Other than eye gaze and referential gestures (e.g. pointing), the relationship between robot motion and observer attention is not well understood. We explore this relationship to achieve social goals, such as influencing human partner behavior or directing attention. We present an algorithm that creates exaggerated variants of a motion in real-time. Through two experiments we confirm that exaggerated motion is perceptibly different than the input motion, provided that the motion is sufficiently exaggerated. We found that different levels of exaggeration correlate to human expectations of robot-like, human-like, and cartoon-like motion. We present empirical evidence that use of exaggerated motion in experiments enhances the interaction through the benefits of increased engagement and perceived entertainment value. Finally, we provide statistical evidence that exaggerated motion causes predictable human partner gaze direction and better retention of interaction details.

Coupled degrees-of-freedom exhibit correspondence, in that their trajectories influence each other. In this paper we add evidence to the hypothesis that spatiotemporal correspondence (STC) of distributed actuators is a component of human-like motion. We demonstrate a method for making robot motion more human-like, by optimizing with respect to a nonlinear STC metric. Quantitative evaluation of STC between coordinated robot motion, human motion capture data, and retargeted human motion capture data projected onto an anthropomorphic robot suggests that coordinating robot motion with respect to the STC metric makes the motion more human-like. A user study based on mimicking shows that STC-optimized motion is (1) more often recognized as a common human motion, (2) more accurately identified as the originally intended motion, and (3) mimicked more accurately than a non-optimized version. We conclude that coordinating robot motion with respect to the STC metric makes the motion more human-like. Finally, we present and discuss data on potential reasons why coordinating motion increases recognition and ability to mimic.

Social robots can benefit from motion variance because non-repetitive gestures will be more natural and intuitive for human partners. We introduce a new approach for synthesizing variance, both with and without constraints, using a stochastic process. Based on optimal control theory and operational space control, our method can generate an infinite number of variations in real-time that resemble the kinematic and dynamic characteristics from the single input motion sequence. We also introduce a stochastic method to generate smooth but nondeterministic transitions between arbitrary motion variants. Furthermore, we quantitatively evaluate task-aware variance against random white torque noise, operational space control, style-based inverse kinematics, and retargeted human motion to prove that task-aware variance generates human-like motion. Finally, we demonstrate the ability of task-aware variance to maintain velocity and time-dependent features that exist in the input motion.

Robots that display anticipatory motion provide their human partners with greater time to respond in interactive tasks because human partners are aware of robot intent earlier. We create anticipatory motion autonomously from a single motion exemplar by extracting hand and body symbols that communicate motion intent and moving them earlier in the motion. We validate that our algorithm extracts the most salient frame (i.e. the correct symbol) which is the most informative about motion intent to human observers. Furthermore, we show that anticipatory variants allow humans to discern motion intent sooner than motions without anticipation, and that humans are able to reliably predict motion intent prior to the symbol frame when motion is anticipatory. Finally, we quantified the time range for robot motion when humans can perceive intent more accurately and the collaborative social benefits of anticipatory motion are greatest.

Stylized motion is prevalent in the field of Human-Robot Interaction (HRI). Robot designers typically hand craft or work with professional animators to design behaviors for a robot that will be communicative or life-like when interacting with a human partner. A challenge is to apply this stylized trajectory in varied contexts (e.g. performing a stylized gesture with different end-effector constraints). The goal of this research is to create useful, task-based motion with variance that spans the reachable space of the robot and satisfies constraints, while preserving the “style” of the original motion. We claim the appropriate representation for adapting and generalizing a trajectory is not in Cartesian or joint angle space, but rather in joint velocity space, which allows for unspecified initial conditions to be supplied by interaction with the dynamic environment. The benefit of this representation is that a single trajectory can be extended to accomplish similar tasks in the world given constraints in the environment. We present quantitative data using a continuity metric to prove that, given a stylized initial trajectory, we can create smoother generalized motion than with traditional techniques such as cyclic-coordinate descent.

Secondary action, a concept borrowed from character animation, improves the animation realism by augmenting natural, passive motion to primary action. We use dynamic simulation to induce three techniques of secondary motion for robot hardware, which exploit actuation passivity to overcome hardware constraints and change the dynamic perception of the robot and its motion characteristics. Results of secondary motion due to internal and external forces are presented including discussion on how to choose the appropriate technique for a particular application.