Value of Assistance for Grasping
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Abstract (arXiv)
In multiple realistic settings, a robot is tasked with grasping an object without knowing its exact pose and relies on a probabilistic estimation of the pose to decide how to attempt the grasp. We support settings in which it is possible to provide the robot with an observation of the object before a grasp is attempted but this possibility is limited and there is a need to decide which sensing action would be most beneficial. We support this decision by offering a novel Value of Assistance (VOA) measure for assessing the expected effect a specific observation will have on the robot's ability to complete its task. We evaluate our suggested measure in simulated and real-world collaborative grasping settings.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 51 references that are in the map. First posted on arXiv 22 Oct 2023.
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12 of 51 references.
References · 12
- 2017Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metricscited by 264
- 2015Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hourscited by 209
- 2019A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithmscited by 142
- 2013Data-Driven Grasp Synthesis - A Surveycited by 106
- 2016Revisiting Active Perceptioncited by 94
- 2022Partially Observable Markov Decision Processes in Robotics: A Surveycited by 86
- 2021Kimera-Multi: Robust, Distributed, Dense Metric-Semantic SLAM for Multi-Robot Systemscited by 54
- 2021Partially Observable Markov Decision Processes (POMDPs) and Roboticscited by 49
- 2021Active Learning in Robotics: A Review of Control Principlescited by 26
- 2020Action Image Representation: Learning Scalable Deep Grasping Policies with Zero Real World Datacited by 13
- 2023Machine Learning Meets Advanced Robotic Manipulationcited by 2
- 2023Value of Assistance for Mobile Agentscited by 1
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