Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together

• URL: http://arxiv.org/abs/2202.01493v1
• Date: Thu, 3 Feb 2022 10:04:26 GMT
• Title: Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together
• Authors: Jeffrey Delmerico, Roi Poranne, Federica Bogo, Helen Oleynikova, Eric Vollenweider, Stelian Coros, Juan Nieto, Marc Pollefeys
• Abstract summary: This paper presents several human-robot systems that utilize spatial computing to enable novel robot use cases. The combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning.
• Score: 68.44697646919515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatial computing -- the ability of devices to be aware of their surroundings and to represent this digitally -- offers novel capabilities in human-robot interaction. In particular, the combination of spatial computing and egocentric sensing on mixed reality devices enables them to capture and understand human actions and translate these to actions with spatial meaning, which offers exciting new possibilities for collaboration between humans and robots. This paper presents several human-robot systems that utilize these capabilities to enable novel robot use cases: mission planning for inspection, gesture-based control, and immersive teleoperation. These works demonstrate the power of mixed reality as a tool for human-robot interaction, and the potential of spatial computing and mixed reality to drive the future of human-robot interaction.

Related papers

• Interação entre robôs humanoides: desenvolvendo a colaboração e comunicação autônoma [0.0]
  The study investigates the interaction between humanoid robots NAO and Pepper, emphasizing their potential applications in educational settings. Through a series of programmed interactions, the robots demonstrated their ability to communicate and coordinate actions autonomously.
  arXiv  Detail & Related papers  (2024-10-22T21:55:54Z)
• HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams [0.0]
  We demonstrate a cognitive strategy for robots in human-robot teams that incorporates metacognition, natural language communication, and explainability. The system is embodied using the HARMONIC architecture that flexibly integrates cognitive and control capabilities.
  arXiv  Detail & Related papers  (2024-09-26T16:48:21Z)
• Robot Interaction Behavior Generation based on Social Motion Forecasting for Human-Robot Interaction [9.806227900768926]
  We propose to model social motion forecasting in a shared human-robot representation space. ECHO operates in the aforementioned shared space to predict the future motions of the agents encountered in social scenarios. We evaluate our model in multi-person and human-robot motion forecasting tasks and obtain state-of-the-art performance by a large margin.
  arXiv  Detail & Related papers  (2024-02-07T11:37:14Z)
• Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots [119.55240471433302]
  Habitat 3.0 is a simulation platform for studying collaborative human-robot tasks in home environments. It addresses challenges in modeling complex deformable bodies and diversity in appearance and motion. Human-in-the-loop infrastructure enables real human interaction with simulated robots via mouse/keyboard or a VR interface.
  arXiv  Detail & Related papers  (2023-10-19T17:29:17Z)
• HERD: Continuous Human-to-Robot Evolution for Learning from Human Demonstration [57.045140028275036]
  We show that manipulation skills can be transferred from a human to a robot through the use of micro-evolutionary reinforcement learning. We propose an algorithm for multi-dimensional evolution path searching that allows joint optimization of both the robot evolution path and the policy.
  arXiv  Detail & Related papers  (2022-12-08T15:56:13Z)
• Generalizable Human-Robot Collaborative Assembly Using Imitation Learning and Force Control [17.270360447188196]
  We present a system for human-robot collaborative assembly using learning from demonstration and pose estimation. The proposed system is demonstrated using a physical 6 DoF manipulator in a collaborative human-robot assembly scenario.
  arXiv  Detail & Related papers  (2022-12-02T20:35:55Z)
• Semantic-Aware Environment Perception for Mobile Human-Robot Interaction [2.309914459672557]
  We present a vision-based system for mobile robots to enable a semantic-aware environment without additional a-priori knowledge. We deploy our system on a mobile humanoid robot that enables us to test our methods in real-world applications.
  arXiv  Detail & Related papers  (2022-11-07T08:49:45Z)
• Robots with Different Embodiments Can Express and Influence Carefulness in Object Manipulation [104.5440430194206]
  This work investigates the perception of object manipulations performed with a communicative intent by two robots. We designed the robots' movements to communicate carefulness or not during the transportation of objects.
  arXiv  Detail & Related papers  (2022-08-03T13:26:52Z)
• Synthesis and Execution of Communicative Robotic Movements with Generative Adversarial Networks [59.098560311521034]
  We focus on how to transfer on two different robotic platforms the same kinematics modulation that humans adopt when manipulating delicate objects. We choose to modulate the velocity profile adopted by the robots' end-effector, inspired by what humans do when transporting objects with different characteristics. We exploit a novel Generative Adversarial Network architecture, trained with human kinematics examples, to generalize over them and generate new and meaningful velocity profiles.
  arXiv  Detail & Related papers  (2022-03-29T15:03:05Z)
• Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration [83.4081612443128]
  We show that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground-truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations.
  arXiv  Detail & Related papers  (2021-03-06T09:14:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.