Thomas Weng

Nonverbal behaviors increase task efficiency and improve collaboration between people and robots. In this paper, we introduce a model for generating nonverbal behavior and investigate whether the usefulness of nonverbal behaviors changes based on task difficulty. First, we detail a robot behavior model that accounts for top-down and bottom-up features of the scene when deciding when and how to perform deictic references (looking or pointing). Then, we analyze how a robot's deictic nonverbal behavior affects people's performance on a memorization task under differing difficulty levels. We manipulate difficulty in two ways: by adding steps to memorize, and by introducing an interruption. We find that when the task is easy, the robot's nonverbal behavior has little influence over recall and task completion. However, when the task is challenging— because the memorization load is high or because the task is interrupted—a robot's nonverbal behaviors mitigate the negative effects of these challenges, leading to higher recall accuracy and lower completion times. In short, nonverbal behavior may be even more valuable for difficult collaborations than for easy ones.

State-of-the-art object grasping methods rely on depth sensing to plan robust grasps, but commercially available depth sensors fail to detect transparent and specular objects. To improve grasping performance on such objects, we introduce a method for learning a multi-modal perception model by bootstrapping from an existing uni-modal model. This transfer learning approach requires only a pre-existing uni-modal grasping model and paired multi-modal image data for training, foregoing the need for ground-truth grasp success labels nor real grasp attempts. Our experiments demonstrate that our approach is able to reliably grasp transparent and reflective objects. Video and supplementary material are available at https://sites.google.com/view/transparent-specular-grasping.

Nonverbal behaviors increase task efficiency and improve collaboration between people and robots. In this paper, we introduce a model for generating nonverbal behavior and investigate whether the usefulness of nonverbal behaviors changes based on task difficulty. First, we detail a robot behavior model that accounts for top-down and bottom-up features of the scene when deciding when and how to perform deictic references (looking or pointing). Then, we analyze how a robot's deictic nonverbal behavior affects people's performance on a memorization task under differing difficulty levels. We manipulate difficulty in two ways: by adding steps to memorize, and by introducing an interruption. We find that when the task is easy, the robot's nonverbal behavior has little influence over recall and task completion. However, when the task is challenging---because the memorization load is high or because the task is interrupted---a robot's nonverbal behaviors mitigate the negative effects of these challenges, leading to higher recall accuracy and lower completion times. In short, nonverbal behavior may be even more valuable for difficult collaborations than for easy ones.

We formulate grasp learning as a neural field and present Neural Grasp Distance Fields (NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a continuous manifold of valid grasps for an object. In contrast to current approaches that predict a set of discrete candidate grasps, the distance-based NGDF representation is easily interpreted as a cost, and minimizing this cost produces a successful grasp pose. This grasp distance cost can be incorporated directly into a trajectory optimizer for joint optimization with other costs such as trajectory smoothness and collision avoidance. During optimization, as the various costs are balanced and minimized, the grasp target is allowed to smoothly vary, as the learned grasp field is continuous. We evaluate NGDF on joint grasp and motion planning in simulation and the real world, outperforming baselines by 63 % execution success while generalizing to unseen query poses and unseen object shapes. Project page: https://sites.google.com/view/neural-grasp-distance-fields.

This paper presents a model that uses a robot's verbal and nonverbal behaviors to successfully communicate object references to a human partner. This model, which is informed by computer vision, human-robot interaction, and cognitive psychology, simulates how low-level and high-level features of the scene might draw a user's attention. It then selects the most appropriate robot behavior that maximizes the likelihood that a user will understand the correct object reference while minimizing the cost of the behavior. We present a general computational framework for this model, then describe a specific implementation in a human-robot collaboration. Finally, we analyze the model's performance in two human evaluations—one video-based (75 participants) and one in person (20 participants)—and demonstrate that the system predicts the correct behaviors to perform successful object references.