Multi-Critic Robot Policy Learning for Safe State-Space Exploration
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Solution Overview
Problem
Robots with high degrees of freedom operating in continuous state spaces face challenges in efficiently exploring and discovering manipulation strategies while ensuring safety in shared workspaces, leading to complexities in planning trajectories and designing adaptive controllers.
Innovation Solution
A multi-critic architecture is employed to balance accuracy, exploration, and safety using multiple objective functions, including reach-reward, discovery-reward, and safety-reward, with a policy learned through weighted feedback of value functions and proximal policy optimization to minimize interference and ensure stable learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robot with high degrees of freedom operates in continuous state spaces to improve adaptability, then the robot can handle complex manipulation tasks, but the search space of possible actions and configurations becomes too large to explore efficiently
Solution Approach 1:
The state space is segmented into discrete regions using grid-based representation, where continuous states are divided into discrete cells. This segmentation transforms the continuous control problem into a discrete planning problem, making the search space manageable while preserving the robot's ability to handle complex manipulation tasks through hierarchical planning approaches.
Solution Approach 2:
The patent introduces a hierarchical dimension to the planning problem by combining discrete state-space planning with continuous control layers. The high-level planner operates in discrete state space to determine strategic transitions, while low-level continuous controllers handle execution, effectively adding a temporal hierarchy dimension that reduces the computational burden of exploring the full continuous state space.
2Reliability
If the robot explores more extensively to discover manipulation strategies, then the robot can find better solutions, but the time required for learning and planning increases
Solution Approach 1:
The system performs preliminary exploration and skill discovery during offline training phases, building pre-computed policies and value functions before actual operation. This preliminary action allows the robot to accumulate knowledge about effective manipulation strategies in advance, reducing the time required for real-time decision-making while maintaining high-quality skill discovery through extensive pre-learning.
Solution Approach 2:
The patent implements continuous learning mechanisms where the robot continuously updates its policies and value functions based on ongoing experience, even during operation. This continuity allows the system to refine its skills progressively without requiring complete re-learning, balancing exploration quality with operational efficiency through incremental improvement rather than periodic extensive retraining.
3Adaptability or versatility
If the robot learns complex policies to handle dynamic environments, then the robot can adapt to unforeseen events, but the complexity of designing adaptive controllers increases
Solution Approach 1:
The controller is segmented into modular components including discrete planners, continuous controllers, value function estimators, and policy update mechanisms. Each module handles specific aspects of the control problem independently, reducing overall system complexity while maintaining adaptability through the coordinated interaction of these specialized components rather than requiring a monolithic complex controller.
Solution Approach 2:
The patent introduces value functions and policies as intermediary representations that bridge the gap between raw sensor inputs and actuator commands. These intermediaries encapsulate learned knowledge about optimal behaviors, allowing the robot to adapt to unforeseen events through learned patterns rather than requiring complex real-time computation, thus reducing controller complexity while preserving adaptability.
4Reliability
If the robot operates safely in shared workspaces with humans, then human safety is ensured, but the degrees of freedom of the state space are constrained
Solution Approach 1:
Safety constraints are applied locally to specific regions of the state space rather than uniformly across all states. The system identifies safe and unsafe regions through learning and applies constraints only where necessary, allowing the robot to maintain full degrees of freedom in safe regions while restricting operations only in potentially hazardous areas. This local application of safety constraints preserves adaptability while ensuring human safety in shared workspaces.
Data Source
AI summary
Systems and methods are disclosed for determining a policy to recommend transition in a position-representing space for a robotic device using a multi-critic architecture. To learn policy in a multi-critic architecture, a set of critics is defined pertaining to a position-representing space where each critic corresponds to a different objective function such as reach-reward, discovery-reward, and safety-reward. For each one of the critics of the set of critics, a learned value function in position-representing space is determined. The policy is learned based on the weighted feedback of the learned value functions to recommend transitions that are safe in the position-representing space. The multi-critic architecture minimizes interference between multiple reward functions and learns a safe and stable policy for the robotic device.