COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916923863728128 |
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| author | Cannizzaro, Ricardo Groom, Michael Routley, Jonathan Ness, Robert Osazuwa Kunze, Lars |
| author_facet | Cannizzaro, Ricardo Groom, Michael Routley, Jonathan Ness, Robert Osazuwa Kunze, Lars |
| contents | Manipulation tasks require robots to reason about cause and effect when interacting with objects. Yet, many data-driven approaches lack causal semantics and thus only consider correlations. We introduce COBRA-PPM, a novel causal Bayesian reasoning architecture that combines causal Bayesian networks and probabilistic programming to perform interventional inference for robot manipulation under uncertainty. We demonstrate its capabilities through high-fidelity Gazebo-based experiments on an exemplar block stacking task, where it predicts manipulation outcomes with high accuracy (Pred Acc: 88.6%) and performs greedy next-best action selection with a 94.2% task success rate. We further demonstrate sim2real transfer on a domestic robot, showing effectiveness in handling real-world uncertainty from sensor noise and stochastic actions. Our generalised and extensible framework supports a wide range of manipulation scenarios and lays a foundation for future work at the intersection of robotics and causality. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_14488 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty Cannizzaro, Ricardo Groom, Michael Routley, Jonathan Ness, Robert Osazuwa Kunze, Lars Robotics Artificial Intelligence Machine Learning Applications I.2.9; I.2.8; I.2.3; G.3; I.2.6; I.6.8; I.2.4; I.2.10 Manipulation tasks require robots to reason about cause and effect when interacting with objects. Yet, many data-driven approaches lack causal semantics and thus only consider correlations. We introduce COBRA-PPM, a novel causal Bayesian reasoning architecture that combines causal Bayesian networks and probabilistic programming to perform interventional inference for robot manipulation under uncertainty. We demonstrate its capabilities through high-fidelity Gazebo-based experiments on an exemplar block stacking task, where it predicts manipulation outcomes with high accuracy (Pred Acc: 88.6%) and performs greedy next-best action selection with a 94.2% task success rate. We further demonstrate sim2real transfer on a domestic robot, showing effectiveness in handling real-world uncertainty from sensor noise and stochastic actions. Our generalised and extensible framework supports a wide range of manipulation scenarios and lays a foundation for future work at the intersection of robotics and causality. |
| title | COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty |
| topic | Robotics Artificial Intelligence Machine Learning Applications I.2.9; I.2.8; I.2.3; G.3; I.2.6; I.6.8; I.2.4; I.2.10 |
| url | https://arxiv.org/abs/2403.14488 |