Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction
and Robust Safe Control
- URL: http://arxiv.org/abs/2306.11862v2
- Date: Sat, 2 Sep 2023 19:51:37 GMT
- Title: Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction
and Robust Safe Control
- Authors: Ruixuan Liu, Rui Chen, Abulikemu Abuduweili, Changliu Liu
- Abstract summary: This paper presents an integrated framework for proactive human-robot collaboration.
A robust intention prediction module is learned to guide the robot for efficient collaboration.
The developed framework is applied to a co-assembly task using a Kinova Gen3 robot.
- Score: 10.973115127845224
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Human-robot collaboration (HRC) is one key component to achieving flexible
manufacturing to meet the different needs of customers. However, it is
difficult to build intelligent robots that can proactively assist humans in a
safe and efficient way due to several challenges. First, it is challenging to
achieve efficient collaboration due to diverse human behaviors and data
scarcity. Second, it is difficult to ensure interactive safety due to
uncertainty in human behaviors. This paper presents an integrated framework for
proactive HRC. A robust intention prediction module, which leverages prior task
information and human-in-the-loop training, is learned to guide the robot for
efficient collaboration. The proposed framework also uses robust safe control
to ensure interactive safety under uncertainty. The developed framework is
applied to a co-assembly task using a Kinova Gen3 robot. The experiment
demonstrates that our solution is robust to environmental changes as well as
different human preferences and behaviors. In addition, it improves task
efficiency by approximately 15-20%. Moreover, the experiment demonstrates that
our solution can guarantee interactive safety during proactive collaboration.
Related papers
- CoBOS: Constraint-Based Online Scheduler for Human-Robot Collaboration [3.3148826359547523]
We propose a novel approach of online constraint-based scheduling in a reactive execution control framework.
This allows the robot to adapt to uncertain events such as delayed activity completions and activity selection (by the human)
In addition to the improved working conditions, our algorithm leads to increased efficiency, even in highly uncertain scenarios.
arXiv Detail & Related papers (2024-03-27T11:18:01Z) - Optimising Human-AI Collaboration by Learning Convincing Explanations [62.81395661556852]
We propose a method for a collaborative system that remains safe by having a human making decisions.
Ardent enables efficient and effective decision-making by adapting to individual preferences for explanations.
arXiv Detail & Related papers (2023-11-13T16:00:16Z) - 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) - Quantifying Assistive Robustness Via the Natural-Adversarial Frontier [40.125563987538044]
RIGID is a method for training adversarial human policies that trade off between minimizing robot reward and acting human-like.
On an Assistive Gym task, we use RIGID to analyze the performance of standard collaborative Reinforcement Learning.
We also compare the frontier RIGID identifies with the failures identified in expert adversarial interaction, and with naturally-occurring failures during user interaction.
arXiv Detail & Related papers (2023-10-16T17:34:54Z) - Safe Multimodal Communication in Human-Robot Collaboration [12.688356318251763]
We propose a framework that enables multi-channel communication between humans and robots by leveraging multimodal fusion of voice and gesture commands.
The framework is validated through a comparative experiment, demonstrating that, thanks to multimodal communication, the robot can extract valuable information for performing the required task.
arXiv Detail & Related papers (2023-08-07T16:08:21Z) - Rearrange Indoor Scenes for Human-Robot Co-Activity [82.22847163761969]
We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better.
Our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet.
Our experiments show that rearranged scenes provide an average of 14% more accessible space and 30% more objects to interact with.
arXiv Detail & Related papers (2023-03-10T03:03:32Z) - PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI
Coordination [52.991211077362586]
We propose a policy ensemble method to increase the diversity of partners in the population.
We then develop a context-aware method enabling the ego agent to analyze and identify the partner's potential policy primitives.
In this way, the ego agent is able to learn more universal cooperative behaviors for collaborating with diverse partners.
arXiv Detail & Related papers (2023-01-16T12:14:58Z) - Intuitive and Efficient Human-robot Collaboration via Real-time
Approximate Bayesian Inference [4.310882094628194]
Collaborative robots and end-to-end AI, promises flexible automation of human tasks in factories and warehouses.
Humans and cobots will collaborate helping each other.
For these collaborations to be effective and safe, robots need to model, predict and exploit human's intents.
arXiv Detail & Related papers (2022-05-17T23:04:44Z) - SERA: Safe and Efficient Reactive Obstacle Avoidance for Collaborative
Robotic Planning in Unstructured Environments [1.5229257192293197]
We propose a novel methodology for reactive whole-body obstacle avoidance.
Our approach allows a robotic arm to proactively avoid obstacles of arbitrary 3D shapes without direct contact.
Our methodology provides a robust and effective solution for safe human-robot collaboration in non-stationary environments.
arXiv Detail & Related papers (2022-03-24T21:11:43Z) - Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration [51.268988527778276]
We present a method for learning a human-robot collaboration policy from human-human collaboration demonstrations.
Our method co-optimizes a human policy and a robot policy in an interactive learning process.
arXiv Detail & Related papers (2021-08-13T03:14:43Z) - 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.