Robotic Manipulator Policy Adaptation for Unseen Object Properties
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Solution Overview
Problem
Existing robotic manipulators face challenges in generalizing manipulation policies across different objects due to varying geometric shapes, physical attributes, and contact dynamics, limiting their ability to adapt to new tasks and environments.
Innovation Solution
A framework that utilizes reinforcement learning (RL) to learn a generalizable manipulation policy by leveraging physical properties of objects as constraints, employing a state adapter model and an action adapter model to adapt the policy for different objects, allowing zero-shot transfer from simulation to real-world scenarios.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model-based approaches are used to develop control strategies by modeling contact dynamics, then control precision is improved, but device complexity and difficulty of detecting and measuring increase due to hybrid contact dynamics requiring strong assumptions and precise system identification
Solution Approach 1:
The patent replaces model-based control approaches with a reinforcement learning-based control system. Instead of using complex contact dynamics models that require precise system identification and strong assumptions about initial conditions, the invention uses RL agents that learn optimal control policies through interaction with the environment. This substitution eliminates the need for explicit contact dynamics modeling while achieving effective manipulation control.
Solution Approach 2:
The patent employs simulation environments to train RL policies before deploying them to real robotic manipulators. Multiple virtual copies of the manipulation environment are created with varying parameters, and the RL agent learns generalizable policies in these simulated settings. This copying approach allows the system to learn from extensive virtual experience without requiring complex physical modeling, and the learned policies transfer to real-world scenarios.
2Ease of operation
If reinforcement learning methods are used to learn manipulation skills by optimizing a designed reward, then ease of operation is improved, but adaptability deteriorates because the learned policy only applies to similar-sized and similar-designed objects
Solution Approach 1:
The patent designs the RL training framework to learn universal manipulation policies that generalize across diverse objects. The simulation environment includes varied object geometries, sizes, and materials, and the reward function is designed to encourage policies that work across this diversity. The state space and action space are formulated to be object-agnostic, enabling the learned policy to adapt to different objects without retraining, thus achieving both ease of operation and versatility.
Solution Approach 2:
The patent employs domain randomization by varying physical parameters (friction coefficients, mass, dimensions) and environmental parameters (lighting, camera angles) during RL training. This parameter variation forces the learned policy to be robust to changes in object properties and environmental conditions. By training across a wide parameter space, the system learns adaptable policies that maintain performance when applied to novel objects with different physical attributes.
3Adaptability or versatility
If domain randomization is applied to force a learned manipulating policy to extract useful information from a state, then adaptability is improved, but loss of information increases due to the need to generalize across diverse task settings and object properties
Solution Approach 1:
The patent extracts only the essential and invariant features from the manipulation states that are relevant for generalization. The state representation is carefully designed to include geometric and physical properties that matter for manipulation (such as object pose, gripper position, contact points) while excluding task-specific or irrelevant details. This selective extraction of useful information allows the policy to generalize across diverse objects without losing critical manipulation-relevant information.
Solution Approach 2:
The patent performs preliminary feature extraction and state transformation during the training phase, converting raw sensor data into meaningful representations that capture essential manipulation dynamics. By preprocessing the state information in advance and organizing it in a standardized format, the system prepares the data in a way that facilitates generalization while preserving necessary information. This preliminary structuring of information reduces the burden on the RL agent to handle raw, unprocessed data with varying levels of detail.
Data Source
AI summary
A controller for controlling robotic manipulator according to a task is provided. The controller is to collect data relating to a state and an object property of an object, and execute a state adapter model to produce a state correction to state of the object having the object property different from a unitary property of a unitary object. The controller is to execute a control policy using the state correction to produce an action for the unitary object, and execute an action adapter model to produce an action correction to the action produced by the control policy. The state correction and action correction are produced based on difference between object property and unitary property. The control policy is to map a state of the unitary object to the action of the robotic manipulator to manipulate the unitary object according to the task.