Manipulation Policy Chaining Through Terminal State Matching
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Solution Overview
Problem
Existing reinforcement learning and imitation learning approaches struggle with long-horizon manipulation tasks due to the high complexity of required skills and the need for large state distributions, leading to policy failures when encountering unseen starting states.
Innovation Solution
A method of fine-tuning policies to match terminal state distributions of adjacent policies, using an adversarial skill chaining framework with terminal state regularization to ensure that the terminal states of one policy align with the initiation states of the next policy, thereby reducing the need for excessively large state distributions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If skill chaining is used to solve long-horizon manipulation tasks, then the complexity of required skills is reduced, but the starting state distribution must be widened to handle unseen states, which limits the solution to short skill sequences
Solution Approach 1:
The system performs preliminary actions by training policies to output terminal states that proactively match the initial states of subsequent policies. This is achieved through the discriminator that evaluates terminal states and provides feedback signals during training, ensuring policies learn to produce compatible states before actual chaining execution.
Solution Approach 2:
A discriminator is introduced that provides feedback signals to policies during training. The discriminator evaluates whether terminal states match the initial states of subsequent policies and generates feedback signals that guide policy optimization, creating a closed-loop learning system that enforces state distribution compatibility.
2Reliability
If the starting state distribution of policies is widened to handle unseen states, then policy robustness improves, but the computational requirements and data coverage needs increase exponentially
Solution Approach 1:
The system changes the parameter of state distribution by using the discriminator to enforce matching between terminal and initial state distributions. This transforms the approach from widening distributions to maintaining targeted distributions, reducing the quantity of training data needed while improving robustness through precise state alignment.
3Productivity
If multiple policies are sequenced to complete long-horizon tasks, then task completion capability improves, but the requirement for coverage of larger state distributions limits the solution to short skill sequences
Solution Approach 1:
Policies are trained in advance with the discriminator to ensure their terminal states match the initial states of subsequent policies. This preliminary training with state matching constraints enables longer skill sequences to be composed without the exponential growth in state distribution coverage requirements that previously limited sequence length.
Data Source
AI summary
A manipulation task may include operations performed by one or more manipulation entities on one or more objects. This manipulation task may be broken down into a plurality of sequential sub-tasks (policies). These policies may be fine-tuned so that a terminal state distribution of a given policy matches an initial state distribution of another policy that immediately follows the given policy within the plurality of policies. The fine-tuned plurality of policies may then be chained together and implemented within a manipulation environment.