In-Hand Robot Manipulation Planning for Adaptive Grasp Transitions
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Solution Overview
Problem
Current robotics technologies face challenges in replicating human dexterity for in-hand manipulation tasks, such as global repositioning of objects and transitions between different grasp types, which are essential for using tools effectively.
Innovation Solution
A system comprising a planner and a controller that performs object trajectory planning and grasp sequence planning using both model-based and learning-based approaches. The planner generates grasp sequences based on optimized cost functions and deep reinforcement learning policies, allowing for adaptive grasp changes during object manipulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional model-based planning is used for grasp sequence generation, then computational efficiency is improved, but adaptability to complex manipulation tasks deteriorates
Solution Approach 1:
The planning system is divided into two distinct modules: a model-based planner for computationally efficient basic grasp sequence generation, and a deep reinforcement learning-based planner for handling complex and adaptive manipulation tasks. This segmentation allows each module to specialize in specific task types, resolving the contradiction between efficiency and adaptability.
Solution Approach 2:
The patent merges two different planning approaches (model-based and learning-based) into a unified system that can leverage the strengths of both. The model-based component provides computational efficiency for standard tasks, while the learning-based component adds adaptability for complex scenarios, achieving both goals simultaneously.
2Adaptability or versatility
If learning-based approaches are used for grasp sequence planning, then adaptability to complex tasks is improved, but computational complexity increases
Solution Approach 1:
The system segments the planning workload by using model-based methods for computationally simple basic tasks and reserving learning-based methods only for complex tasks that require adaptability. This reduces the overall computational complexity while maintaining high adaptability where needed.
Solution Approach 2:
The model-based planner acts as an intermediary that handles routine planning tasks, freeing the learning-based component from unnecessary computational overhead. This intermediary structure allows the complex learning-based system to focus only on tasks where its complexity provides value.
3Measurement precision
If final grasp is required as input for grasp sequence planning, then planning accuracy is improved, but flexibility in generating grasp sequences deteriorates
Solution Approach 1:
Instead of requiring the final grasp as input and working backwards, the learning-based planner inverts this approach by generating grasp sequences forward without needing the final grasp specified. This inversion maintains planning accuracy through learned policies while significantly improving flexibility in sequence generation.
Solution Approach 2:
The learning-based planner is self-sufficient in that it does not require the final grasp to be provided as input. It autonomously determines the entire grasp sequence based on the initial state and task objectives, thereby improving flexibility without sacrificing accuracy through its trained policy.
Data Source
AI summary
A robot for object manipulation may include sensors, a robot appendage, actuators configured to drive joints of the robot appendage, a planner, and a controller. Object path planning may include determining poses. Object trajectory optimization may include assigning a set of timestamps to the poses, optimizing a cost function which may be a cost function for finger sliding based on a penalty for a sliding distance, a change in desired normal direction, and a wrench error associated with sliding a robot finger, and generating an object trajectory based on the optimized cost function. Grasp sequence planning may be model-based or deep reinforcement learning (DRL) policy based. The controller may execute the object trajectory and the grasp sequence via the robot appendage and actuators.