In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large- scale indoor structures, our approach—Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by postprocessing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation.

This paper addresses the problem of training a robot to carry out temporal tasks of arbitrary complexity via evaluative human feedback that can be inaccurate. A key idea explored in our work is a kind of curriculum learning---training the robot to master simple tasks and then building up to more complex tasks. We show how a training procedure, using knowledge of the formal task representation, can decompose and train any task efficiently in the size of its representation. We further provide a set of experiments that support the claim that non-expert human trainers can decompose tasks in a way that is consistent with our theoretical results, with more than half of participants successfully training all of our experimental missions. We compared our algorithm with existing approaches and our experimental results suggest that our method outperforms alternatives, especially when feedback contains mistakes.

Robotic companions offer a unique combination of embodiment and computation which open many new interesting opportunities in the field of pediatric care. As these new technologies are developed, we must consider the central research questions of how such systems should be designed and what the appropriate applications for such systems are. In this paper we present the Huggable, a robotic companion in the form factor of a teddy bear and outline a series of studies we are planning to run using the Huggable in a pediatric care unit.

Robots as an embodied, multi-modal technology have great potential to be used as a new type of communication device. In this paper we outline our development of the Huggable robot as a semi-autonomous robot avatar for two specific types of remote interaction — family communication and education. Through our discussion we highlight how we have applied six important elements in our system to allow for the robot to function as a richly embodied communication channel.