Recent progress in robotic manipulation has dealt with the case of no prior object models in the context of relatively simple tasks, such as bin-picking. Existing methods for more constrained problems, however, such as deliberate placement in a tight region, depend more critically on shape information to achieve safe execution. This work introduces a possibilistic object representation for solving constrained placement tasks without shape priors. A perception method is proposed to track and update the object representation during motion execution, which respects physical and geometric constraints. The method operates directly over sensor data, modeling the seen and unseen parts of the object given observations. It results in a dynamically updated conservative representation, which can be used to plan safe manipulation actions. This task-driven perception process is integrated with manipulation task planning architecture for a dual-arm manipulator to discover efficient solutions for the constrained placement task with minimal sensing. The planning process can make use of handoff operations when necessary for safe placement given the conservative representation. The pipeline is evaluated with data from over 240 real-world experiments involving constrained placement of various unknown objects using a dual-arm manipulator. While straightforward pick-sense-and-place architectures frequently fail to solve these problems, the proposed integrated pipeline achieves more than 95% success and faster execution times.
Task-driven Perception and Manipulation for Constrained Placement with No Shape Priors
Recent progress in robotic manipulation has dealt with the case of no prior object models in the context of relatively simple tasks, such as bin-picking. Existing methods for more constrained problems, however, such as deliberate placement in a tight region, depend more critically on shape information to achieve safe execution. This work introduces a possibilistic object representation for solving constrained placement tasks without shape priors. A perception method is proposed to track and update the object representation during motion execution, which respects physical and geometric constraints. The method operates directly over sensor data, modeling the seen and unseen parts of the object given observations. It results in a dynamically updated conservative representation, which can be used to plan safe manipulation actions. This task-driven perception process is integrated with manipulation task planning architecture for a dual-arm manipulator to discover efficient solutions for the constrained placement task with minimal sensing. The planning process can make use of handoff operations when necessary for safe placement given the conservative representation. The pipeline is evaluated with data from over 240 real-world experiments involving constrained placement of various unknown objects using a dual-arm manipulator. While straightforward pick-sense-and-place architectures frequently fail to solve these problems, the proposed integrated pipeline achieves more than 95% success and faster execution times.
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