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Proactive Human-Robot Co-Assembly: Leveraging Human Intention Prediction and Robust Safe Control
Robotics · Submitted 20 Jun 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The experiment demonstrates that our solution is robust to environmental changes as well as different human preferences and behaviors.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Human-robot collaboration (HRC) is one key component to achieving flexible manufacturing to meet the different needs of customers. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | However, it is difficult to build intelligent robots that can proactively assist humans in a safe and efficient way due to several challenges. | background 0.59 | 0.06 | 0.02 | asserted 1.00 |
| s2 | First, it is challenging to achieve efficient collaboration due to diverse human behaviors and data scarcity. | background 0.75 | 0.05 | 0.02 | asserted 1.00 |
| s3 | Second, it is difficult to ensure interactive safety due to uncertainty in human behaviors. | background 0.75 | 0.04 | 0.02 | asserted 1.00 |
| s4 | This paper presents an integrated framework for proactive HRC. | contribution 1.00 | 0.88 | 0.08 | asserted 1.00 |
| s5 | 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. | method 0.87 | 0.90 | 0.08 | asserted 1.00 |
| s6 | The proposed framework also uses robust safe control to ensure interactive safety under uncertainty. | method 0.84 | 0.82 | 0.08 | asserted 1.00 |
| s7 | The developed framework is applied to a co-assembly task using a Kinova Gen3 robot. | method 0.92 | 0.93 | 0.03 | asserted 1.00 |
| s8 | The experiment demonstrates that our solution is robust to environmental changes as well as different human preferences and behaviors. | result 0.99 | 0.94 | 0.19 | asserted 1.00 |
| s9 | In addition, it improves task efficiency by approximately 15-20%. | result 1.00 | 0.94 | 0.16 | asserted 0.99 |
| s10 | Moreover, the experiment demonstrates that our solution can guarantee interactive safety during proactive collaboration. | result 1.00 | 0.93 | 0.32 | asserted 1.00 |
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The paper
Original 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.
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How the literature uses it
What this paper does with the arXiv papers it cites
What it cites · 1 sentence without a confident label
Uses the method or tool of 4 papers
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Given the nominal control $u$, we incorporate the safe set algorithm (SSA) [ref] at the downstream to adjust $u$ if necessary according to the real-time environment.
Safe Interactive Industrial Robots using Jerk-based Safe Set Algorithm · Hierarchical Controller · 1.00
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We use a 3-layer FCNN (2 Relu hidden layers with 32 neurons and 1 linear output layer) to model $h_θ(·)$ with IADA training [ref] .
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance · EXPERIMENTS · 1.00
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A scalar function $ϕ_k=Φ(q^r_k, E_k)$, which is derived from user safety specification as shown in <ref> [ref] , defines the safety status of the entire system. $Φ(·)$ is defined such that the system is safe when $ϕ≤0$ and unsafe when $ϕ> 0$.
Safety Index Synthesis via Sum-of-Squares Programming · Hierarchical Controller · 0.96
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