Robotic Object Placement Using Refined In-Hand Pose Estimation
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Solution Overview
Problem
Conventional robotics planning for object placement requires immense manual programming, which is tedious, time-consuming, and error-prone, and is not robust to small changes in the operating environment.
Innovation Solution
A system and method that automatically refine a placement plan by refining in-hand pose estimates of objects held by an end effector, involving determining an initial in-hand state, a show pose, moving to the show pose, determining a refined in-hand state, and optionally determining a placement plan.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual programming is used to dictate robotic movements, then precise control of robotic components is achieved, but the programming process becomes tedious, time-consuming, and error-prone
Solution Approach 1:
The system enables robots to autonomously plan and execute their own movements by implementing a motion planning module that generates trajectories independently, eliminating the need for tedious manual programming while maintaining precise control through automated feedback mechanisms
Solution Approach 2:
The patent replaces manual programming mechanisms with automated motion planning algorithms and neural network-based control systems that automatically generate and adjust robotic trajectories, substituting human-operated programming with intelligent automated systems
2Reliability
If manual programming is used to dictate robotic movements, then specific movement sequences are controlled, but the system becomes brittle and not robust to small changes in the operating environment
Solution Approach 1:
The motion planning module dynamically adjusts robotic trajectories in real-time based on sensor feedback and environmental changes, allowing the system to adapt to small variations in the operating environment while maintaining reliable control through continuous optimization
Solution Approach 2:
The system implements closed-loop feedback mechanisms where sensor data from the operating environment is continuously fed back to the motion planning module, enabling automatic adjustment of movement sequences to maintain reliability despite environmental changes
3Manufacturing precision
If refinement of in-hand pose estimates is implemented, then placement accuracy is increased, but additional processing time is required
Solution Approach 1:
The system performs preliminary pose estimation before the actual placement operation, using pre-captured images and pre-computed trajectories to refine in-hand pose estimates in advance, allowing the refinement process to occur without pausing the main placement workflow
Solution Approach 2:
The motion planning module continuously refines pose estimates and adjusts trajectories without interrupting the overall placement process, maintaining continuous useful action by overlapping computation with execution phases rather than sequential processing
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing planning for robotic placement tasks. One of the methods includes determining an initial in-hand state for a grasped object. A show pose for the grasped object is determined, and the object is moved to the show pose. A refined in-hand state for the grasped object is determined based on the show pose, and a placement plan is determined based on the refined in-hand state for the grasped object.