Robot Critic Value Distributions for Conservative Action Control
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Solution Overview
Problem
Current reinforcement learning approaches for robotic tasks often rely on discrete value outputs from critic networks, which limit the robot's ability to control its behavior conservatively and generalize to new objects and environments effectively, especially when dealing with continuous action spaces.
Innovation Solution
The implementation of distributional QT-Opt, which trains the critic network to generate value distributions instead of scalar values, allowing for continuous action spaces and enabling the robot to control its behavior conservatively by considering out-of-band signals such as user preferences and environmental attributes, and uses off-policy deep reinforcement learning with self-supervised data collection to learn generalizable strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If discrete value outputs from critic networks are used, then the control system is simple, but the robot's ability to control behavior conservatively and generalize to new objects and environments is limited
Solution Approach 1:
The patent transforms the critic network's output from discrete scalar values to continuous value distributions. This parameter change enables the robot to control behavior conservatively by selecting different quantiles from the distribution (e.g., lower quantiles for conservative behavior) and improves generalization to new objects and environments through off-policy deep reinforcement learning with self-supervised data collection.
2Reliability
If scalar values are used from critic networks, then the computational process is fast, but the robot cannot control its behavior conservatively
Solution Approach 1:
The patent adds a dimensional transformation by outputting value distributions instead of scalar values. This allows the system to control conservative behavior by selecting specific quantiles (e.g., 0.25-quantile) from the distribution, providing a mechanism for risk-aware decision-making without requiring fundamentally different computational processes.
3Productivity
If discrete values are used for continuous action spaces, then the implementation is simple, but the task performance and stability are reduced
Solution Approach 1:
The patent changes the output parameter from discrete scalars to continuous value distributions, which naturally accommodate continuous action spaces. This improvement in task performance and stability is achieved through off-policy deep reinforcement learning with self-supervised data collection, which learns generalizable strategies that transfer to continuous domains.
Data Source
AI summary
Techniques are described herein for robotic control using value distributions. In various implementations, as part of performing a robotic task, state data associated with the robot in an environment may be generated based at least in part on vision data captured by a vision component of the robot. A plurality of candidate actions may be sampled, e.g., from continuous action space. A trained critic neural network model that represents a learned value function may be used to process a plurality of state-action pairs to generate a corresponding plurality of value distributions. Each state-action pair may include the state data and one of the plurality of sampled candidate actions. The state-action pair corresponding to the value distribution that satisfies one or more criteria may be selected from the plurality of state-action pairs. The robot may then be controlled to implement the sampled candidate action of the selected state-action pair.