Bipedal Robot Control Policy for Closing the Sim2Real Gap
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Solution Overview
Problem
The Sim2Real gap poses a significant challenge for bipedal robots, where policies trained in simulators fail to translate effectively to real-world environments due to inherent noise and drift, leading to instability and failure in real-world walking tasks.
Innovation Solution
The method involves learning a corrective policy that modifies actions in the simulator to reduce the transition error between simulated and real-world states, effectively closing the Sim2Real gap by fine-tuning the initial policy in an augmented simulation environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If domain randomization is applied to improve robustness, then reliability is improved, but responsiveness to commands deteriorates
Solution Approach 1:
The control policy is segmented into two distinct components: a robust policy trained with domain randomization for stability, and a responsive policy trained without randomization for command following. Each policy handles different aspects of control, allowing the system to maintain both robustness and responsiveness simultaneously.
Solution Approach 2:
The system changes the training parameters (presence or absence of domain randomization) to create two specialized policies. By adjusting which policy is active based on the situation, the system can optimize for either robustness or responsiveness as needed.
2Adaptability or versatility
If domain randomization is applied during training, then adaptability is improved, but manufacturing precision requirements worsen
Solution Approach 1:
The system uses feedback from real-world robot performance to identify which policy component (robust or responsive) is underperforming. Based on this feedback, the training process can be adjusted to improve the specific policy that needs enhancement, allowing continued adaptation without requiring increased manufacturing precision.
3Measurement precision
If a regressor is used to learn the Sim2Real gap, then measurement precision is improved, but device complexity worsens
Solution Approach 1:
Instead of using a complex regressor to model the entire Sim2Real gap, the system extracts and addresses only the critical aspects of the gap through separate training regimes. The robust policy handles dynamics mismatches while the responsive policy handles control accuracy, simplifying the overall system architecture.
Solution Approach 2:
Rather than trying to measure and correct the Sim2Real gap directly through a regressor, the system inverts the approach by training two policies under different assumptions and combining their strengths. This avoids the complexity of explicit gap measurement while achieving similar goals.
Data Source
AI summary
There is provided a method for learning a bipedal robot control policy, the method includes (i) learning, by a processing circuit, an action-related corrective policy that once applied reduces a gap associated with an initial simulation state transition function and with a real world state transition function; and (ii) determining a control policy of the bipedal robot in a simulator, using the action-related corrective policy.