Whole-Body Robot Teleoperation Using Motion Synergies
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Solution Overview
Problem
Existing robotics methods face challenges in replicating human whole-body manipulation skills due to contact-rich problems leading to non-convex search spaces and the need for extensive expert demonstrations, making training inefficient.
Innovation Solution
A method involving teleoperation with defined primitives, domain randomization, and example-guided reinforcement learning to train robots for whole-body manipulation, using a controller to combine joint movements and leverage tactile and proprioceptive observations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If model-based planning methods are used for contact-rich problems, then planning capability is provided, but the search space becomes non-convex and disconnected due to stiff and discontinuous numerics with excessive discrete modes
Solution Approach 1:
The patent replaces traditional model-based planning methods with a neural network-based approach. The neural network learns to predict robot states and plan motions directly from sensor data and high-level commands, substituting the mechanical/mathematical modeling process with a data-driven neural network that handles contact-rich problems without requiring explicit physical models or convex optimization.
Solution Approach 2:
The patent introduces an intermediary neural network layer that translates high-level semantic commands into low-level motor commands. This intermediary network acts as a mediator between the user's intent and the robot's physical execution, bypassing the complex non-convex search space of traditional planning methods by learning direct mappings from training data.
2Adaptability or versatility
If imitation learning is used to teach robot manipulation skills, then expert human skills can be replicated, but a considerable amount of expert demonstrations is required
Solution Approach 1:
The patent implements self-service learning where the robot system automatically generates its own training data by executing random actions and observing the consequences. The neural network learns from these self-generated experiences rather than requiring external expert demonstrations, enabling the robot to autonomously improve its manipulation skills through reinforcement learning.
Solution Approach 2:
The patent changes the learning approach by transitioning from static imitation learning to dynamic reinforcement learning. Instead of learning fixed demonstrations, the neural network learns policies that adapt to varying conditions through trial-and-error learning, modifying its behavior based on rewards and penalties received during execution.
3Adaptability or versatility
If the robot is controlled with full body engagement for gross manipulation, then complex tasks like lifting and reorienting heavy objects become possible, but the control complexity increases due to multiple joints moving together
Solution Approach 1:
The patent segments the complex whole-body control problem into independent neural network modules, each responsible for specific aspects of manipulation. The network processes different body parts and joint configurations separately through multiple neural pathways, then integrates their outputs to produce coordinated whole-body motions, reducing the complexity of controlling all joints simultaneously.
Data Source
AI summary
A method may comprise receiving one or more primitives associated with a robot, each of the one or more primitives comprising a plurality of joints of the robot that move together; while a user teleoperates the robot to perform a task of manipulating an object, receiving robot configuration data associated with the robot, object configuration data associated with the object, and position commands input by the user based on the one or more primitives as training data; perturbing the training data; performing domain randomization on the training data; and learning a policy for controlling the robot to autonomously perform the task based on the training data.