Enhancing robotic unstructured bin-picking performance by enabling remote human interventions in challenging perception scenarios

Kaipa, Krishnanand N.; Kankanhalli-Nagendra, Akshaya S.; Kumbla, Nithyananda B.; Shriyam, Shaurya; Thevendria-Karthic, Srudeep Somnaath; Marvel, Jeremy A.; Gupta, Satyandra K.

doi:10.1109/coase.2016.7743462

Cited by 7 publications

(1 citation statement)

References 23 publications

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“…They used a perception module to evaluate the outcome of pushing actions. In our previous work [23, 24], any failure in the automatic pose estimation was handled with the help of human assistance. The human operator used an interface to estimate the pose of the part and sent it back to the robot.…”

Section: Related Workmentioning

confidence: 99%

Handling Perception Uncertainty in Simulation-Based Singulation Planning for Robotic Bin Picking

Kumbla

Thakar

Kaipa

et al. 2018

Journal of Computing and Information Science in Engineering

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Robotic bin picking requires using a perception system to estimate the pose of the parts in the bin. The selected singulation plan should be robust with respect to perception uncertainties. If the estimated posture is significantly different from the actual posture, then the singulation plan may fail during execution. In such cases, the singulation process will need to be repeated. We are interested in selecting singulation plans that minimize the expected task completion time. In order to estimate the expected task completion time for a proposed singulation plan, we need to estimate the probability of success and the plan execution time. Robotic bin picking needs to be done in real-time. Therefore, candidate singulation plans need to be generated and evaluated in real-time. This paper presents an approach for utilizing computationally efficient simulations for generating singulation plans. Results from physical experiments match well with predictions obtained from simulations.

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Section: Related Workmentioning

confidence: 99%