactivity. This project is an exploration of new ways both to bring fantasy into the real world, and vice versa.