Reinforcement Learning for Robust Legged Locomotion
Training and robustness evaluation for locomotion policies under terrain, sensing, and dynamics shift, using identified failure modes to define targeted evaluation and curriculum scenarios.
- Status
- Deployed research
Problem
Locomotion policies deployed in industrial inspection face terrain, sensing, contact, and dynamics conditions that differ from training. Average simulated return can conceal rare failures that dominate real-world reliability.
My work at ANYbotics covers reinforcement-learning-based locomotion controllers in Isaac Sim and Isaac Lab, GPU-accelerated experimentation, robustness evaluation, and sim-to-real iteration for quadruped robots deployed in industrial inspection.
Claim
Robustness evaluation can identify failure modes that inform targeted regression tests and curriculum scenarios.
Method
The workflow connects:
- objective and curriculum design for useful motion without unsafe shortcuts;
- parallel simulation for large policy-training and evaluation batches;
- controlled policy comparisons with reproducible experiment definitions;
- robustness evaluation and failure analysis across terrain, commands, disturbances, contacts, sensing, and model parameters;
- sim-to-real iteration that reproduces hardware observations in simulation where possible.
Fixed capability slices and safety-event definitions keep policy changes attributable. Replaying observed failures then turns them into regression cases or curriculum scenarios rather than allowing them to disappear inside aggregate return.
Evidence
This is deployed professional research on a physical robot platform, not a public benchmark result. The experimentation stack supports controller selection and deployment through large scenario suites, policy-to-policy comparisons, and structured analysis of hardware failures.
I also led and secured a public EuroHPC grant allocation of up to 50,000 H100 GPU-hours for RL robustness and scaling experiments.
Limitations
Product-specific controller details, environment counts, thresholds, and deployment results are confidential. The public links document the robot platform and ANYbotics’ broader learning-based locomotion approach; they should not be read as attributing every demonstrated capability to my individual work.
Public context
See public ANYmal footage and ANYbotics’ technical overview of learning-based mobility.