Anticipatory Gait Switching for Quadrupeds via Look-Ahead MDP Planning
We propose a lightweight height-map-only gait supervisor that unifies look-ahead and locomotion constraints in a single decision policy.
ProofPaper ↗
Key points
- Quadruped robots often switch gaits to traverse mixed terrain safely.
- A robot-centered height map is partitioned into near-, mid-, and far-field bands, each discretized into a terrain class, and band evolution is modeled as a Markov process.
- Gait selection is cast as a look-ahead Markov Decision Process whose transition model and cost explicitly encode platform-specific switching constraints.
- Overall, the MDP formulation yields anticipative constraint-aware gait switching that improves robustness without course-specific retuning.
Sources (1)
- [1]Anticipatory Gait Switching for Quadrupeds via Look-Ahead MDP PlanningarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 17, 10:35 PM
We propose a lightweight height-map-only gait supervisor that unifies look-ahead and locomotion constraints in a single decision policy.
Quadruped robots often switch gaits to traverse mixed terrain safely.
Extractive summary: sentences quoted from the sources.
Before this
- Oct 17, 2026ProbePO: Sparse Probes, Redistributed Credit for Critic-Free LLM Reinforcement Learning
- Oct 17, 2026How to Learn ML in 2026 interview
- Oct 16, 2026Hidden Trajectories Don't Lie: Correcting Corruption Drift for Test-Time Adaptation
- Oct 16, 2026Learning Robot Metareasoning: Adaptive Computation Budgets for Real-Time Control
- Oct 15, 2026Granite Switch 4.1: Chat, Decisions, and Speech from One Endpoint
- Oct 15, 2026Local Epochs as Optimal Stopping: Theory and Evidence in Federated Learning