Robot Safe Interaction System for Intelligent Industrial Co-Robots

Liu, Changliu; Tomizuka, Masayoshi

doi:10.48550/arxiv.1808.03983

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…In a related strand, researchers have used keypoint detection for human pose estimation [30][31][32][33][34]. Nevertheless, in robotics applications, it is not uncommon for objects to be detected via fiducial markers [35][36][37]. Closely related to our work, Deng et al [16] used a particle filter-based approach to predicting the distribution of possible object poses.…”

Section: A Object Pose Estimationmentioning

confidence: 81%

Fast Uncertainty Quantification for Deep Object Pose Estimation

Shi¹,

Zhu²,

Tremblay³

et al. 2020

Preprint

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Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transfer. Efficient and robust uncertainty quantification (UQ) in pose estimators is critically needed in many robotic tasks. In this work, we propose a simple, efficient, and plug-and-play UQ method for 6-DoF object pose estimation. We ensemble 2-3 pre-trained models with different neural network architectures and/or training data sources, and compute their average pairwise disagreement against one another to obtain the uncertainty quantification. We propose four disagreement metrics, including a learned metric, and show that the average distance (ADD) is the best learning-free metric and it is only slightly worse than the learned metric, which requires labeled target data. Our method has several advantages compared to the prior art: 1) our method does not require any modification of the training process or the model inputs; and 2) it needs only one forward pass for each model. We evaluate the proposed UQ method on three tasks where our uncertainty quantification yields much stronger correlations with pose estimation errors than the baselines. Moreover, in a real robot grasping task, our method increases the grasping success rate from 35% to 90%. Video and code are available at https://sites.google.com/view/fastuq.

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Section: A Object Pose Estimationmentioning

confidence: 81%

Fast Uncertainty Quantification for Deep Object Pose Estimation

Shi¹,

Zhu²,

Tremblay³

et al. 2020

Preprint

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show abstract

“…Instead of directly predicting the future trajectories based on only current and historical human trajectories, we leverage the action labels from the plan recognition module. Motion Control Module includes two controllers: an efficiency controller and a safety controller, as in [21]. The efficiency controller is a long-term global controller to assure the efficiency of robot, and the safety controller is a short-term local controller for real-time safety under uncertainties.…”

Section: An Integrated Plan Recognition and Trajectory Prediction Fra...mentioning

confidence: 99%

Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction

Cheng

Sun

Liu

et al. 2020

IEEE Robot. Autom. Lett.

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Human-robot collaboration (HRC) is becoming increasingly important as the paradigm of manufacturing is shifting from mass production to mass customization. The introduction of HRC can significantly improve the flexibility and intelligence of automation. To efficiently finish tasks in HRC systems, the robots need to not only predict the future movements of human, but also more high-level plans, i.e., the sequence of actions to finish the tasks. However, due to the stochastic and time-varying nature of human collaborators, it is quite challenging for the robot to efficiently and accurately identify such task plans and respond in a safe manner. To address this challenge, we propose an integrated human-robot collaboration framework. Both plan recognition and trajectory prediction modules are included for the generation of safe and efficient robotic motions. Such a framework enables the robots to perceive, predict and adapt their actions to the human's work plan and intelligently avoid collisions with the human. Moreover, by explicitly leveraging the hierarchical relationship between plans and trajectories, more robust plan recognition performance can be achieved. Physical experiments were conducted on an industrial robot to verify the proposed framework. The results show that the proposed framework could accurately recognize the human workers' plans and thus significantly improve the time efficiency of the HRC team even in the presence of motion classification noises.

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“…We get inspired by the spirit of combing short-term and longterm planning in Ref. [15]. They use two-layer planners, whereas, we propose to use a single planner to combine short-term and long-term planning to a unified framework to improve the efficiency.…”

Section: Prediction-relatedmentioning

confidence: 99%

“…The idea of hybrid prediction and planning is inspired by Liu et. al [15]. The authors proposed to combine a safetyoriented short-term planner and an efficiency-oriented longterm planner to deal with a safe human-robot interaction problem.…”

Section: A Prediction Of Dynamic Obstaclesmentioning

confidence: 99%

Safe Planning for Self-Driving Via Adaptive Constrained ILQR

Pan

Shah

et al. 2020

Preprint

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Constrained Iterative Linear Quadratic Regulator (CILQR), a variant of ILQR, has been recently proposed for motion planning problems of autonomous vehicles to deal with constraints such as obstacle avoidance and reference tracking. However, the previous work considers either deterministic trajectories or persistent prediction for target dynamical obstacles. The other drawback is lack of generality -it requires manual weight tuning for different scenarios. In this paper, two significant improvements are achieved. Firstly, a twostage uncertainty-aware prediction is proposed. The short-term prediction with safety guarantee based on reachability analysis is responsible for dealing with extreme maneuvers conducted by target vehicles. The long-term prediction leveraging an adaptive least square filter preserves the long-term optimality of the planned trajectory since using reachability only for long-term prediction is too pessimistic and makes the planner over-conservative. Secondly, to allow a wider coverage over different scenarios and to avoid tedious parameter tuning case by case, this paper designs a scenario-based analytical function taking the states from the ego vehicle and the target vehicle as input, and carrying weights of a cost function as output. It allows the ego vehicle to execute multiple behaviors (such as lane-keeping and overtaking) under a single planner. We demonstrate safety, effectiveness, and real-time performance of the proposed planner in simulations.

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Robot Safe Interaction System for Intelligent Industrial Co-Robots

Cited by 5 publications

References 27 publications

Fast Uncertainty Quantification for Deep Object Pose Estimation

Fast Uncertainty Quantification for Deep Object Pose Estimation

Towards Efficient Human-Robot Collaboration With Robust Plan Recognition and Trajectory Prediction

Safe Planning for Self-Driving Via Adaptive Constrained ILQR

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