Robot Movement Policy Learning for New Constraint Adaptation
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Solution Overview
Problem
Existing approaches struggle to apply policies learned from limited demonstrations to new environments with varying constraints, lacking generalizability and adaptability in robotic tasks like pouring.
Innovation Solution
A method using recurrent mixture density networks (RMDNs) to generate trajectories by learning a stochastic policy from demonstration data, weighting action particles by their likelihood of achieving desired outcomes, and selecting optimal actions to adapt to new constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Learning from Demonstration (LfD) is used to learn policies from expert demonstrations, then the robot can perform tasks under demonstrated constraints, but the learned policy does not transfer when constraints change
Solution Approach 1:
The patent changes the parameter representation from deterministic trajectories to stochastic policies with confidence values. By representing policies as probability distributions rather than fixed sequences, the system can adapt to varying constraints by adjusting the confidence parameters and sampling from different regions of the policy distribution, enabling transfer to new constraint conditions.
Solution Approach 2:
The patent introduces dynamic adaptability by using recurrent neural networks with hidden states that can adjust policy generation in real-time based on current system state and constraint conditions. The stochastic nature of the policy allows dynamic selection of actions based on confidence levels, enabling the system to adapt to changing constraints during execution.
2Reliability
If Reinforcement Learning (RL) is used to learn policies through trial-and-error, then the robot can learn optimal policies, but it requires executing policies and observing outcomes during learning which is not always possible
Solution Approach 1:
The patent uses demonstration data as copies of expert behavior to train the stochastic policy model. Instead of requiring the robot to execute and learn from actual trial-and-error outcomes, the system learns from recorded demonstrations and generates stochastic policies that replicate expert behavior patterns, eliminating the need for physical trial-and-error execution during the learning phase.
3Manufacturing precision
If deterministic policies are learned from demonstrations, then the robot can execute tasks accurately under demonstrated conditions, but the policy lacks flexibility for new constraints
Solution Approach 1:
The patent transforms deterministic policies into dynamic stochastic policies where the same policy can generate different actions based on the current state and confidence levels. This dynamic approach maintains precision by learning from accurate demonstrations while gaining flexibility through probabilistic action selection and confidence-based adaptation to new constraints.
Solution Approach 2:
The patent changes the policy representation from fixed deterministic values to stochastic distributions with confidence parameters. This parameter change allows the system to maintain the precision of learned demonstrations while introducing flexibility through confidence-based sampling, enabling accurate execution under demonstrated conditions and adaptable behavior under new constraints.
Data Source
AI summary
System and methods for generating a trajectory of a dynamical system are described herein. An example method includes modelling a policy from demonstration data. The method also includes generating a first set of action particles by sampling from the policy, where each of the action particles in the first set includes a respective system action, predicting a respective outcome of the dynamical system in response to each of the action particles in the first set, and weighting each of the action particles in the first set according to a respective probability of achieving a desired outcome. The method further includes generating a second set of action particles by sampling from the weighted action particles, where each of the action particles in the second set includes a respective system action, and selecting a next system action in the trajectory of the dynamical system from the action particles in the second set.