257 research outputs found
Task Planning and Execution for Human Robot Team Performing a Shared Task in a Shared Workspace
A cyber-physical system is developed to enable a human-robot team to perform a shared task in a shared workspace. The system setup is suitable for the implementation of a tabletop manipulation task, a common human-robot collaboration scenario. The system integrates elements that exist in the physical (real) and the virtual world. In this work, we report the insights we gathered throughout our exploration in understanding and implementing task planning and execution for human-robot team
Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment
The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety shutdowns that reduce workflow and production times. This paper presents a novel strategy that combines the execution of contact-rich tasks, namely disassembly, with real-time collision avoidance through machine learning for safe human-robot interaction. Specifically, a goal-conditioned reinforcement learning approach is proposed, in which the removal direction of a peg, of varying friction, tolerance, and orientation, is subject to the location of a human collaborator with respect to a 7-degree-of-freedom manipulator at each time step. For this purpose, the suitability of three state-of-the-art actor-critic algorithms is evaluated, and results from simulation and real-world experiments are presented. In reality, the policy’s deployment is achieved through a new scalable multi-control framework that allows a direct transfer of the control policy to the robot and reduces response times. The results show the effectiveness, generalization, and transferability of the proposed approach with two collaborative robots against static and dynamic obstacles, leveraging the set of available solutions in non-monotonic tasks to avoid a potential collision with the human worker
Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment
With the rapid growth of computing powers and recent advances in deep
learning, we have witnessed impressive demonstrations of novel robot
capabilities in research settings. Nonetheless, these learning systems exhibit
brittle generalization and require excessive training data for practical tasks.
To harness the capabilities of state-of-the-art robot learning models while
embracing their imperfections, we present Sirius, a principled framework for
humans and robots to collaborate through a division of work. In this framework,
partially autonomous robots are tasked with handling a major portion of
decision-making where they work reliably; meanwhile, human operators monitor
the process and intervene in challenging situations. Such a human-robot team
ensures safe deployments in complex tasks. Further, we introduce a new learning
algorithm to improve the policy's performance on the data collected from the
task executions. The core idea is re-weighing training samples with
approximated human trust and optimizing the policies with weighted behavioral
cloning. We evaluate Sirius in simulation and on real hardware, showing that
Sirius consistently outperforms baselines over a collection of contact-rich
manipulation tasks, achieving an 8% boost in simulation and 27% on real
hardware than the state-of-the-art methods, with twice faster convergence and
85% memory size reduction. Videos and code are available at
https://ut-austin-rpl.github.io/sirius
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